TY - JOUR AU - Andersen,Torben G. AU - Bollerslev,Tim AU - Christoffersen,Peter F. AU - Diebold,Francis X. TI - Practical Volatility and Correlation Modeling for Financial Market Risk Management JF - National Bureau of Economic Research Working Paper Series VL - No. 11069 PY - 2005 Y2 - January 2005 UR - http://www.nber.org/papers/w11069 L1 - http://www.nber.org/papers/w11069.pdf N1 - Author contact info: Torben G. Andersen Kellogg School of Management Northwestern University 2001 Sheridan Road Evanston, IL 60208 Tel: 847/467-1285 Fax: 847/491-5719 E-Mail: t-andersen@kellogg.northwestern.edu Tim Bollerslev Department of Economics Duke University Box 90097 Durham, NC 27708-0097 Tel: 919/660-1846 Fax: 919/684-8974 E-Mail: boller@econ.duke.edu Peter Christoffersen Professor of Finance Rotman School of Management University of Toronto 105 St. George Street 447 Toronto, ON, M5S 3E6, Canada Tel: 416-946-5511 E-Mail: Peter.Christoffersen@rotman.utoronto.ca Francis X. Diebold Department of Economics University of Pennsylvania 3718 Locust Walk Philadelphia, PA 19104-6297 Tel: 215/898-1507 Fax: 212/573-4217 E-Mail: fdiebold@sas.upenn.edu M1 - published as Torben G. Andersen, Tim Bollerslev, Peter Christoffersen, Francis X. Diebold. "Practical Volatility and Correlation Modeling for Financial Market Risk Management ," in Mark Carey and René M. Stulz, editors, "The Risks of Financial Institutions" University of Chicago Press (2006) AB - 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. ER -