The purpose of this seminar is to give you a good understanding of advanced quantitative risk measurement methods.
We start with an overall introduction to modern risk analysis and explain why risk measurement has become more important and challenging. We briefly review basic risk measures such as beta, duration, modified duration, convexity and standard deviation and discuss their limitations in a world with increasingly complex financial instruments.
We then give a thorough explanation of how “Value-at-Risk” and other measures of shortfall risk can be calculated for linear as well as non-linear exposures. We explain the use of delta-normal and delta-gamma-normal methods for the calculation of VaR for forwards, swaps and options, and we explain and demonstrate the use numerical techniques (including historical simulation and Monte Carlo simulation and principal components analysis) for calculating VaR of more complex instruments and portfolios.
Further, we explain how to back-test these “Value-at-Risk” models. As a particular case study, we look at the back-testing requirements of the Basel framework. We also take you a step further to show how the impact of estimation risks can be considered by using dynamic parametric VaR models and by correcting standard back-testing procedures.
Finally, we introduce Extreme Value Theory and explain and demonstrate its applications in finance. We present the two main approaches to estimating tail distributions: the “Block Maxima” and the “Peaks over Threshold” groups of models. We demonstrate how a “Generalized Pareto Distribution” can be fitted to real-life financial data (stock prices etc.), and we visualize results using graphical tools. We also explain and demonstrate how EVT can be used in financial risk management. We use extreme value theory to calculate conditional and non-conditional VaR, and we discuss the use of EVT in Stress Testing and in asset allocation.