Modeling risk situations. Risk theory and modeling of risk situations. Risk concept. Risk classification criteria. Investment in a portfolio of securities
When managing risks, it is often necessary to compare real situations with hypothetical ones (what would happen if things went differently). This dramatically complicates the analysis of risk situations, as it requires a basis for learning and measuring what was not there. At present, there is no other way to describe such hypothetical situations, except for the use of mathematical models called models of risk situations. This provides the basis for quantitative risk management. Its essence lies in the application of economic and mathematical models to predict situations characterized by risk and uncertainty, and justify the corresponding management decisions.
A model is a simplified description of a real object or process, which focuses on properties that are important for the researcher and ignores those aspects that are insignificant to the researcher. The main challenge in modeling is figuring out which properties are important and which are not. A correct description of important properties ensures the adequacy of the model, and the correct choice of minor, ignored properties helps to sufficiently simplify such a representation. The model should serve as a decision-making tool, that is, it should clarify for the decision-maker how the process can develop, what outcomes will take place, and suggest various actions (for example, to prevent damage).
The most important class of models used in risk management are mathematical models. They allow you to describe the essential aspects of the studied process or phenomenon in the form of mathematical relationships, and then analyze them using the appropriate mathematical apparatus. It is especially important to use mathematical models to predict alternatives for future development. This is what allows the manager to numerically assess the future consequences of the decisions made.
Mathematical models used in risk management are very diverse and have different capabilities. There is no such thing as a universal model. The multiplicity of types of risks and the variety of mechanisms of their occurrence makes this impossible. In different situations, we will use specific tools (in this case, models), because each model is unique in its own way, since when building it, one should start from the properties of the modeling object itself. However, similar situations allow us to use similar (if not the same) tools: there are some general approaches to modeling (for example, using stochastic differential equations or other mathematical apparatus). If a more or less standard approach can be applied, then the modeling process will be simpler (approaches to building a model and obtaining a solution are known).
In the field of quantitative risk management, the most common probabilistic and statistical models.
For some types of risks, the widespread use of mathematical models is standard, for others it is not yet. Nevertheless, there is an intensive development of various modeling techniques that use the features of risk management. Quantitative risk management is becoming a separate “branch” of risk management.
Name: Risk theory and modeling of risk situations.
The textbook describes the essence of uncertainty and risk, classification and factors acting on them; methods of qualitative and quantitative assessment of economic and financial situations in conditions of uncertainty and risk are presented.
A classification of service technologies is given, examples of the activities of service organizations in risky situations are considered.
The methodology for managing investment projects in conditions of risk is presented, recommendations are given for managing the investment portfolio, the financial condition and development prospects of the investment object are assessed, and a model for accounting for risks in investment projects is proposed.
Considerable attention is paid to methods and models of management in conditions of risk and psychology of behavior and assessment of a decision-maker.
For students and postgraduates of economic universities and faculties, students of business schools, risk managers, managers of innovations, investments, as well as specialists of banking and financial structures, employees of pension, insurance and investment funds.
Content
Foreword
Chapter 1 PLACE AND ROLE OF ECONOMIC RISKS IN MANAGING THE ACTIVITIES OF ORGANIZATIONS
1.1. Organizations, types of enterprises, their characteristics and goals
1.2. Place and role of risks in economic activity
1.2.1. Definition and essence of risks
1.2.2. Management of risks
1.2.3. Risk classification
1.2.4. Uncertainty system
1.3. Risk management system
1.3.1. Management activities
1.3.2. Risk management
1.3.3. Risk management process
1.3.4. Mathematical methods for assessing economic risks
Chapter 2. RISKS OF SERVICE ENTERPRISES
2.1. Service technology
2.2. Classification of risks of enterprises in the service sector
2.3. Dynamic analysis of the situation in the service market
2.4. Risk management model for service sector organizations
Chapter 3. INFLUENCE OF KEY MARKET BALANCE FACTORS ON RISK MANAGEMENT
3.1. Risk limiting factors
3.2. Influence of market equilibrium factors on risk change
3.2.1. Relationship between market equilibrium and commercial risk
3.2.2. The influence of market equilibrium factors on the change in commercial risk
3.2.3. Modeling the process of achieving equilibrium
3.2.4. Impact of changes in demand on the level of commercial risk
3.2.5. Impact of changes in supply on the degree of commercial risk
3.2.6. Building supply-demand dependencies
3.3. Influence of the time factor on the degree of risk
3.4. Influence of supply and demand elasticity factors on the level of risk
3.5. Impact of the taxation factor in market equilibrium on the level of risk
Chapter 4. FINANCIAL RISK MANAGEMENT
4.1. Financial risks
4.1.1. Classification of financial risks
4.1.2. Relationship of financial and operating leverage to aggregate risk
4.1.3. Development risks
4.2. Interest rate risks
4.2.1. Types of interest rate risks
4.2.2. Interest transactions
4.2.3. Average percentages
4.2.4. Variable interest rate
4.2.5. Interest rate risks
4.2.6. Interest rate risk of bonds
4.3. Risk of losses from changes in the flow of payments
4.3.1. Equivalent streams
4.3.2. Payment streams
4.4. Risky investment processes
4.4.1. Investment risks
4.4.2. Rates of return on risky assets
4.4.3. Net present value
4.4.4. Annuity and redemption fund
4.4.5. Investment appraisal
4.4.6. Risky investment payments
4.4.7. Discounting in time
4.5. Credit risks
4.5.1. Factors contributing to the emergence of credit risks
4.5.2. Credit risk analysis
4.5.3. Credit risk mitigation techniques
4.5.4. Loan payments
4.5.5. Accrual and payment of interest on a consumer loan
4.5.6. Credit guarantees
4.6. Liquidity risk
4.7. Inflation risk
4.7.1. Relationship between interest rates and inflation
4.7.2. Inflation premium
4.7.3. Influence of inflation on various processes
4.7.4. Measures to reduce inflation
4.8. Currency risks
4.8.1. Currency conversion and interest accrual
4.8.2. Exchange rates in time
4.8.3. Reducing foreign exchange risks
4.9. Asset risks
4.9.1. Exchange risks
4.9.2. Impact of default risk and taxation of asset value
4.10. Probabilistic assessment of the degree of financial risk
Chapter 5. QUANTITATIVE ESTIMATES OF ECONOMIC RISK UNDER UNCERTAINTY
5.1. Methods for making effective decisions in conditions of uncertainty
5.2. Matrix games
5.2.1. The concept of playing with nature
5.2.2. The subject of game theory. Basic concepts
5.3. Performance criteria under conditions of complete uncertainty
5.3.1. Guaranteed result criterion
5.3.2. Optimism criterion
5.3.3. Pessimism criterion
5.3.4. Savage's Minimax Risk Criterion
5.3.5. The criterion of generalized maximin (pessimism - optimism) by Hurwitz
5.4. Comparative assessment of solution options depending on performance criteria
5.5. Multicriteria problems of choosing effective solutions
5.5.1. Multicriteria tasks
5.5.2. Pareto optimality
5.5.3. Choice of solutions in the presence of multi-criteria alternatives
5.6. Decision Making Model Under Partial Uncertainty
5.7. Determination of the optimal volume of garment production under conditions of uncertainty
5.7.1. Upper and lower game prices
5.7.2. Reduction of a Matrix Game to a Linear Programming Problem
5.7.3. Selection of the optimal range of products
5.8. Risks associated with the operation of a sewing enterprise
Chapter 6. MAKING AN OPTIMAL DECISION IN THE CONDITIONS OF ECONOMIC RISK
6.1. Probabilistic Statement of Preferred Decisions
6.2. Assessment of the degree of risk in conditions of certainty
6.3. Selection of the optimal number of jobs in a hairdressing salon, taking into account the risk of service
6.4. Statistical decision-making methods under risk conditions
6.5. Choosing the optimal plan by constructing event trees
6.5.1. Decision tree
6.5.2. Optimizing go-to-market strategy
6.5.3. Maximizing stock returns
6.5.4. Selection of the optimal project for the reconstruction of a dry cleaning factory
6.6. Comparative assessment of solution options
6.6.1. Choosing the optimal solution using statistical evaluations
6.6.2. Normal distribution
6.6.3. Risk curve
6.6.4. Choosing the Optimal Solution Using Confidence Intervals
6.6.5. Production cost forecasting model
6.7. The emergence of risks when setting the mission of the goals of the company
6.8. Activity of service enterprises in conditions of risk
6.8.1. Finishing and design company Company for baking bakery products and their subsequent sale
6.8.3. Beauty saloon
Chapter 7. RISK INVESTMENT MANAGEMENT
7.1. Investment projects under conditions of uncertainty and risk
7.1.1. Basic concepts of investment projects
7.1.2. Analysis and evaluation of investment projects
7.1.3. Investment project risks
7.2. The optimal choice of investment volume, ensuring the maximum increase in production output
7.3. Investment in a portfolio of securities
7.3.1. Investment management process
7.3.2. Diversified portfolio
7.3.3. Risks associated with investing in a securities portfolio
7.3.4. Practical recommendations for the formation of an investment portfolio
7.4. Analysis of the economic efficiency of an investment project
