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...

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Main Authors Habart-Corlosquet, Marine, Janssen, Jacques, Manca, Raimondo
Format eBook Book
LanguageEnglish
Published Newark John Wiley & Sons, Incorporated 2013
ISTE Ltd/John Wiley and Sons Inc
Wiley-Blackwell
ISTE Press
Wiley-ISTE
Edition1
SeriesFocus series in finance, business and management
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Online AccessGet full text
ISBN1848214642
9781848214644
DOI10.1002/9781118733691

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Abstract 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 companies and banks, value at risk (VaR) - one of the most popular risk indicator techniques plays a fundamental role in defining appropriate levels of equities. The aim of this book is to show how new VaR techniques can be built more appropriately for a crisis situation. VaR methodology for non-Gaussian finance looks at the importance of VaR in standard international rules for banks and insurance companies; gives the first non-Gaussian extensions of VaR and applies several basic statistical theories to extend classical results of VaR techniques such as the NP approximation, the Cornish-Fisher approximation, extreme and a Pareto distribution. Several non-Gaussian models using Copula methodology, Lévy processes along with particular attention to models with jumps such as the Merton model are presented; as are the consideration of time homogeneous and non-homogeneous Markov and semi-Markov processes and for each of these models. Contents 1. Use of Value-at-Risk (VaR) Techniques for Solvency II, Basel II and III. 2. Classical Value-at-Risk (VaR) Methods. 3. VaR Extensions from Gaussian Finance to Non-Gaussian Finance. 4. New VaR Methods of Non-Gaussian Finance. 5. Non-Gaussian Finance: Semi-Markov Models. About the Authors Marine Habart-Corlosquet is a Qualified and Certified Actuary at BNP Paribas Cardif, Paris, France. She is co-director of EURIA (Euro-Institut d'Actuariat, University of West Brittany, Brest, France), and associate researcher at Telecom Bretagne (Brest, France) as well as a board member of the French Institute of Actuaries. She teaches at EURIA, Telecom Bretagne and Ecole Centrale Paris (France). Her main research interests are pandemics, Solvency II internal models and ALM issues for insurance companies. Jacques Janssen is now Honorary Professor at the Solvay Business School (ULB) in Brussels, Belgium, having previously taught at EURIA (Euro-Institut d'Actuariat, University of West Brittany, Brest, France) and Telecom Bretagne (Brest, France) as well as being a director of Jacan Insurance and Finance Services, a consultancy and training company. Raimondo Manca is Professor of mathematical methods applied to economics, finance and actuarial science at University of Roma "La Sapienza" in Italy. He is associate editor for the journal Methodology and Computing in Applied Probability. His main research interests are multidimensional linear algebra, computational probability, application of stochastic processes to economics, finance and insurance and simulation models.
AbstractList 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 companies and banks, value at risk (VaR) - one of the most popular risk indicator techniques plays a fundamental role in defining appropriate levels of equities. The aim of this book is to show how new VaR techniques can be built more appropriately for a crisis situation. VaR methodology for non-Gaussian finance looks at the importance of VaR in standard international rules for banks and insurance companies; gives the first non-Gaussian extensions of VaR and applies several basic statistical theories to extend classical results of VaR techniques such as the NP approximation, the Cornish-Fisher approximation, extreme and a Pareto distribution. Several non-Gaussian models using Copula methodology, Lévy processes along with particular attention to models with jumps such as the Merton model are presented; as are the consideration of time homogeneous and non-homogeneous Markov and semi-Markov processes and for each of these models. Contents 1. Use of Value-at-Risk (VaR) Techniques for Solvency II, Basel II and III. 2. Classical Value-at-Risk (VaR) Methods. 3. VaR Extensions from Gaussian Finance to Non-Gaussian Finance. 4. New VaR Methods of Non-Gaussian Finance. 5. Non-Gaussian Finance: Semi-Markov Models. About the Authors Marine Habart-Corlosquet is a Qualified and Certified Actuary at BNP Paribas Cardif, Paris, France. She is co-director of EURIA (Euro-Institut d'Actuariat, University of West Brittany, Brest, France), and associate researcher at Telecom Bretagne (Brest, France) as well as a board member of the French Institute of Actuaries. She teaches at EURIA, Telecom Bretagne and Ecole Centrale Paris (France). Her main research interests are pandemics, Solvency II internal models and ALM issues for insurance companies. Jacques Janssen is now Honorary Professor at the Solvay Business School (ULB) in Brussels, Belgium, having previously taught at EURIA (Euro-Institut d'Actuariat, University of West Brittany, Brest, France) and Telecom Bretagne (Brest, France) as well as being a director of Jacan Insurance and Finance Services, a consultancy and training company. Raimondo Manca is Professor of mathematical methods applied to economics, finance and actuarial science at University of Roma "La Sapienza" in Italy. He is associate editor for the journal Methodology and Computing in Applied Probability. His main research interests are multidimensional linear algebra, computational probability, application of stochastic processes to economics, finance and insurance and simulation models.
