Talk Overview:
This talk will discuss a general approach for stress testing correlations in stock and credit portfolios. Using Bayesian variable selection methods, we build a sparse factor structure, linking individual names or stocks with country and industry factors. Based on methods from modelling correlations in interest interest rate modelling, especially in the context of market models, we calibrate a parametric correlation matrix, where correlations of stocks / names are represented as a function of the country and industry factors. Economically meaningful stress scenarios on the factors can then be translated into stressed correlations. The method also lends itself as a r The method also lends itself as a reverse tress testing framework: using e.g. the Mahalanobis distance on the joint risk factor distribution, allows to infer worst-case correlation scenarios. Natalie will give examples of stress tests including an application to analyse a USD 6.2 bn loss by JP Morgan in 2012, known as the “London Whale”. This is joint work with Fabian Woebbeking.
Speaker Overview:
Natalie Packham is Professor of Mathematics and Statistics at Berlin School of Economics and Law. Natalie has several years of industry experience as a front office software engineer at an investment bank, and is frequently involved in industry-related research and consulting projects. Her research Her research expertise includes Mathematical Finance, Financial Risk Management and Computational Finance, and her academic work has been published in Mathematical Finance, Finance & Stochastics, Quantitative Finance, Journal of Applied Probability and many other academic journals. Natalie is associate editor of “Methodology and Computing in Applied Probability” and co-chair of the GARP Research Fellowship Advisory Board. She holds an M.Sc. in Computer Science from the University of Bonn, a Master’s degree in Banking & Finance from Frankfurt School, and a Ph.D. in Quantitative Finance from Frankfurt School.
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