7.4.1. Analysis of associated risk factors
7.4.2. Preliminary assessment and selection of enterprises
7.4.3. Assessment of the financial condition of an enterprise as an investment object
7.4.4. Examples of analysis using financial ratios
7.4.5. Assessment of the development prospects of the organization
7.4.6. Comparative financial analysis of investment projects
7.4.7. On-site analysis of organization survey methods
7.5. Accounting for risk in investment projects
7.5.1. Project risk assessment model
7.5.2. Consideration of risk when investing
7.5.3. Practical conclusions for managing risky investment projects
Chapter 8. RISK MANAGEMENT TOURISM
8.1. Factors influencing the dynamics of tourism development
8.1.1. Tourism development in Russia
8.1.2. Types and forms of tourism
8.1.3. Features of tourism as factors of development uncertainty
8.2. Psychology of the impact of tourism on participants and others
8.2.1. Travel motivation
8.2.2. Impact of tourism
8.3. Risks associated with tourism
8.3.1. Factors affecting tourism and the tourism economy
8.3.2. Tourism risk classification
8.4. Economic impact of tourism
8.5. Making a management decision
8.6. Analysis of the activities of the organization for the provision of tourism services in conditions of risk
Chapter 9. RISK MANAGEMENT OF HOTELS AND RESTAURANTS
9.1. Development of hotel enterprises
9.2. Development factors of the restaurant business
9.3. Features and specifics of hospitality
9.4. Hospitality Industry Risks and Management
9.4.1. Identifying risks
9.4.2. Investment project risks
9.4.3. Reducing the risks of the hospitality industry
9.5. Management solutions in the hospitality business
Chapter 10. KEY METHODS AND WAYS TO REDUCE ECONOMIC RISKS
10.1. General principles of risk management
10.1.1. Risk management process diagram
10.1.2. Examples of risks
10.1.3. Choice of risk management techniques
10.2. Diversification
10.3. Risk insurance
10.3.1. The essence of insurance
10.3.2. Main characteristics of insurance contracts
10.3.3. Calculation of insurance transactions
10.3.4. Insurance contract
10.3.5. Advantages and disadvantages of insurance
10.4. Hedging
10.4.1. Risk management strategies
10.4.2. Basic concepts
10.4.3. Forward and futures contracts
10.4.4. Exchange rate hedging
10.4.5. Key aspects of risk
10.4.6. Hedging an exchange rate using a swap
10.4.7. Options
10.4.8. Insurance or hedging
10.4.9. Synchronization of cash flows
10.4.10. Hedging model
10.4.11. Measuring hedge effectiveness
10.4.12. Minimizing hedging costs
10.4.13. Correlated hedging transaction
10.5. Limiting
10.6. Funds reservation (self-insurance)
10.7. Good risk management
10.8. Purchasing additional information
10.9. Evaluating the effectiveness of risk management methods
10.9.1. Risk financing
10.9.2. Assessing the effectiveness of risk management
Chapter 11. PSYCHOLOGY OF BEHAVIOR AND EVALUATION OF THE PERSON MAKING A DECISION
11.1. Personal factors affecting the degree of risk when making management decisions
11.1.1. Psychological problems of the behavior of an economic person
11.1.2. Administrative actions of an entrepreneur in the service sector
11.1.3. Personality's attitude to risk
11.1.4. Intuition and risk
11.2. Expected utility theory
11.2.1. Utility function graphs
11.2.2. Expected utility theory
11.2.3. Taking into account the attitude of the decision maker to risk
11.2.4. Group decision making
11.3. Rational Behavior Theory
11.3.1. Perspective theory
11.3.2. Rational approach to decision making
11.3.3. Decision asymmetry
11.3.4. Behavior invariance
11.3.5. The role of information in decision making
11.4. Conflict situations
11.5. The Role of the Manager in Making Risk Decisions
11.5.1. Decision making in the face of risk
11.5.2. Requirements for the decision-maker
11.5.3. Principles for evaluating the effectiveness of decisions made by decision makers
Review questions
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Description : The textbook "Risk Theory and Modeling of Risk Situations" is written in accordance with the requirements of the State Educational Standards of the 2nd generation of the Ministry of Education of the Russian Federation. It corresponds to the programs of disciplines "Risk theory and modeling of risk situations" and "Mathematical methods of financial analysis" special. 061800 "Mathematical methods in economics", discipline program "Decision theory and risk management in the financial and tax area" special. 351200 "Taxes and Taxation", the discipline program "Management" special. 061100 "Management", as well as a number of economic specialties containing the discipline "Management", since "Risk management" is part of this discipline.
The textbook "Risk Theory and Modeling of Risk Situations" contains eleven chapters.
In the first chapter, "The place and role of economic risks in the management of organizations' activities," the definition of an organization is given, the types of organizations, their characteristics and goals are considered. The place and role of risks in economic activity is determined, definitions and essence of risks are given. The classification of uncertainties and risks is given, the risk management system is revealed, and the basic concepts of risk management are given. The main mathematical methods for assessing economic risks are considered and their characteristics are given.
The second chapter "Risks of service enterprises" is devoted to service technologies and their differences from industrial technologies. The classification of the risks of enterprises in the service sector is given and a dynamic analysis of the situation in the service market is given. A model of risk management of service sector organizations is proposed.
The third chapter "Influence of the main factors of market equilibrium on risk management" is devoted to the study of the influence on the change in the degree of economic risk of such factors characterizing the uncertainty of the market economy as: limiting risks, uncertainty of supply and demand, accounting for time, elasticity, taxation, etc.
In the fourth chapter, "Financial risk management", the theoretical foundations of financial risk management are created based on the methods of financial and actuarial mathematics. The classification of financial risks is presented, the main parameters inherent in the considered financial risks are highlighted, and analytical dependences for their assessment are given using the proposed mathematical methods. This allows for a comparative quantitative analysis of risks and, on its basis, select those risk management methods that are most effective.
The fifth chapter, "Quantitative Estimates of Economic Risk under Uncertainty", discusses methods for making effective decisions under conditions of uncertainty, using various performance criteria. The multicriteria problems of choosing effective solutions are studied. A sewing enterprise is considered, for which the optimal volume of production is selected in conditions of uncertainty and the functioning of the enterprise in a risky situation is investigated.
The sixth chapter "Making the optimal decision under risk" is devoted to the presentation of probabilistic and statistical methods for making effective decisions and the choice of the optimal decision using confidence intervals. The problem of choosing the optimal number of jobs in a hairdressing salon is considered, taking into account the risk of service. Using the "decision tree" method, the problems of optimizing the strategy for entering the market, maximizing the profit from shares and choosing the optimal project for the reconstruction of a dry cleaning factory are considered. The material on the emergence of risks in setting the mission and goals of the company is touched upon. The activities of a company for the decoration and design of premises, an enterprise for baking bakery products and their subsequent sale, and a beauty salon at risk are investigated.
In the seventh chapter "Management of investment projects in conditions of risk" the basic concepts of investment projects, their analysis and evaluation are given, investment risks are given. The article examines investments in a securities portfolio, the purpose of which is to form an effective portfolio made up of a combination of risk-free and risky assets. Methods for analyzing the economic efficiency of an investment project and comparative financial analysis of investment projects are given. The method of accounting for project risks is considered and practical recommendations for their management are provided.
The eighth chapter "Risk management of tourism" is devoted to the types and forms, dynamics of tourism development in Russia. The factors of uncertainty in the development of tourism and the risks associated with tourism are considered, as well as their classification. The economic impact of tourism and the specificity of making management decisions are investigated. The analysis of the activities of the organization for the provision of tourist services in conditions of risk is given.
The ninth chapter "Risk management of hotels and restaurants" examines the factors of development, features and specifics of hospitality, risks inherent in the hospitality industry, and their management. Recommendations are given on reducing and managing risks in the hospitality business.
In the tenth chapter "Basic methods and ways of reducing economic" risks, on the basis of mathematical modeling, economic tools for reducing risks are investigated: diversification, insurance, hedging using forward and futures contracts, swaps and options, etc. to reduce their level and increase profitability. An assessment of the effectiveness of risk management methods is given.
Chapter eleven "Psychology of behavior and assessment of a decision maker" is devoted to the study and systematization of the influence of psychological factors on the problems of behavior of market participants and the formation of packages of recommendations for risk management and the choice of effective solutions. Conflict situations and the role of a leader in making risky decisions are considered.
At the end of the textbook "Risk Theory and Modeling of Risk Situations" for each chapter there are questions for repetition and self-control.
