A Practical Guide to Volatility Forecasting through Calm and Storm
Christian T. Brownlees
Universitat Pompeu Fabra - Department of Economics and Business; Barcelona Graduate School of Economics (Barcelona GSE)
Robert F. Engle
New York University - Leonard N. Stern School of Business - Department of Economics; New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER)
Bryan T. Kelly
University of Chicago - Booth School of Business; National Bureau of Economic Research (NBER)
August 1, 2011
We present a volatility forecasting comparative study within the ARCH class of models. Our goal is to identify successful predictive models over multiple horizons and to investigate how predictive ability is influenced by choices for estimation window length, innovation distribution, and frequency of parameter re-estimation. Test assets include a range of domestic and international equity indices and exchange rates. We find that model rankings are insensitive to forecast horizon and suggestions for estimation best practices emerge. While our main sample spans 1990-2008, we take advantage of the near-record surge in volatility during the last half of 2008 to ask if forecasting models or best practices break down during periods of turmoil. Surprisingly, we find that volatility during the 2008 crisis was well approximated by predictions one-day ahead, and should have been within risk managers' 1% confidence intervals up to one month ahead.
Number of Pages in PDF File: 23
Keywords: Volatility, ARCH, Forecasting, Forecast Evaluation
Date posted: November 10, 2009 ; Last revised: November 15, 2013
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