Mathematical Finance, Stochastic Analysis, and Machine Learning Seminar by Silvana Pesenti: Reverse Stress Testing: Static and Dynamic




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Silvana Pesenti, Assistant Professor, Department of Statistical Sciences, University of TorontoUniversity of Toronto


Reverse Stress Testing: Static and Dynamic


This presentation is based on a collection of works that focus on the development of a mathematical framework for reverse stress testing. In the static setting, a model comprises of a vector of random input factors, an aggregation function mapping input factors to a random output, and a (baseline) probability measure. As is common in risk management, the value of the risk measure applied to the output is a decision variable. Therefore, it is of interest to associate a critical increase in the risk measure to a change in the baseline model. We propose a global and model-independent framework, termed “reverse stress testing”, comprising two steps: (a) an output stress is specified, corresponding to an increase in a risk measure(s) and (b) a (stressed) probability measure is derived, minimising a divergence to the baseline probability, under constraints generated by the output stress. 

In the dynamic setting the model comprises of a stochastic process and a baseline probability measure. In an analogous fashion, the reverse sensitivity framework first specifies a stress on the distribution of the stochastic process at terminal time, and second derives the stressed probability measure that minimises, along the path, a divergence to the baseline probability. We characterise the unique solution to this optimisation problem, that is the stressed stochastic process closest to the baseline process that fulfills the stress. 

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Meeting ID: 817 7010 7418

Passcode: iitmath


Mathematical Finance, Stochastic Analysis, and Machine Learning

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