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Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies

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Author Info
Eric Ghysels
Pedro Santa-Clara
Rossen Valkanov

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Abstract

We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare models across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms model based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of high-frequency (5-minute) data does not improve volatility predictions. Finally, daily lags of one to two months are sucient to capture the persistence in volatility. These findings hold both in- and out-of-sample.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 10914.

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Date of creation: Nov 2004
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Ángel León & Juan Nave & Gonzalo Rubio, 2005. "The Relationship between Risk and Expected Return in Europe," DFAEII Working Papers 200508, University of the Basque Country - Department of Foundations of Economic Analysis II, revised 04 Jul 2006. [Downloadable!]
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    Other versions:
  3. Christopher F. Baum & Mustafa Caglayan & Oleksandr Talavera, 2006. "On the Sensitivity of Firms' Investment to Cash Flow and Uncertainty," Boston College Working Papers in Economics 638, Boston College Department of Economics, revised 26 Apr 2008. [Downloadable!]
  4. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "There is a Risk-Return Tradeoff After All," NBER Working Papers 10913, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  5. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies. [Downloadable!]
    Other versions:
  6. Wolfgang Härdle & Julius Mungo, 2007. "Long Memory Persistence in the Factor of Implied Volatility Dynamics," SFB 649 Discussion Papers SFB649DP2007-027, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany. [Downloadable!]
  7. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers tecipa-304, University of Toronto, Department of Economics. [Downloadable!]
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  8. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2004. "A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1," NBER Working Papers 10447, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  9. Gregory H. Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility
    ," Working Papers 07-20, Bank of Canada. [Downloadable!]
  10. Peter Christoffersen & Stefano Mazzotta, 2004. "The information content of over-the-counter currency options," Working Paper Series 366, European Central Bank. [Downloadable!]
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  11. Robin G. de Vilder & Marcel P. Visser, 2007. "Proxies for daily volatility," PSE Working Papers 2007-11, PSE (Ecole normale supérieure). [Downloadable!]
  12. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre. [Downloadable!]
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  13. Herwartz, Helmut & Golosnoy, Vasyl, 2007. "Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance," Economics Working Papers 2007,23, Christian-Albrechts-University of Kiel, Department of Economics. [Downloadable!]
  14. Ralf Becker & Adam Clements, 2007. "Forecasting stock market volatility conditional on macroeconomic conditions," NCER Working Paper Series 18, National Centre for Econometric Research. [Downloadable!]
  15. Eric Ghysels & Jonathan H. Wright, 2006. "Forecasting professional forecasters," Finance and Economics Discussion Series 2006-10, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  16. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO. [Downloadable!]
  17. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics. [Downloadable!]
  18. Alain Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Mico Loretan, 2007. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," International Finance Discussion Papers 905, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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