VaR Methodology for Non-Gaussian Finance

VaR Methodology for Non-Gaussian Finance
Author :
Publisher : John Wiley & Sons
Total Pages : 176
Release :
ISBN-10 : 9781118733981
ISBN-13 : 1118733983
Rating : 4/5 (983 Downloads)

Book Synopsis VaR Methodology for Non-Gaussian Finance by : Marine Habart-Corlosquet

Download or read book VaR Methodology for Non-Gaussian Finance written by Marine Habart-Corlosquet and published by John Wiley & Sons. This book was released on 2013-05-06 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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