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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.

Suggested Citation

  • Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," PIER Working Paper Archive 05-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Handle: RePEc:pen:papers:05-007
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    File URL: http://economics.sas.upenn.edu/system/files/working-papers/05-007.pdf
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    References listed on IDEAS

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    Cited by:

    1. Li, Leon, 2017. "Testing and comparing the performance of dynamic variance and correlation models in value-at-risk estimation," The North American Journal of Economics and Finance, Elsevier, vol. 40(C), pages 116-135.
    2. Aboura, Sofiane & Chevallier, Julien, 2014. "Cross-market spillovers with ‘volatility surprise’," Review of Financial Economics, Elsevier, vol. 23(4), pages 194-207.
    3. Gregory H. Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    4. Francis X. Diebold & Kamil Yılmaz, 2007. "Macroeconomic Volatility and Stock Market Volatility,World-Wide," Koç University-TUSIAD Economic Research Forum Working Papers 0711, Koc University-TUSIAD Economic Research Forum.
    5. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    6. repec:eee:jbfina:v:80:y:2017:i:c:p:215-234 is not listed on IDEAS
    7. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    8. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Jin Wu, 2005. "A Framework for Exploring the Macroeconomic Determinants of Systematic Risk," American Economic Review, American Economic Association, vol. 95(2), pages 398-404, May.
    9. David S. Bates, 2009. "U.S. Stock Market Crash Risk, 1926-2006," NBER Working Papers 14913, National Bureau of Economic Research, Inc.
    10. 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.
    11. repec:eee:intfor:v:34:y:2018:i:1:p:45-63 is not listed on IDEAS
    12. 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.
    13. Jondeau, Eric, 2015. "The dynamics of squared returns under contemporaneous aggregation of GARCH models," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 80-93.
    14. 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.
    15. Bates, David S., 2012. "U.S. stock market crash risk, 1926–2010," Journal of Financial Economics, Elsevier, vol. 105(2), pages 229-259.
    16. repec:cpn:umkdem:v:17:y:2017:p:161-176 is not listed on IDEAS
    17. 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.
    18. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.

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    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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