Content of the tutorial
PLACE AND ROLE OF ECONOMIC RISKS IN MANAGING THE ACTIVITIES OF ORGANIZATIONS
1.1. Organizations, types of enterprises, their characteristics and goals
1.2. Place and role of risks in economic activity
- 1.2.1. Definition and essence of risks
- 1.2.2. Management of risks
- 1.2.3. Risk classification
- 1.2.4. Uncertainty system
- 1.3.1. Management activities
- 1.3.2. Risk management
- 1.3.3. Risk management process
- 1.3.4. Mathematical methods for assessing economic risks
2.1. Service technology
2.2. Classification of risks of enterprises in the service sector
2.3. Dynamic analysis of the situation in the service market
2.4. Risk management model for service sector organizations
INFLUENCE OF KEY MARKET BALANCE FACTORS ON RISK MANAGEMENT
3.1. Risk limiting factors
3.2. Influence of market equilibrium factors on risk change
- 3.2.1. Relationship between market equilibrium and commercial risk
- 3.2.2. The influence of market equilibrium factors on the change in commercial risk
- 3.2.3. Modeling the process of achieving equilibrium
- 3.2.4. Impact of changes in demand on the level of commercial risk
- 3.2.5. Impact of changes in supply on the degree of commercial risk
- 3.2.6. Building supply-demand dependencies
3.4. Influence of supply and demand elasticity factors on the level of risk
3.5. Impact of the taxation factor in market equilibrium on the level of risk
FINANCIAL RISK MANAGEMENT
4.1. Financial risks
- 4.1.1. Classification of financial risks
- 4.1.2. Relationship of financial and operating leverage to aggregate risk
- 4.1.3. Development risks
- 4.2.1. Types of interest rate risks
- 4.2.2. Interest transactions
- 4.2.3. Average percentages
- 4.2.4. Variable interest rate
- 4.2.5. Interest rate risks
- 4.2.6. Interest rate risk of bonds
- 4.3.1. Equivalent streams
- 4.3.2. Payment streams
- 4.4.1. Investment risks
- 4.4.2. Rates of return on risky assets
- 4.4.3. Net present value
- 4.4.4. Annuity and redemption fund
- 4.4.5. Investment appraisal
- 4.4.6. Risky investment payments
- 4.4.7. Discounting in time
- 4.5.1. Factors contributing to the emergence of credit risks
- 4.5.2. Credit risk analysis
- 4.5.3. Credit risk mitigation techniques
- 4.5.4. Loan payments
- 4.5.5. Accrual and payment of interest on a consumer loan
- 4.5.6. Credit guarantees
4.7. Inflation risk
- 4.7.1. Relationship between interest rates and inflation
- 4.7.2. Inflation premium
- 4.7.3. Influence of inflation on various processes to reduce inflation
- 4.8.1. Currency conversion and interest accrual
- 4.8.2. Exchange rates in time
- 4.8.3. Reducing foreign exchange risks
- 4.9.1. Exchange risks
- 4.9.2. Impact of default risk and taxation
- 4.9.3. Maximizing asset value
QUANTITATIVE ESTIMATES OF ECONOMIC RISK UNDER UNCERTAINTY
5.1. Methods for making effective decisions in conditions of uncertainty
5.2. Matrix games
- 5.2.1. The concept of playing with nature
- 5.2.2. The subject of game theory. Basic concepts
- 5.3.1. Guaranteed result criterion
- 5.3.2. Optimism criterion
- 5.3.3. Pessimism criterion
- 5.3.4. Savage's Minimax Risk Criterion
- 5.3.5. The criterion of generalized maximin (pessimism - optimism) by Hurwitz
5.5. Multicriteria problems of choosing effective solutions
- 5.5.1. Multicriteria tasks
- 5 5 2. Pareto optimality
- 5.5.3. Choice of solutions in the presence of multi-criteria alternatives
5.7. Determination of the optimal volume of garment production under conditions of uncertainty
- 5.7.1. Upper and lower game prices
- 5.7.2. Reduction of a Matrix Game to a Linear Programming Problem
- 5.7.3. Selection of the optimal range of products
MAKING AN OPTIMAL DECISION IN THE CONDITIONS OF ECONOMIC RISK
6.1. Probabilistic Statement of Preferred Decisions
6.2. Assessment of the degree of risk in conditions of certainty
6.3. Selection of the optimal number of jobs in a hairdressing salon, taking into account the risk of service
6.4. Statistical decision-making methods under risk conditions
6.5. Choosing the optimal plan by constructing event trees
- 6.5.1. Decision tree
- 6.5.2. Optimizing go-to-market strategy
- 6.5.3. Maximizing stock returns
- 6.5.4. Selection of the optimal project for the reconstruction of a dry cleaning factory
- 6.6.1. Choosing the optimal solution using statistical evaluations
- 6.6.2. Normal distribution
- 6.6.3. Risk curve
- 6.6.4. Choosing the Optimal Solution Using Confidence Intervals
- 6.6.5. Production cost forecasting model
6.8. Activity of service enterprises in conditions of risk
- 6.8.1. Interior decoration and design company
- 6.8.2. Enterprise for baking bakery products and their subsequent sale
- 6.8.3. Beauty saloon
7.1. Investment projects under conditions of uncertainty and risk
- 7.1.1. Basic concepts of investment projects
- 7.1.2. Analysis and evaluation of investment projects
- 7.1.3. Investment project risks
7.3. Investment in a portfolio of securities
- 7.3.1. Investment management process
- 7.3.2. Diversified portfolio
- 7.3.3. Risks associated with investing in a securities portfolio
- 7.3.4. Practical recommendations for the formation of an investment portfolio
- 7.4.1. Analysis of associated risk factors
- 7.4.2. Preliminary assessment and selection of enterprises
- 7.4.3. Assessment of the financial condition of an enterprise as an investment object
- 7.4.4. Examples of analysis using financial ratios
- 7.4.5. Assessment of the development prospects of the organization
- 7.4.6. Comparative financial analysis of investment projects
- 7.4.7. On-site analysis of organization survey methods
- 7.5.1. Project risk assessment model
- 7.5.2. Consideration of risk when investing
- 7.5.3. Practical conclusions for managing risky investment projects
8.1. Factors influencing the dynamics of tourism development
- 8.1.1. Tourism development in Russia
- 8.1.2. Types and forms of tourism
- 8.1.3. Features of tourism as factors of development uncertainty
- 8.2.1. Travel motivation
- 8.2.2. Impact of tourism
- 8.3.1. Factors affecting tourism and the tourism economy
- 8.3.2. Tourism risk classification
8.5. Making a management decision
8.6. Analysis of the activities of the organization for the provision of tourism services in conditions of risk
RISK MANAGEMENT OF HOTELS AND RESTAURANTS
9.1. Development of hotel enterprises
9.2. Development factors of the restaurant business
9.3. Features and specifics of hospitality
9.4. Hospitality Industry Risks and Management
- 9.4.1. Identifying risks
- 9.4.2. Investment project risks
- 9.4.3. Reducing the risks of the hospitality industry
KEY METHODS AND WAYS TO REDUCE ECONOMIC RISKS
10.1. General principles of risk management
- 10.1.1. Risk management process diagram
- 10.1.2. Examples of risks
- 10.1.3. Choice of risk management techniques
10.3. Risk insurance
- 10.3.1. The essence of insurance
- 10.3.2. Main characteristics of insurance contracts
- 10.3.3. Calculation of insurance transactions
- 10.3.4. Insurance contract
- 10.3.5. Advantages and disadvantages of insurance
- 10.4.1. Risk management strategies
- 10.4.2. Basic concepts
- 10.4.3. Forward and futures contracts
- 10.4.4. Exchange rate hedging
- 10.4.5. Key aspects of risk
- 10.4.6. Hedging an exchange rate using a swap
- 10.4.7. Options
- 10.4.8. Insurance or hedging
- 10.4.9. Synchronization of cash flows
- 10.4.10. Hedging model
- 10.4.11. Measuring hedge effectiveness
- 10.4.12. Minimizing hedging costs
- 10.4.13. Correlated hedging transaction
10.6. Funds reservation (self-insurance)
10.7. Good risk management
10.8. Purchasing additional information
10.9. Evaluating the effectiveness of risk management methods
- 10.9.1. Risk financing
- 10.9.2. Assessing the effectiveness of risk management
11.1. Personal factors affecting the degree of risk when making management decisions
- 11.1.1. Psychological problems of the behavior of an economic person
- 11.1.2. Administrative actions of an entrepreneur in the service sector
- 11.1.3. Personality's attitude to risk
- 11.1.4. Intuition and risk
- 11.2.1. Utility function graphs
- 11.2.2. Expected utility theory
- 11.2.3. Taking into account the attitude of the decision maker to risk
- 11.2.4. Group decision making
- 11.3.1. Perspective theory
- 11.3.2. Rational approach to decision making
- 11.3.3. Decision asymmetry
- 11.3.4. Behavior invariance
- 11.3.5. The role of information in decision making
- 11.5.1. Decision making in the face of risk
- 11.5.2. Requirements for the decision-maker
- 11.5.3. Principles for evaluating the effectiveness of decisions made by decision makers
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Introduction
1.1 Introductory notes
1.4.2 Risk management system
Chapter 2. Modeling the process of managing the operational risk of credit institutions
2.1 Mathematical formulation of the problem
2.2 Modeling loss amounts
2.3 Modeling of dependent structures of random variables. Copular functions
2.4 Modeling loss frequencies
2.5 Stochastic Monte Carlo model of random approximation
2.6 Calculation of the amount of risk capital 66
Chapter 3. Implementation of the operational risk management system
3.1 Development and implementation of an operational risk management system
3.2 Calculation of the amount of risk capital
3.3 Assessing the economic efficiency and sustainability of the model
Conclusion
List of used literature
Applications
Introduction
mathematical operational risk economic
Economic and mathematical modeling is now at a stage when a qualitative leap is ripe. A huge number of different models have accumulated around the world. Whatever area of economics we take, there is always a whole range of mathematical, computer, verbal - meaningful models, one way or another, related to it. Hundreds of scientific journals publish monthly descriptions of new models, or modifications and development of old ones.
All of them, although they are called models of the economy, are in fact models of one of its areas, explain one thing. Each of them contributes to the system of knowledge about the economy. The peculiarity of the process of understanding, human cognition of complex phenomena is in their simplification, reduction to a simple image. Therefore, since knowledge is infinite, the creation of models also, apparently, has no limit.
Within the framework of mathematical economics with the help of formal means, the study of complex economic mechanisms is already encountering significant difficulties. The models cease to be as beautiful and complete as in the classical cases, although they consider the most common or most economically justified combinations of simple mechanisms.
From a practical point of view, any, even a very large amount of information in itself has no value. Data in its pure form is not the kind of knowledge that is called "power." Information becomes power when it allows you to foresee the future, i.e. answer the main question when choosing a solution: "What will happen if?" To answer this question, in addition to data, you must have a model of the real world.
Where do the models come from and why are they practically absent in banking management systems? In the banking business, the process of creating adequate models is complicated by two objectively existing factors. The first is that from the point of view of management, a bank is an extremely complex object consisting of many different subsystems, between which there are a large number of heterogeneous connections. The bank's activities consist of a number of business processes that significantly depend on many external factors: legislative, economic, social, political.
In cybernetics, such objects as a bank are called complex systems, and the methods of their study - methods of system analysis. The most significant results in this area are associated with operations research, an approach based on the use of quantitative mathematical methods to evaluate decisions made. However, the use of quantitative methods is possible only when the researcher has adequate mathematical models, which are just absent in banking.
The second factor is manifested in the fact that in banking (especially in the context of the transition to the market), it is impossible to conduct targeted experiments that precede the formation of a hypothesis and allow you to test it in practice. The accumulation of personal experience among analysts is hindered by the dynamic change in the situation typical of modern Russia.