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 companies and banks, value at risk (VaR) - one of the most popular risk indicator techniques plays a fundamental role in defining appropriate levels of equities. The aim of this book is to show how new VaR techniques can be built more appropriately for a crisis situation. VaR methodology for non-Gaussian finance looks at the importance of VaR in standard international rules for banks and insurance companies; gives the first non-Gaussian extensions of VaR and applies several basic statistical theories to extend classical results of VaR techniques such as the NP approximation, the Cornish-Fisher approximation, extreme and a Pareto distribution. Several non-Gaussian models using Copula methodology, Lévy processes along with particular attention to models with jumps such as the Merton model are presented; as are the consideration of time homogeneous and non-homogeneous Markov and semi-Markov processes and for each of these models.
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 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 companies and banks, value at risk (VaR) - one of the most popular risk indicator techniques plays a fundamental role in defining appropriate levels of equities. The aim of this book is to show how new VaR techniques can be built more appropriately for a crisis situation.VaR methodology for non-Gaussian finance looks at the importance of VaR in standard international rules for banks and insurance companies; gives the first non-Gaussian extensions of VaR and applies several basic statistical theories to extend classical results of VaR techniques such as the NP approximation, the Cornish-Fisher approximation, extreme and a Pareto distribution. Several non-Gaussian models using Copula methodology, Lévy processes along with particular attention to models with jumps such as the Merton model are presented; as are the consideration of time homogeneous and non-homogeneous Markov and semi-Markov processes and for each of these models. Contents 1. Use of Value-at-Risk (VaR) Techniques for Solvency II, Basel II and III.2. Classical Value-at-Risk (VaR) Methods.3. VaR Extensions from Gaussian Finance to Non-Gaussian Finance.4. New VaR Methods of Non-Gaussian Finance.5. Non-Gaussian Finance: Semi-Markov Models.
Author Habart-Corlosquet, Marine
Janssen, Jacques
Manca, Raimondo
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Keywords Value at risk
Solvency II
Copule
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Snippet With the impact of the recent financial crises, more attention must be given to new models in finance rejecting "Black-Scholes-Samuelson" assumptions leading...
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SubjectTerms Black-Scholes-Modell
Computer Science
Computers and Society
Finance
Financial risk management
Finanzierungstheorie
Finanzmathematik
General Finance
Mathematics
Portfolio-Management
Probability
Prognoseverfahren
Quantitative Finance
Risikomaß
Risk Management
TableOfContents 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 portfolio -- 2.5.1. VaR methodology -- 2.6. A simulation example: the rates of investment of assets -- CHAPTER 3. VAR EXTENSIONS FROM GAUSSIAN FINANCE TO 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. Conclusion -- CHAPTER 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 model with 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 bounds -- 4.2.4. 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 time -- 5.2.8. 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-Markov VaR 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 -- CONCLUSION -- BIBLIOGRAPHY -- INDEX
Title VaR Methodology for Non-Gaussian Finance
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