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Activity Number: 618 - Modeling Extremes in Weather, Networks, and Finance
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Risk Analysis
Abstract #328827
Title: Conditional Extremes in Financial Markets
Author(s): Natalia Nolde* and Jinyuan Zhang
Companies: The University of British Columbia and INSEAD
Keywords: systemic risk; conditional extremes; heavy tails; asymmetry
Abstract:

The latest global financial crisis revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The paper proposes a method for dynamic forecasting of CoVaR, a popular measure of systemic risk. As a first step, we develop a semi-parametric framework using asymptotic results in the spirit of extreme value theory (EVT) to model the conditional probability distribution of a bivariate random vector given that one of the components takes on a large value, taking into account important features of financial data such as asymmetry and heavy tails. In the second step, we embed the proposed EVT method into a dynamic framework via a bivariate GARCH process. An empirical analysis is conducted to demonstrate and compare the performance of the proposed methodology relative to a very flexible fully parametric alternative.


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