Most of all, financial science is associated with the analysis of the profitability of investment activities. In addition to measuring profitability, banking analysts also deal with the uncertainty of earning income; associated with this uncertainty is risk analysis. The lack of development of these issues in our practice explains the need to study foreign experience in terms of its application in Russia.
The set of indicators, methods and calculation models used in assessing the profitability of a particular banking strategy is the subject of new, dynamically developing scientific areas - financial mathematics and financial analysis, formed at the junction of modern finance theory and a number of mathematical disciplines, such as econometrics, probability theory , mathematical statistics, operations research, theory of random processes.
The main goal of banking is to maximize profits; minimizing banking risks is also an almost equivalent task. A decline in the rate of return from banking operations, a shrinking customer base and a decrease in turnover on customer accounts lead to the fact that the ratio between the bank's profit and its operating costs becomes extremely unfavorable. Thus, a situation is created when banks are forced to look for ways to reduce costs and minimize risks. And this, in turn, forces banks to pay special attention to financial analysis and methods of managing their resources.
The ability to take reasonable risks is one of the elements of the culture of entrepreneurship in general, and banking in particular. In market conditions, each of its participants accepts certain rules of the business game and, to a certain extent, depends on the behavior of partners. One of these rules can be considered the willingness to take on risk and take into account the possibility of its implementation in their activities.
One of the main types of risks of credit institutions is operational risk caused by the uncertainty of the state and functioning of their internal and external environment. Losses from the occurrence of operational risk events can lead to significant direct and indirect losses, company ruin and even loss of life. The high-profile bankruptcies of recent years, which, among other things, were caused by errors in the organization of the operational risk management system, indicate the scale and insufficient elaboration of issues of assessment, prevention and minimization of losses from the occurrence of events related to operational risk. The lack of representative statistical information, a heterogeneous and individual operational risk profile for each credit institution makes it impossible to use generally accepted methods and models for measuring and managing financial risks used in the theory of risk management to analyze and manage operational risk.
The need to reserve capital for operational risk (including operational risk in the calculation of the capital adequacy ratio H1) became a reality for Russian commercial banks already in August 2010, as this reflects the banking sector development strategy and the Central Bank's policy of introducing risk-based approaches in the assessment of credit organizations.
Thus, the tasks of building an effective system for measuring, forecasting and minimizing operational risk arising in the course of the activities of credit institutions determine the relevance of the study.
The aim of the study is to develop methods and models for the integrated management of the operational risk of credit institutions. In accordance with this goal, the following tasks have been set and solved in the work:
1. Conduct a study of existing models and methods of analysis and financial risk management in relation to the specifics of operational risk.
2. Develop a comprehensive classification of events and operational risk factors, taking into account the specifics of the activities of credit institutions.
3. Develop the mathematical tools necessary for the analysis, measurement and management of operational risk, including:
· To set and implement the task of mathematical modeling of random processes of occurrence of losses, taking into account the presence of the effect of correlations between them;
· Develop and programmatically implement a stochastic algorithm for modeling the aggregate amount of losses with a given structure of dependencies and calculating the amount of risk capital to cover them (taking into account the availability of various insurance coverage and risk measures).
4. Develop a software implementation for modeling the process of managing the operational risk of a credit institution, assess the sensitivity of the implemented methods to various perturbations of input parameters.
5. Determine the economic efficiency of the implemented operational risk management model. To develop guidelines for organizing the operational risk management process in credit institutions.
The subject of the thesis research is operational risks arising in the course of the current activities of credit institutions. The subject of the diploma research is economic and mathematical methods and models of the operational risk management process as an element of the risk management system of a credit institution.
The theoretical and methodological basis of the study was the work of domestic scientists in the field of insurance, financial and actuarial mathematics, game theory, probability theory and mathematical statistics, the theory of extreme values, random processes, numerical methods, risk management.
The scientific novelty of the research consists in the development of an integrated approach to operational risk management based on the synthesis of the following problems of economic and mathematical modeling: analysis of the processes of occurrence of losses, assessment of the total amount of losses, calculation of the amount of risk capital to cover them. The subject of protection is the following provisions and results containing elements of scientific novelty:
1. The problem of mathematical modeling of random processes of occurrence of losses of credit institutions associated with operational risk, which allows for a more accurate assessment of the magnitude of operational risk, in comparison with the existing calculation methods, has been posed and solved.
2. Implemented probabilistic modeling of the aggregate amount of losses, taking into account the presence of correlations between them, which makes it possible to more accurately estimate the total amount of losses, reasonably reduce the estimated value of the required risk capital to cover them.
3. A software implementation has been developed for stochastic modeling of the sums of random processes (losses) with a predetermined structure of dependencies and the calculation of the amount of capital to cover them, taking into account the availability of various insurance programs and risk measures. The sensitivity of the developed methods to various perturbations of the input parameters is estimated.
4. The economic efficiency of application of the developed integrated model of operational risk management in credit institutions has been proved in comparison with existing methods and models of analysis and management of operational risk (in terms of saving the amount of risk capital).
The first chapter examines the features of simulation of banking processes, the model of the functioning of the bank, the concept of risk in banking, the classification of banking risks and the risk management system.
In the second chapter, the problem of mathematical modeling of the processes of occurrence of losses of credit institutions associated with operational risk is posed and solved. The following mathematical models were implemented: methods for assessing, measuring and predicting the aggregate amount of aggregate losses, calculating and coherent distribution of the amount of risk capital, a mechanism for supplementing own data by mapping information about the losses of external organizations was proposed, taking into account the effect of the time structure of money and the presence of a threshold of significance, when modeling the amount of losses. In the third section of the chapter, the basic facts of copula theory are presented, which are necessary for modeling dependent stochastic processes, and the correlation measures invariant to monotonic transformations are discussed. An algorithm for stochastic modeling of random processes with known distribution functions and a predetermined dependency structure using a Gaussian copula has been implemented. Using copula theory, an algorithm for generating dependent processes that simulates the frequency of occurrence of losses is implemented. Section 2.5 describes the stochastic Monte Carlo model, developed and implemented in the MATLAB package, for estimating the probability distributions of the total losses of a credit institution for the general case, using the Gaussian and Student t-copulas and the fast Fourier transform. This model formed the basis of the AMA model, the results of which are discussed in the third chapter. As an alternative to the Basel II VaR quantile function for calculating the amount of capital to cover operational risk, Section 2.6 proposes the use of coherent risk measures. The measure (Expected ShortFall - ES), which satisfies the condition of subadditivity, allows obtaining results that are more resistant to various extreme distributions of losses. The problem of coherent distribution of risk capital between the areas of activity - and / or divisions of the credit institution has been posed and solved. The result is that in terms of non-atomic game theory, the principle of coherent distribution of risk capital can be uniquely determined through the Aumann-Shapley vector, which always exists and belongs to the core of the game.
In the third chapter, the main stages are developed - the implementation and information support of the integrated operational risk management system of the credit institution. The key points of the creation of internal regulations and methods governing the process of operational risk management, which are subject to mandatory coverage in accordance with the requirements of the Central Bank of the Russian Federation and Basel II recommendations, are given. In addition to calculating quantitative indicators of operational risk, it is recommended to monitor the qualitative indicators of operational risk that maximally characterize the main areas of the credit institution's activities exposed to operational risk. Section 3.1 has developed a comprehensive system of indicators (KRI - key risk indicators) for medium-sized credit institutions.
As a demonstration of the developed quantitative methods of operational risk management, in the second part of the third chapter, a simplified implementation of the AMA model is considered using the example of calculating the CaR value for an average credit bank. The comparison of risk capital values calculated on the basis of different approaches and for different risk measures and levels of significance is carried out. In Section 3.3, we analyze the sensitivity of the implemented model for various perturbations of the input parameters. The assessment of the expected economic effect from the implementation of the developed models and methods of operational risk management of credit institutions in comparison with existing approaches has been carried out.
In the conclusion, the main results and conclusions of the study are formulated.
Chapter 1. Analysis of existing mathematical models of the bank
1.1 Introductory notes
As mentioned above, the main goal of banking is to maximize profits; minimizing banking risks is also an almost equivalent task. This means that the policy of a commercial bank should be based on a thorough assessment and simulation of various situations, analysis of many factors that affect the size of profit. These factors determine the level of banking risk; the bank's task is to minimize it.
Bank profitability = Profitability of credit resources + Profitability of investments:
where is the specific weight of the th and th types of resources,
DB - the profitability of the bank,
KR - credit resources,
Central Bank - investments in securities.
Investors purchase assets such as stocks, bonds, or real estate with the intention of earning income either from selling them at a higher price, or in the form of dividends, coupon interest, or annuities. Lenders lend money in the hope of earning interest income when the borrower repays the loan in full. Thus, lenders and investors have a common goal - to receive income or interest as a result of investment or lending activities.
A decline in the rate of return from banking operations, a shrinking customer base and a decrease in turnover on customer accounts lead to the fact that the ratio between the bank's profit and its operating costs becomes extremely unfavorable. Thus, a situation is created when banks are forced to look for ways to reduce costs and minimize risks. And this, in turn, forces Russian banks to pay special attention to financial analysis and methods of managing their resources.
The most important rule on which decision-making strategies are based in a business risk environment:
Risk and return change in the same direction: the higher the return, the higher the risk of the transaction, as a rule.
If banks want to raise additional funds, they must demonstrate to their clients that they fully take into account the risk-reward ratio.
This thesis is currently used in a number of the largest foreign banks.
Under the conditions of a planned economy, the understanding of risk and uncertainty as integral components of socio-economic development, as the most important scientific categories requiring comprehensive study, was excluded. The formation of market relations in Russia and the corresponding economic mechanisms led to the return of the concept of risk to the theory and practice of managing economic objects of all levels and forms of ownership.
