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Practical Volatility and Correlation Modeling for Financial Market Risk Management

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  • Torben G. Andersen
  • Tim Bollerslev
  • Peter F. Christoffersen
  • Francis X. Diebold

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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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11069.

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Date of creation: Jan 2005
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Publication status: published as Practical Volatility and Correlation Modeling for Financial Market Risk Management , Torben G. Andersen, Tim Bollerslev, Peter Christoffersen, Francis X. Diebold. in The Risks of Financial Institutions , Carey and Stulz. 2006
Handle: RePEc:nbr:nberwo:11069

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Cited by:
  1. 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, Koc University-TUSIAD Economic Research Forum 1001, Koc University-TUSIAD Economic Research Forum, revised Mar 2010.
  2. Bates, David S., 2012. "U.S. stock market crash risk, 1926–2010," Journal of Financial Economics, Elsevier, Elsevier, vol. 105(2), pages 229-259.
  3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin (Ginger) Wu, 2005. "A Framework for Exploring the Macroeconomic Determinants of Systematic Risk," PIER Working Paper Archive 05-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  4. 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.
  5. Francis X. Diebold & Kamil Yilmaz, 2008. "Macroeconomic Volatility and Stock Market Volatility, Worldwide," NBER Working Papers 14269, National Bureau of Economic Research, Inc.
  6. Krahnen, Jan Pieter & Wilde, Christian, 2006. "Risk Transfer with CDOs and Systemic Risk in Banking," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5618, C.E.P.R. Discussion Papers.
  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. Pérignon, Christophe & Smith, Daniel R., 2010. "The level and quality of Value-at-Risk disclosure by commercial banks," Journal of Banking & Finance, Elsevier, Elsevier, vol. 34(2), pages 362-377, February.
  9. Gregory H. Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Working Papers, Bank of Canada 07-20, Bank of Canada.
  10. David S. Bates, 2009. "U.S. Stock Market Crash Risk, 1926-2006," NBER Working Papers 14913, National Bureau of Economic Research, Inc.

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