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Practical Volatility and Correlation Modeling for Financial Market Risk Management

Listed author(s):
  • Torben G. Andersen


    (Department of Finance, Kellogg School of Management, Northwestern University)

  • Tim Bollerslev


    (Department of Economics, Duke University)

  • Peter F. Christoffersen


    (Faculty of Management, McGill University)

  • Francis X. Diebold


    (Department of Economics, Univerrsity of Pennsylvania)

What do academics have to offer market risk management practitioners in financial institutions? Current industry practice largely follows one of two extremely restrictive approaches: historical simulation or RiskMetrics. In contrast, we favor flexible methods based on recent developments in financial econometrics, which are likely to produce more accurate assessments of market risk. Clearly, the demands of real-world risk management in financial institutions - in particular, real-time risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress parsimonious models that are easily estimated, and we discuss a variety of practical approaches for high-dimensional covariance matrix modeling, along with what we see as some of the pitfalls and problems in current practice. In so doing we hope to encourage further dialog between the academic and practitioner communities, hopefully stimulating the development of improved market risk management technologies that draw on the best of both worlds.

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Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 05-007.

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Length: 41 pages
Date of creation: 11 Jan 2005
Handle: RePEc:pen:papers:05-007
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