Much attention is paid to modeling banking processes abroad. The idea of portfolio management or end-to-end balance sheet management has its origins in modern portfolio theory, developed in the mid-1950s. The first attempts to apply modern portfolio theory to banking were carried out in the form of linear and quadratic models of mathematical programming. Although these models were quite slender in the classical sense, they were too limited and complex for practical use. Their main value lies in the ability to penetrate the complete balance sheet management. It is useful as a guide to understanding how to manage a bank portfolio and risk.
Portfolio management concepts are illustrated using a linear programming model. Of course, in order to reduce reality to a two-dimensional problem, we had to seriously simplify the problem statement.
Let's represent the bank balance in the following simplified form:
where Central Bank - securities,
KR - loans,
DV - demand deposits,
SD - term deposits,
K - capital. Egorova N.E., Smulov A.S. Enterprises and banks: interaction, economic analysis and modeling.-M.; Delo, 2002. P.61.
Profit on securities and profit on loans will be denoted by P CB and P cr, respectively. The costs of attracting deposits and capital are assumed to be zero. Hence, the income or profit of the bank Pr is given by the equation:
We will also give a classification of analytical banking programs:
1. Level in the organizational structure of the bank: top management, middle level, performers.
2. Type of the analyzed transaction: credit transactions, securities, foreign exchange transactions, other transactions.
3. Type of the problem being solved: monitoring, analysis, optimization, modeling, forecasting, planning, control.
4. Time lag of analysis: current moment, short-term estimates, mid-term estimates, long-term estimates.
1.2 Features of simulation modeling of banking processes
The need to use simulation modeling is primarily due to the specifics of the Russian market. A distinctive feature of the Russian financial market is its "subjectivity", extreme dependence on non-economic factors and, as a result, a high degree of uncertainty, which makes it difficult to make informed financial decisions.
This uncertainty is created by:
1. instability of the external environment of Russian banks, lack of clearly established rules and procedures for organizing various sectors of the financial market (institutional aspect);
2. the lack of a sufficiently developed apparatus for predicting the macroeconomic situation in uncertain conditions and analyzing a plurality of factors (instrumental aspect);
3. the impossibility of taking into account and formalizing all ties to build an economic and mathematical model that adequately reflects the structure of the financial market (cognitive aspect);
4. inaccessibility of reliable information - lack of a single information space "bank - client - financial market - state" (information aspect);
5. inadequate reflection of the real financial condition of the bank in the accounting statements (balance sheet, etc.) and, thus, - the lack of financial transparency in the bank (accounting aspect). The use of traditional tools for supporting management decisions and forecasting in these conditions is difficult, and the more valuable is the possibility of using the method of simulation modeling. Emelyanov A.A. Simulation modeling in risk management. - SPB: St. Petersburg Engineering and Economic Academy, 2000. P.132.
Many modern software products are designed specifically for forecasting the situation in the financial market. These include tools for technical analysis of the stock market, expert systems and statistical packages. These products are aimed primarily at decision makers in the government debt market.
The practice of using forecasting tools by banks and investment companies in trading on the securities market shows that the forecast is not always reliable, even from the point of view of a trend. One of the reasons for this is the limited period of statistical observations.
In turn, simulation is a tool that can be used to cover all areas of the bank's activities: credit and deposit, stock, work with foreign exchange assets. The Bank Simulation Model (MBM) does not predict market behavior. Its task is to take into account the maximum possible number of financial factors of the external environment (foreign exchange market, securities market, interbank loans, etc.) to support financial decision-making at the level of the head of the bank, the treasury, the asset and liability management committee.
In this sense, the IMB in its functions is closely related to the developed automated banking systems (ABS) of Western design, which are used by large international commercial banks.
Modeling processes in a bank allows you to simulate the registration of bank transactions and take into account the information contained in the transaction. The use of this ideology of construction is fully justified not only from the point of view of imitation of real financial flows in the bank, but also from the point of view of the practical applicability of the modeling results in the activities of the bank's financial manager.
Indeed, the balance sheet turns out to be a secondary outcome of decisions made. Both in practice and in IMB, a manager, when making a decision on a deal, assesses its risks and consequences for the bank not at once, but throughout the entire life cycle of the deal.
Simulation models are an integral part of modern banking management. Management of assets and liabilities, planning of large-scale operations requires reliable analytical techniques.
Simulation systems are widely used for the analysis, forecasting and study of various processes in various areas of the economy, industry, scientific research, both purely theoretical and practical.
The use of such systems is most effective and justified for long-term forecasting and in situations when it is impossible or difficult to conduct a practical experiment. Simulation modeling is an information technology that works with a simulation model and allows you to evaluate its parameters (hence efficiency) in an accelerated time scale.
A simulation model is software that allows you to simulate the activity of a complex object. Sometimes the simulated objects can be so complex and have so many parameters that it can take too long to create a simulation model in a standard high-level programming language to justify the results. Emelyanov A.A. Simulation modeling in risk management. - SPB: St. Petersburg Engineering and Economic Academy, 2000. P.24
There are many tasks and situations that require the use of simulation technologies. These include modeling bank scenarios, “checking” certain decisions, analyzing alternative strategies, and much more. A qualified specialist is able to lead dozens of typical and specific tasks that require analytical techniques. These include both classic banking planning tasks and home-based tasks such as coordinating commitments and receipts schedules. Simulation models allow making both rough estimates and express audit of the decisions made, as well as detailed numerical forecasts and calculations. A quick situation analysis based on a compact model of medium complexity is a valuable opportunity for any bank executive.
Simulation models make it possible to integrate the activities of all divisions of the bank into a single whole. On this basis, it becomes possible to effectively organize the entire system of operational and strategic planning of a commercial bank. Through the use of streaming approaches, information about the activities of the bank and its services takes on a concise and easy-to-read form. It lends itself to quantitative and qualitative (meaningful) analysis. A simulation model based on one of the expert packages is a reliable benchmark for the bank's management. The streaming "picture" of the bank's activities greatly facilitates both operational management and long-term planning of the bank's work.
Simulation models can be embedded in the basis of the expert complex of a commercial bank. In this case, a simulation model created on the basis of one of the expert packages is linked by data exchange channels with other specialized software packages and database spreadsheets. Such a complex can operate in real time. In terms of its capabilities, it comes close to large, expensive automation systems for bank management.
Optimization models, including multicriteria ones, have a common property - a goal is known, to achieve which one often has to deal with complex systems, where it is not so much about solving optimization problems, but about studying and predicting states depending on the chosen control strategies. And here we are faced with the difficulties of implementing the previous plan. They are as follows:
1. a complex system contains many connections between elements;
2. the real system is influenced by random factors, which cannot be taken into account analytically;
3. The possibility of comparing the original with the model exists only at the beginning, and after the application of the mathematical apparatus, since the intermediate results may have no analogues in the real system. Emelyanov A.A. Simulation modeling in risk management. -SPB: St. Petersburg Engineering and Economics Academy, 2000. P.58.
In connection with various difficulties arising in the study of complex systems, practice demanded a more flexible method, and it appeared - simulation modeling.
Usually, a simulation model is understood as a complex of computer programs that describes the functioning of individual blocks of systems and the rules of interaction between them. The use of random variables makes it necessary to carry out repeated experiments with a simulation system (on a computer) and subsequent statistical analysis of the results. A very common example of using simulation models is solving a queuing problem using the Monte Carlo method.
Thus, working with a simulation system is an experiment carried out on a computer. What are the benefits?
1. greater proximity to the real system than mathematical models;
2. the block principle makes it possible to verify each block before it is included in the overall system;
3. the use of dependencies of a more complex nature, not described by simple mathematical relationships.
The listed advantages determine the disadvantages:
1.Build a simulation model longer, more difficult and more expensive;
2. to work with the simulation system, it is necessary to have a computer suitable for the class;
3. the interaction between the user and the simulation model (interface) should not be too complicated, convenient and well known;
4. Building a simulation model requires a deeper study of the real process than mathematical modeling. Emelyanov A.A. Simulation modeling in risk management. -SPB: St. Petersburg Engineering and Economic Academy, 2000. P.79.
The question arises: can simulation modeling replace optimization methods? No, but it conveniently complements them. A simulation model is a program that implements a certain algorithm, to optimize the control of which the optimization problem is first solved.
So, neither a computer, nor a mathematical model, nor an algorithm for its study, separately, can solve a sufficiently complex problem. But together they represent the force that allows you to know the world around you, to manage it in the interests of man.
Considering the complex of tasks facing banking analysts, this system should provide:
1. calculation of indicators of the current and future financial conditions of the bank;
2. forecast of the state of individual financial transactions and the bank's balance sheet as a whole;
3. assessment of the attractiveness of individual financial transactions;
4. synthesis (formation) of management decisions;
5. evaluation of the effectiveness of the adopted management decision;
6. assessment of the completeness and non-redundancy of the sets of indicators of the bank's financial condition.
Performing any of the listed functions requires modeling the financial activities of the bank.
1.3 Bank functioning model
The set of methods used for the analysis and modeling of banking activities is extensive and varied. Throughout the evolution of the mathematical theory of banks, methods of mathematical statistics, optimal control theory, theory of random processes, game theory, theory of operations research, etc. have been used. It should be remembered that a bank is a complex entity that requires an integrated approach. It will be extremely difficult to create an integrated model of a bank that simultaneously encompasses liquidity management, the formation of an asset portfolio, the formation of a credit and deposit policy, etc., therefore we will describe the functioning of the bank in a rather aggregated manner.
Consider the bank's operation over a fairly long time interval.
Let the bank receive income in the form of payment for its services for carrying out settlements of guarantee operations, brokerage services (or other income independent of the asset portfolio) - and income from securities acquired with free funds that make up the portfolio of banking assets in aggregate.
