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VaR Methodology for Non-Gaussian Finance.
With the impact of the recent financial crises, more attention must be given to new models in finance rejecting "Black-Scholes-Samuelson" assumptions leading to what is called non-Gaussian finance. With the growing importance of Solvency II, Basel II and III regulatory rules for insurance...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Wiley-ISTE,
2013.
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| Colección: | Focus series in finance, business and management.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Contents; INTRODUCTION; CHAPTER 1. USE OF VALUE-AT-RISK (VAR) TECHNIQUES FOR SOLVENCY II, BASEL II AND III; 1.1. Basic notions of VaR; 1.1.1. Definition; 1.1.2. Calculation methods; 1.1.3. Advantages and limits; 1.2. The use of VaR for insurance companies; 1.2.1. Regulatory approach; 1.2.2. Risk profile approach; 1.3. The use of VaR for banks; 1.3.1. Basel II; 1.3.2. Basel III; 1.4. Conclusion; CHAPTER 2. CLASSICAL VALUE-AT-RISK (VAR) METHODS; 2.1. Introduction; 2.2. Risk measures; 2.3. General form of the VaR; 2.4. VaR extensions: tail VaR and conditional VaR.
- 2.5. VaR of an asset portfolio2.5.1. VaR methodology; 2.6. A simulation example: the rates of investment of assets; CHAPTER 3. VAR EXTENSIONS FROM GAUSSIAN FINANCETO NON-GAUSSIAN FINANCE; 3.1. Motivation; 3.2. The normal power approximation; 3.3. VaR computation with extreme values; 3.3.1. Extreme value theory; 3.3.2. VaR values; 3.3.3. Comparison of methods; 3.3.4. VaR values in extreme theory; 3.4. VaR value for a risk with Pareto distribution; 3.4.1. Forms of the Pareto distribution; 3.4.2. Explicit forms VaR and CVaR in Pareto case; 3.4.3. Example of computation by simulation.
- 3.5. ConclusionCHAPTER 4. NEW VAR METHODS OF NON-GAUSSIAN FINANCE; 4.1. Lévy processes; 4.1.1. Motivation; 4.1.2. Notion of characteristic functions; 4.1.3. Lévy processes; 4.1.4. Lévy-Khintchine formula; 4.1.5. Examples of Lévy processes; 4.1.6. Variance gamma (VG) process; 4.1.7. Risk neutral measures for Lévy models in finance; 4.1.8. Particular Lévy processes: Poisson-Brownian model with jumps; 4.1.9. Particular Lévy processes: Merton modelwith jumps; 4.1.10. VaR techniques for Lévy processes; 4.2. Copula models and VaR techniques; 4.2.1. Introduction; 4.2.2. Sklar theorem (1959).
- 4.2.3. Particular case and Fréchet bounds4.2.4. Examples of copula; 4.2.5. The normal copula; 4.2.6. Estimation of copula; 4.2.7. Dependence; 4.2.8. VaR with copula; 4.3. VaR for insurance; 4.3.1. VaR and SCR; 4.3.2. Particular cases; CHAPTER 5. NON-GAUSSIAN FINANCE: SEMI-MARKOV MODELS; 5.1. Introduction; 5.2. Homogeneous semi-Markov process; 5.2.1. Basic definitions; 5.2.2. Basic properties [JAN 09]; 5.2.3. Particular cases of MRP; 5.2.4. Asymptotic behavior of SMP; 5.2.5. Non-homogeneous semi-Markov process; 5.2.6. Discrete-time homogeneous and non-homogeneous semi-Markov processes.
- 5.2.7. Semi-Markov backward processes in discrete time5.2.8. Semi-Markov backward processes in discrete time; 5.3. Semi-Markov option model; 5.3.1. General model; 5.3.2. Semi-Markov Black-Scholes model; 5.3.3. Numerical application for the semi-Markov Black-Scholes model; 5.4. Semi-Markov VaR models; 5.4.1. The environment semi-Markov VaR (ESMVaR) model; 5.4.2. Numerical applications for the semi-MarkovVaR model; 5.4.3. Semi-Markov extension of the Merton's model; 5.5. The Semi-Markov Monte Carlo Model in a homogeneous environment; 5.5.1. Capital at Risk; 5.5.2. A credit risk example.