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Practical Volatility and Correlation Modeling for Financial Market Risk Management

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  • 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)

Abstract

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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Bibliographic Info

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
Date of revision:
Handle: RePEc:pen:papers:05-007

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Cited by:
  1. Gaisser, Sandra & Memmel, Christoph & Schmidt, Rafael & Wehn, Carsten, 2009. "Time dynamic and hierarchical dependence modelling of an aggregated portfolio of trading books: a multivariate nonparametric approach," Discussion Paper Series 2: Banking and Financial Studies 2009,07, Deutsche Bundesbank, Research Centre.
  2. Pérignon, Christophe & Smith, Daniel R., 2010. "The level and quality of Value-at-Risk disclosure by commercial banks," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 362-377, February.
  3. Francis X. Diebold & Kamil Yilmaz, 2010. "Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers," Koç University-TUSIAD Economic Research Forum Working Papers 1001, Koc University-TUSIAD Economic Research Forum, revised Mar 2010.
  4. David S. Bates, 2009. "U.S. Stock Market Crash Risk, 1926-2006," NBER Working Papers 14913, National Bureau of Economic Research, Inc.
  5. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Wu, Jin, 2005. "A framework for exploring the macroeconomic determinants of systematic risk," CFS Working Paper Series 2005/04, Center for Financial Studies (CFS).
  6. Francis X. Diebold & Kamil Yilmaz, 2008. "Macroeconomic Volatility and Stock Market Volatility, Worldwide," NBER Working Papers 14269, National Bureau of Economic Research, Inc.
  7. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2007. "Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation," MPRA Paper 3963, University Library of Munich, Germany.
  8. Gregory H. Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Working Papers 07-20, Bank of Canada.
  9. Bates, David S., 2012. "U.S. stock market crash risk, 1926–2010," Journal of Financial Economics, Elsevier, vol. 105(2), pages 229-259.
  10. Krahnen, Jan Pieter & Wilde, Christian, 2006. "Risk Transfer with CDOs and Systemic Risk in Banking," CEPR Discussion Papers 5618, C.E.P.R. Discussion Papers.

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