Income from purchased securities consists of interest on securities - and payments of invested funds upon redemption or sale of securities -
(in case of a promotion
where is the interest rate on purchased securities
average time to maturity of securities purchased by the bank. Kolemaev V.A. Mathematical economics. - M .: UNITI, 1998.S. 68.
The bank also receives borrowed funds from its placement of its securities at a rate of W. We will assume that the securities issued by the bank are initially placed and redeemed at par, and the interest income on them is determined based on the situation in the financial market at the time of issue ...
The bank primarily directs the received income to pay the costs of raising funds, which consist of interest payments on placed securities - and payments of principal amounts of borrowed funds -
where is the interest rate on the placed securities
Average time to maturity of securities issued by the bank.
In addition, the bank bears expenses that do not depend on the volume of its liabilities - where:
Consumer price index,
To pay for the lease of premises, to pay for telecommunications costs, as well as other costs that do not depend on the amount of attracted funds (liabilities).
The bank then pays the necessary taxes. The bank uses the remaining funds to invest in its own infrastructure (internal investments) - and for dividend payments -.
The fact that the bank is obliged to pay some expenses from its net profit can be taken into account by increasing the amount of expenses by dividing by (1-tax rate). There are also taxes levied on income regardless of the costs incurred in generating that income, such as a tax on road users. Such taxes can be taken into account by multiplying the amount of income in advance by (1-tax rate). Similar methods can take into account other features determined by tax deductions, so we will not consider below the problems associated with taxation and tax benefits for some securities, for example, government securities. Note that the costs are paid by the bank in a specific order. First of all, the bank is obliged to redeem previously issued securities and pay interest on them, then it pays expenses that do not depend on the volume of liabilities, taxes, and only after that can it pay dividends.
If the bank has free funds, then it directs them to purchase securities (external investments) at a speed of -. In case of a lack of funds, the securities in the bank's portfolio can be sold, then it has a negative sign. Artyukhov SV, Bazyukina O.A., Korolev V.Yu., Kudryavtsev A.A. Optimal pricing model based on risk processes with random premiums. // Systems and means of informatics. Special issue. - M .: IPIRAN, 2005.S. 102
The amount of money, securities purchased by the bank and securities placed by the bank change over time as follows:
where is the expenditure of money for the purchase of securities (the receipt of money from their sale), and is a fairly small time constant characterizing the quality of the bank's assets, in the sense of liquidity. If a bank places all of its assets in any one segment of the financial market, then there is a value for it that characterizes the degree of development of this segment. In the general case, it is obtained as a weighted average by the volume of assets from the values characterizing the degree of development of each of the "segments of the financial market in which the assets are placed. Since we do not consider the problem of asset formation in this work, A is assumed to be a given value.
The maximum amount of funds that a bank can raise by placing its own securities is limited and depends mainly on the amount of the bank's equity capital, the structure of its balance sheet, the quality of the bank's investment portfolio and other less important indicators of its work. We will assume that
where is the bank's reliability coefficient,
The volume of the bank's own funds.
The placement by the bank of its own securities, in order to attract borrowed funds, also takes place at a certain limited speed, therefore
where is the time constant characterizing the degree of development of the market for other securities issued by the bank. It depends on how developed the bank's infrastructure is, how large the number of market participants with whom the bank cooperates.
Let's introduce a variable - the value of the portfolio of purchased securities. Then equations (1.4) - (1.6) take the form
We introduce dimensionless controls: through which the rate of spending money for the purchase of securities and the rate of receipt of money from the placement of bank securities are expressed as follows:
The value corresponds to buying / selling securities of third-party issuers as quickly as the efficiency of the securities market allows. The value corresponds to the fastest attraction of borrowed funds by the bank, and - to a complete refusal to attract funds.
The main feature of money - which makes it significantly different from the securities acquired by the bank, even government securities - is the ability to use them to pay the current expenses of the bank. The flow of payments cannot be carried out if there is not a sufficient supply of money, therefore, the speed of payments is limited and depends on the amount of money:
where is the characteristic time of receipt of funds in the bank (payments). Restrictions of this type are called liquidity restrictions.
Payments made by the bank must be divided into two groups:
Obligatory payments. These include payments for the redemption of securities issued by the bank - payment of interest on securities - expenses that do not depend on the volume of liabilities - In practice, the bank may delay mandatory payments, but this will lead to serious financial losses, and with a long delay, to its recognition insolvent and eventually to liquidation. We will assume that the delay in obligatory payments is completely excluded, that is, the bank is required to constantly maintain liquidity.
Optional payments. Making these payments depends on the management and owners of the bank. These include domestic investment - and dividends - pC 2.
For the bank to maintain liquidity, it is necessary that:
for all (1.11)
Thus, we get the first phase constraint for our problem - condition (1.11).
Note that this inequality, under the condition of nonnegativity, in particular, implies that for all
Making optional payments is also limited in speed:
According to this inequality, a dimensionless control can be introduced so that:
Since the volume of domestic investment depends on the bank's preservation of its share in the financial services market, it is possible to classify expenses, in a sense, as mandatory, at least in most of the planning area. (After reaching the planning horizon T, the bank can be liquidated by its owners). Since dividend payments cannot be negative, we get another phase constraint:
for all (1.13)
Thus, we have come to the conclusion that domestic investment is indeed obligatory in the sense of the constraint (1.13).
We will assume that on the planning site the bank does not receive "super profits", that is, profits greater than its own capital, which do not depend on the volume of assets. Consequently, the maximum amount of money that he can attract and receive in the form of profit is limited by some constant, i.e. for all, and this is the third phase constraint (1.14).
The estimate can be obtained based on the maximum amount of borrowing, the ratio of interest rates for attracting and placing funds, the amount of income that does not depend on the amount of assets.
Note that for most of the planning site, it should be close to zero, since it is not profitable for a bank to keep cash that does not bring income, because there are always absolutely reliable government securities on the financial market that bring a fixed positive income.
The absence of "super profits" also means that the relative growth rate of the securities rate is limited in the planning area:
We will describe the interests of the bank (its owners) by the desire to maximize the discounted utility of future dividend payments over a fairly long time interval. ... The coefficient is called the coefficient of discounting the utility of dividend payments. Then the functional to be maximized is written as follows:
where is the utility function of dividend payments.
When the utility of consumption plays the role, it is usually required that it be continuous, monotonic, concave and bounded from above, and is also imposed on the condition. The last condition guarantees the positiveness of the current consumption at each moment of time. Since dividends may not be paid, we will not enforce the condition, assuming that the utility function has a low aversion to zero consumption.
If the utility function has a constant relative aversion to risk according to Arrow-Pratt: then it can be shown that it can be written as:
To get rid of the high aversion to zero consumption, consider a slightly modified utility function
In this case, the relative aversion to risk will depend on the volume of consumption:. Based on (1.9) and (1.11), we obtain
Instead of function (1.13), consider the straight line passing through the points
Since the function (1.17) will be negative for any amount of dividends, that is, it is bounded from above by zero, and is also continuous and monotone for any. Such a utility function has zero relative aversion to risk according to Arrow-Pratt, and by varying the parameter, only the nominal value of dividend payments can be changed. This fact underlines the difference in attitude to risk between the private consumer and the commercial organization. On the one hand, the latter does not have aversion to risk, since it can exist indefinitely, in comparison with the life span of a person, and is not subject to dangers, like living beings. On the other hand, a private consumer who has spent the amount of 2 * M rubles receives more satisfaction from the first M rubles spent than from subsequent ones, which determines the concavity of the consumption utility function for individuals. We will assume that doubling dividend payments leads to a doubling of their usefulness for recipients, of whom there are quite a few, and they include both individuals and legal entities. This determines the linearity of the dividend payout utility function. In what follows, we will use the utility function (1.17).
Thus, we obtain the optimal control problem in continuous time
In addition, there is a boundary condition under which it means that the bank is obliged to repay its debt by the end of the planning period.
Here - phase variables, - controls. Here - the predicted values of the corresponding variables - are considered to be given non-negative functions of time, - are constants with the dimension of time.
Note that if at some point vanishes, then according to equation (1.21), i.e. the solution does not decrease at this point. Accordingly, if it reaches a value at some point, then the solution does not increase. Thus, under the controls, from equation (1.21), condition and continuity, we obtain that on the entire segment the volume at par of the placed securities of the bank is nonnegative, i.e., and does not exceed the admissible maximum -, for all (generally speaking on ).
Then, from the conditions and conditions of non-negativity of the given functions, as well as non-negativity, we obtain that for all. Assuming continuity, it can be shown using equation (1.20) as for all. In what follows we will assume that and are continuous and piecewise continuous on.
Since and from equation (1.20) it follows that. Using this inequality, it is easy to show the existence of such that, for everyone.
We will not, as previously assumed, consider how the portfolio of securities purchased by the bank is formed, depending on the reliability, profitability and liquidity of the latter, as well as on the preferences of the bank's management. All bank assets will be presented in aggregated form - one variable.
From the above, it can be seen that the bank's credit and deposit policy, defined in the management model and, is inextricably linked with the policy of making dividend payments set by the management, therefore, we will further explore them jointly.
For the convenience of further study of the work, we write out the designations separately:
The amount of free funds of the bank - cash at the cash desk of the bank, or money on the correspondent. bank accounts in the settlement centers of the Central Bank of the Russian Federation, as well as at the correspondent. accounts with other banks
Amount of purchased securities at par
Volume of placed securities at par
Income independent of the volume of assets (commissions for settlement and cash services, warranty operations, brokerage services, etc.)
