Tail Risk Modelling
Tail Risk Modelling
Preface
Risk Modelling and its Myths
Mastering the R Statistical Package
Key Concepts on Probability
Tools for Describing Risk Factors and Portfolios
The Essentials of Hypothesis Testing for Risk Managers
Alternative Methods to Measure Correlation
A Primer On Maximum Likelihood Estimation
Regression in a Nutshell
Fitting Probability Distributions to Data
Practical Principal Components Analysis
Three Essential Models for Volatility
Random Numbers and Applied Simulation
Tail Risk Modelling
Conclusion to 'Market Risk Modelling (2nd edition)'
The occurrence of rare, yet extreme, events seem to becoming more apparent in financial markets around the globe. The financial crisis of 2008, the slump in global economic activity and the banking crisis in Europe and the US mean there is greater need to design robust risk modelling techniques which can predict the probability of rare risky events. VaR models have dominated to landscape for capturing large-scale portfolio risk. We will explore some of these models in this chapter. Extreme value theory (EVT) provides well-established statistical models for the computation of extreme risk measures such as the return level, value-at-risk and expected shortfall. In this chapter, we will also detail the statistical modelling of extreme observations using EVT. We begin first with an overview of VaR modelling.
VALUE-AT-RISK MODELLING
The potential for significant losses in a firm’s portfolio of assets is a major concern of portfolio managers, investors and regulators. VaR addresses this concern directly by providing a probabilistic measure of extreme market risk. The output is a single number representing the risk inherent in a portfolio. It therefore reduces the complexities of risk
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