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
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.
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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