Planning horizon
The volume of the bank's own funds (capital)
Bank reliability ratio
The rate at which the bank spends funds for the maintenance of the management apparatus, payment for the lease of premises, etc. or expenses that do not depend on the volume of the bank's liabilities in prices at the initial moment of time
The rate of reinvestment in the bank's infrastructure (internal investment) in prices at the initial moment of time
The speed of dividend payments in prices at the initial moment of time
Current market rate of securities purchased by the bank
Market value of the bank's securities portfolio
Time constant characterizing the degree of development of the financial market, taking into account the distribution of the bank's assets by its sectors
Time constant characterizing the degree of development of the market for securities issued by the bank
The nominal growth index of the portfolio of securities purchased by the bank. For each purchased security, the nominal rate is adjusted to the annual rate, taking into account reinvestment, then the weighted average annual rate for all securities in the bank's portfolio is calculated. The index is defined as ln (1 + "weighted average annual rate")
Effective growth index of the portfolio of securities purchased by the bank
Growth index of total debt on placed securities. For each placed security, the nominal rate is adjusted to the annual rate, taking into account debt refinancing through new placements, then the weighted average annual rate for all placed securities is calculated. The index is defined as ln (1 + + "weighted average annual rate")
Average maturity time of securities purchased by the bank - average maturity time of securities issued by the bank - consumer price index
Inflation index
Typical time of payments (cash receipts)
The speed of circulation of money in the banking system
The rate of spending money on the purchase of securities of third-party issuers, or the receipt of money from their sale
The rate of receipt of money from the placement of bank securities
Discounting factor of the utility of dividend payments
Arrow-Pratt relative risk aversion, a parameter used to define the utility function of dividend payments
M * - the maximum amount of money that can belong to the bank
Utility function of dividend payments, continuous, monotonic
Management of bank dividend payments
Management of placement of free funds of the bank
Management of raising funds to the bank.
1.4 The concept of risk in banking
Risk - the possible danger of an adverse outcome.
In market conditions, each of its participants accepts certain rules of the game and, to a certain extent, depends on the behavior of partners. One of these rules can be considered the willingness to take on risk and take into account the possibility of its implementation in their activities.
Risk is usually understood as the likelihood, or rather the threat of the bank losing part of its resources, loss of income or the appearance of additional costs as a result of certain financial transactions. Shchelov O. Management of operational risk in a commercial bank. Accounts department and banks, 2006 - №6. P. 112
In a crisis, the problem of professional management of banking risks, operational accounting of risk factors are of paramount importance for financial market participants, and especially for commercial banks.
The guiding principle in the work of commercial banks in the transition to market relations is the desire to obtain the greatest possible profit. The higher the expected profitability of the operation, the greater the risks. Risks are formed as a result of deviations of valid data from the assessment of the current state and future development.
The modern banking market is unthinkable without risk. The risk is present in any operation, only it can be of different scales and be "mitigated" and compensated in different ways. It would be extremely naive to look for options for carrying out banking operations that would completely eliminate risk and would guarantee a certain financial result in advance.
1.4.1 Classification of banking risks
In the course of their activities, banks are faced with a set of different types of risks, differing in the place and time of occurrence, external and internal factors that affect their level, and, consequently, the methods of their analysis and methods of describing them. Lobanov A.A., Chugunov A.V. Encyclopedia of Financial Risk Management. - M., Alpina Business Books, 2005.S. 89. All types of risks are interrelated and have an impact on the bank's activities.
Depending on the sphere of influence or the occurrence of banking risk, they are divided into external and internal.
External risks include risks that are not related to the activities of the bank or a specific client, political, economic and others. These are losses resulting from the outbreak of war, revolution, nationalization, a ban on payments abroad, consolidation of debts, the introduction of an embargo, the abolition of an import license, an exacerbation of the economic crisis in the country, natural disasters. Internal risks, in turn, are divided into losses in the main and auxiliary activities of the bank. The former represent the most common group of risks: credit, interest rate, currency and market risks. The latter include losses on the formation of deposits, risks of new types of activities, risks of bank abuse.
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Federal Agency for Education
T O M S K I J P O L I T E X N I Ch E S K I J U N I V E R S I T E T
APPROVED
Dean of AVTF
Gaivoronsky S.A.
"_____" _______________ 2009
CALCULATION OF OPERATIONAL RISKS
methodological instructions for the course
CALCULATION OF OPERATIONAL RISKS
Course Guidelines Course Guidelines
"Risk theory and modeling of risk situations"
for students of the specialty 080116 "Mathematical Methods in Economics"
Tomsk: Ed. TPU, 2009 .-- 26 p.
Compiled by: A.I. Kochegurov
Reviewer: Babushkin Yu.V.
Methodological instructions were discussed at a meeting of the Department of Applied Mathematics on November 16, 2008.
Head department V.P. Grigoriev
1. The concept of economic risk and risk classification
The processes currently taking place in Russia, the changed business conditions have demanded a reorientation of the principles of work of enterprises to the analysis and assessment of a variety of external and internal factors that affect the efficiency of their activities. In foreign countries, even in relatively stable economic conditions, considerable attention is paid to the problem of risk research. The leading principle in the work of an organization (industrial enterprise, commercial bank, trading company) is the pursuit of profit. This desire is limited by the possibility of incurring losses. This is where the concept of risk appears and is formed.
I would like to note that this concept has a fairly long history. But the most actively began to study various aspects of risk only in the late 19th - early 20th centuries.
There was no interest in the problem of economic risks in the USSR, and the reasons for this are obvious: the economic policy of the USSR for a long time corresponded to an orientation towards the predominantly extensive development of the country's national economy and the domination of administrative methods of management. All this led to the fact that the substantiation of the effectiveness of economic activity in a planned economy and, accordingly, all feasibility studies of any projects did without risk analysis.
The implementation of modern economic reform in Russia aroused interest in the consideration of risk in economic activity, and the theory of risk itself in the process of the formation of market relations not only received its further development, but became practically in demand.
At the moment, there is still no unambiguous understanding of the essence of risk. Each financial manager has his own idea of risk, methods of assessing it and how to determine its size. In addition, risk is a complex phenomenon that has many non-coinciding and sometimes opposite foundations and prerequisites, which determines the possibility of the existence of several definitions of risk concepts from different points of view, and here are just a few of them:
risk - possible danger; acting at random in the hope of a happy outcome;
risk is a potential, numerically measurable possibility of loss;
risk - uncertainty associated with the possibility of adverse situations and consequences arising during the project implementation;
risk - a concept used to express uncertainty about events and / or their consequences that can materially affect the objectives of the organization;
risk - any event, as a result of which the financial results of the company's activities may be lower than expected.
It should also be noted that the concept of “risk” is interpreted differently and depending on the scope of the risk circulation. For mathematicians, risk is a distribution function of a random variable, for insurers it is an insurance object, the amount of possible insurance compensation associated with an insurance object, for investors it is the uncertainty associated with the value of investments at the end of the period, the probability of not reaching the goal, for economists it is an event, associated with a hazardous phenomenon that may or may not occur, etc.
These definitions clearly show the close relationship between risk, probability and uncertainty. Therefore, for the most complete and accurate disclosure of the “risk” category, it is necessary to substantiate such concepts as “probability” and “uncertainty”, since the probabilistic nature of market activity and the uncertainty of the situation during its implementation underlies risks.
Let's consider the concept of probability. This term is fundamental to the theory of probability and allows you to quantitatively compare events according to the degree of their possibility. The probability of an event is a certain number, which is the greater, the more possible the event is. Probability is the possibility of getting a certain result. Obviously, the more likely event is the one that occurs more often. Thus, first of all, the concept of probability is associated with the experimental, practical concept of the frequency of an event.
The accuracy of measuring probabilities depends on the volume of statistical data and the possibility of their use for future events, i.e. from preserving the conditions in which past events took place. But at the same time, in many cases, when making decisions, statistical data are very small in volume or are completely absent, therefore, another way of measuring the probabilities of a situation is used, based on the subjective views of the decision maker.
In this regard, the probabilities measured in this way are called the subjective probabilities of the situation. When determining subjective probabilities, the subject's opinion, reflecting the state of his information fund, comes first. In other words, the subjective probability is determined on the basis of an assumption based on the judgment or personal experience of the evaluator (expert), and not on the frequency with which a similar result was obtained under similar conditions. Hence the wide variation in subjective probabilities, which is explained by the spectrum of different information or the possibilities of operating with the same information.
The dependence on the amount of initial information and on the subject leads to the fact that uncertainty is added to the probabilistic situation. Thus, the concept of probability alone is not enough to characterize risk.
Uncertainty implies the presence of factors in which the results of actions are not deterministic, and the degree of possible influence of these factors on the results is unknown, for example, it is incomplete or inaccurate information.
The conditions of uncertainty that occur in any type of entrepreneurial activity are the subject of research and the object of constant observation of economists of various profiles.
Such an integrated approach to the study of the phenomenon of uncertainty is due to the fact that business entities in the course of their functioning are dependent on a number of factors that can be subdivided into external (legislation, market reaction to manufactured products, actions of competitors) and internal (competence of the firm's personnel, erroneous defining the characteristics of the project, etc.).
Another approach to the classification of uncertainty is used in the design of work and it is associated with human uncertainty, with the impossibility of accurately predicting the behavior of people in the process of work. Technical uncertainty is much less than human uncertainty, it is associated with the reliability of equipment, predictability of production processes, the complexity of technology, the level of automation, etc. Social uncertainty is determined by the desire of people to form social connections and help each other.
In these conditions, the development of construction projects and business plans, forecasting and planning of production volumes, sales and cash flows can be approximate calculations. Often, activities instead of expected profits can bring losses.
Further, it should be borne in mind that the risk accompanies all processes in the enterprise, regardless of whether they are active or passive. In this case, a third party of risk is revealed - its belonging to any activity. For example, if an enterprise plans to implement a project, it is exposed to investment and market risks; and if the company does not take any action, it again bears risks - the risk of unearned profits, market risk.
