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The Risks of Financial Institutions$
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Mark Carey and Rene M. Stulz

Print publication date: 2007

Print ISBN-13: 9780226092850

Published to Chicago Scholarship Online: February 2013

DOI: 10.7208/chicago/9780226092980.001.0001

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PRINTED FROM CHICAGO SCHOLARSHIP ONLINE (www.chicago.universitypressscholarship.com). (c) Copyright University of Chicago Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in CHSO for personal use.date: 17 September 2021

Practical Volatility and Correlation Modeling for Financial Market Risk Management

Practical Volatility and Correlation Modeling for Financial Market Risk Management

(p.513) 11 Practical Volatility and Correlation Modeling for Financial Market Risk Management
The Risks of Financial Institutions

Torben G. Andersen

Tim Bollerslev

Peter F. Christoffersen

Francis X. Diebold

University of Chicago Press

This chapter demonstrates how important it is to recognize time-varying volatility and correlation in value at risk estimation. It also illustrates how new techniques in multivariate time-series estimation could be usefully brought to bear to address some of the problems that pushed banks toward historical simulation and stress testing. Then, it considers various strategies for modeling and forecasting realized covariances, treating them as directly observable vector time series. Standard model-free methods rely on false assumptions of independent returns. Generalized autoregressive conditional heteroskedastic (GARCH) volatility models provide a convenient and parsimonious framework for modeling key dynamic features of returns. Recent advances in multivariate GARCH modeling are likely to be useful for medium-scale models. Volatility measures based on high-frequency return data hold great promise for practical risk management. Risk management requires fully specified conditional density models, not just conditional covariance models.

Keywords:   risk estimation, modeling, volatility models, risk management, covariance models, time-varying volatility, multivariate time-series

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