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