This situation is already incorporated in the very definition of the concept of "enterprise", because in the implementation of its activities, the enterprise sets certain goals - to receive income, to make costs, etc. Consequently, it plans its activities. But, choosing one or another development strategy, the company may lose its funds or receive an amount less than planned. This is due to the uncertainty of the situation in which it finds itself. In conditions of uncertainty, the management of the enterprise has to make decisions, the likelihood of successful implementation of which (and, therefore, of receiving income in full) depends on many factors affecting the enterprise from the inside and from the outside. In this situation, the concept of risk manifests itself, which means that the risk can be characterized as the likelihood of not receiving the planned income under the conditions of uncertainty accompanying the activities of the enterprise.
Then it is possible to give the most appropriate definition of the concept of "risk". So, risk is the likelihood of an event or the occurrence of circumstances associated with a given structure of business processes that can affect the achievement of the tasks.
This approach looks at the outcome of an event without interrupting the cause. In addition, it draws a dividing line between controllable causes of risk and uncontrollable ones, which will be considered "events" or "circumstances". Risk factors that, in combination with risk events, can cause damage are organizational gaps and should be investigated for their place in the business process diagram.
Thus, it should be noted that risk is a complex concept with uncertainty as its cause and is closely related to probabilistic processes. Risk is inextricably linked with the activities of the enterprise, regardless of whether this activity is active or passive. However, there are common goals that an efficiently structured risk management process should contribute to.
As a rule, the main goals that companies pursue when creating a risk management system are as follows:
the most efficient use of capital and maximum income;
increasing the sustainability of the company's development, work efficiency, overall productivity, reducing the likelihood of losing part or all of the company's value;
improving the image.
But whatever the goal - to eliminate or manage risks effectively - the value of an approach that addresses all three aspects of risk (event, impact, and organizational structure) is extremely high. This logical approach occurs three times: before the event (prevention), during the event (detection), and after the event (protection). A threat without prevention leads to a risky event, an event without detection leads to the omission of the event itself, and an omission of an event without protection leads to damage.
So, in order to achieve the above goals, it is necessary to reveal the essence of the main types of risks that the company faces.
Since the concept of risk covers almost all the activities of an economic entity, then, as a result, there is a variety of risks arising in the work of a company, and in order to properly manage risks, a company must know what risks its activities are associated with. The classification of these risks is a rather complex problem that economists have been dealing with for quite some time. Moreover, there are more than 40 different risk criteria and more than 220 types of risks, so there is no common understanding in the economic literature on this issue.
So, one of the first to classify risks was J.M. Keynes. He approached this issue from the side of an entity engaged in investment activities, highlighting three main types of risks [J. M. Keynes. General theory of employment, interest and money,. Chapter 11]:
entrepreneurial risk - the uncertainty of obtaining the expected income from investment;
“lender” risk - the risk of non-repayment of a loan, which includes legal risk (evasion of loan repayment) and credit risk (insufficient collateral);
the risk of a change in the value of a monetary unit - the probability of loss of funds as a result of a change in the exchange rate of the national monetary unit (market risk).
Keynes noted that these risks are closely intertwined - so the borrower, participating in a risky project, seeks to get the greatest possible difference between the interest on the loan and the rate of return; the lender, given the high risk, also seeks to maximize the difference between the net interest rate and his interest rate. As a result, the risks “overlap” each other, which investors do not always notice.
Currently, most often, especially foreign authors, adhere to the classification, which necessarily includes the following types of risks:
operational risk;
market risk;
credit risk.
Leading Western banks, experts of the Basel Committee, developers of systems for analysis, measurement and risk management adhere to a similar approach.
Several more options are added to these basic risks, which occur in one sequence or another:
business risk;
liquidity risk;
legal risk;
regulatory risk.
The last 4 risks do not appear in all developments. Thus, the risk associated with regulatory bodies is most relevant for banking organizations, therefore, it is more common in areas related to banking. Some authors include liquidity risk in the concept of market risks.
In this work, we will rely on the classification that corresponds to the considered direction of the consulting company. This, firstly, will allow at the initial stage of the analysis to be limited to those risks that have a direct impact on the work of the company. Secondly, taking into account the specifics of the organization's activities will make it possible to prioritize the study of profile risks and consider, first of all, those that have the greatest impact on the organization's activities.
The most comprehensive classifying risks are the “Risk Management Guidelines for Derivatives”. According to this document, the organization faces the following types of risks:
Credit risks (including the risk of repayment) are the probable losses associated with the refusal or inability of the counterparty to fully or partially fulfill its credit obligations. These risks exist both for banks (the classic risk of loan defaults) and for enterprises with receivables and organizations operating in the securities market.
Operational risks are the likelihood of direct or indirect losses as a result of uncontrollable events, shortcomings in business organization, inadequate control, incorrect decisions, system errors that are related to human resources (unprofessional, illegal actions of company personnel), technology, property, internal systems, relationships with the internal and external environment, legislative regulation and individual risky projects. This can include risks associated with mistakes of the company's management, its employees, problems with the internal control system, poorly developed work rules, etc. operational risk is the risk associated with the internal organization of the company's work, as well as the risk of damage to the environment (environmental risk); the risk of accidents, fires, breakdowns; the risk of disruption to the functioning of the facility due to errors in design and installation, a number of construction risks; equipment malfunctions, etc.
Liquidity risks - the risk that the firm will not be able to pay off its obligations with the available capital at a particular moment. the likelihood of a loss due to a lack of funds in the required time frame and, as a result, the inability of the company to fulfill its obligations. The onset of such a risky event may entail fines, penalties, damage to the business reputation of the company, up to and including declaring it bankrupt. For example, a company must pay off its accounts payable within two weeks, but due to a delay in payment for shipped products, it does not have cash. Obviously, the creditors will impose penalties on the company. As a rule, liquidity risk arises due to unprofessional management of cash flows, receivables and payables.
Market risks are possible losses resulting from changes in market conditions. They are associated with fluctuations in prices in commodity markets and exchange rates of currencies, rates in stock markets, etc. largely depends on the prevailing market prices.
Legal risks - the risk that, in accordance with the current legislation, the partner is not obliged to fulfill his obligations under the transaction.
Often, these risks are closely intertwined - a striking example is the situation with the notorious British bank "Barings" - the lack of internal control systems (operational risk) and, as a result, the game on the stock exchange of one of the employees led to the impossibility of closing futures positions on SIMEX (risk of loss liquidity) due to incorrect price prediction (market risk).
So, after conducting a study of the current situation in the company, we can say that at present the priority for Success is the reduction of operational risks. Therefore, in this work we will focus on operational risks, their assessment and management.
In addition to the above classification, risks can be classified according to other criteria. For example, strategic and informational risks are often identified.
Information risks are understood as the likelihood of damage as a result of the loss of information relevant to the company.
Strategic risks represent a risk of losses due to the uncertainty arising from the company's long-term strategic decisions. In addition, they affect the company as a whole, and the application of the implemented risk analysis system to them at the enterprise can often lead to a change in the company's course, give a clear assessment of the company's planned actions to create a competitive advantage and conquer the market. In assessing the company's strategic risks, both the microeconomic environment (such as close competitors, changes in market conditions or resource prices) and the macroeconomic environment (in particular, political risks that are difficult to assess) should be taken into account.
Often, according to their consequences, risks are divided into three categories:
acceptable risk- this is the risk of a decision, as a result of non-implementation of which the enterprise is threatened with loss of profit; within this zone, entrepreneurial activity retains its economic viability, i.e. losses occur, but they do not exceed the expected profit;
critical risk- this is the risk at which the enterprise is threatened with loss of revenue; in other words, the critical risk zone is characterized by the danger of losses that obviously exceed the expected profit and, in extreme cases, can lead to the loss of all funds invested by the enterprise in the project;
catastrophic risk- the risk at which the insolvency of the enterprise occurs; losses can reach a value equal to the property status of the enterprise. Also, this group includes any risk associated with a direct danger to human life or the occurrence of environmental disasters.
The basis for the following classification of risks is also the nature of the impact on the results of the enterprise. So, the risks are divided into two types:
clean risk - the possibility of a loss or zero result;
speculative risk - the probability of getting both positive and negative results.
It is obvious that the above classifications are interrelated.
2. Modeling of risk situations and management of operational risks
2.1 Features of operational risks
Once again, we note the fact that operational risks are primarily associated with a person: direct and indirect business losses arise due to errors of personnel, management, theft and abuse, and even in cases when they are caused by malfunctions of telecommunications, computers and information technology. systems, in most cases they are based on human errors.
Before talking about modeling operational risks, let's consider their unique characteristics:
Operational risks are endogenous in nature, which means they are different for each company. They depend on the technology, process, organization, people and culture of the company, therefore, to manage operational risk, it is necessary to collect company-specific data. It should be noted that most companies do not have a long history of relevant data. And industry data may not be fully applicable.
Operational risks are dynamically and constantly changing depending on the strategy, business processes, applied technologies, competitive environment, etc., as a result of this, it becomes clear that even the historical data of the company itself may not be relevant indicators of current and future risks.
The most effective risk mitigation strategies include changes in business processes, technology, organization, and people. a modeling approach is needed that can measure the impact on operational decisions. For example, how will operational risks change if a company starts selling and servicing products over the Internet, or if outsourcing is used for a number of key functions?
The most common operational risks are:
errors in computer programs ( failure of software and information technology or systems, equipment and communication failure);
staff errors (n insufficient qualifications of employees performing this operation; bad faith execution of the established provisions and regulations; overload of personnel; random one-time errors);
distribution system errors(duplication of functions; exclusion of certain functions);
lack of a work plan or its poor quality leads to delays in making management decisions, and the formalization of plans and procedures for action in critical situations not only facilitates the identification of problematic aspects, but also reduces the risk of labor conflicts.
Historically, the highest operational risks and the largest losses from them occur when the following circumstances exist:
concentration occurs in a non-core area of activity, where the company's management is not aware of the real risks associated with these trading operations;
an event of more than a few months indicates a careless control environment, negligent management, and a lack of awareness of the seriousness of the problem.
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