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 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.

Keywords: introduction; solvency; use; techniques; valueatrisk var; notions; var; basic; insurance; companies; banks; classical valueatrisk; conditional; tail; var extensions; portfolio; asset; assets; simulation; rates