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High-Frequency Data, Frequency Domain Inference, And Volatility Forecasting

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Author Info
Tim Bollerslev
Jonathan H. Wright

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Abstract

Although it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high-frequency data. The method avoids using a tight parametric model by instead simply fitting a long autoregression to log-squared, squared, or absolute high-frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared, or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise. © 2001 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog

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Publisher Info
Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 83 (2001)
Issue (Month): 4 (November)
Pages: 596-602
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Handle: RePEc:tpr:restat:v:83:y:2001:i:4:p:596-602

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References listed on IDEAS
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. Muller, Ulrich A. & Dacorogna, Michel M. & Olsen, Richard B. & Pictet, Olivier V. & Schwarz, Matthias & Morgenegg, Claude, 1990. "Statistical study of foreign exchange rates, empirical evidence of a price change scaling law, and intraday analysis," Journal of Banking & Finance, Elsevier, vol. 14(6), pages 1189-1208, December. [Downloadable!] (restricted)
  2. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June. [Downloadable!] (restricted)
  3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59. [Downloadable!] (restricted)
  4. Robert F. Engle & Gary G.J. Lee, 1993. "A Permanent and Transitory Component Model of Stock Return Volatility," University of California at San Diego, Economics Working Paper Series 92-44r, Department of Economics, UC San Diego. [Downloadable!]
  5. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 5(2), pages 199-242. [Downloadable!] (restricted)
  6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April. [Downloadable!] (restricted)
  7. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348. [Downloadable!] (restricted)
  8. Diebold & Lopez, . "Modeling Volatility Dynamics," Home Pages _062, University of Pennsylvania. [Downloadable!]
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  9. Jorion, Philippe, 1995. " Predicting Volatility in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 50(2), pages 507-28, June. [Downloadable!] (restricted)
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  1. Suhejla Hoiti & Esfandiar Maasoumi & Michael McAleer & Daniel Slottje, 2005. "Measuring the Volatility in U.S. Treasury Benchmarks and Debt Instruments," DEA Working Papers 14, Universitat de les Illes Balears, Departament d'Economía Aplicada. [Downloadable!]
    Other versions:
  2. Carmen Broto & Esther Ruiz, 2002. "Estimation Methods For Stochastic Volatility Models: A Survey," Statistics and Econometrics Working Papers ws025414, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
    Other versions:
  3. Luisa Bisaglia & Silvano Bordignon & Francesco Lisi, 2003. "k -Factor GARMA models for intraday volatility forecasting," Applied Economics Letters, Taylor and Francis Journals, vol. 10(4), pages 251-254, March. [Downloadable!] (restricted)
  4. Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney. [Downloadable!]
  5. Andrew J. Patton & Kevin Sheppard, 2008. "Evaluating Volatility and Correlation Forecasts," OFRC Working Papers Series 2008fe22, Oxford Financial Research Centre. [Downloadable!]
  6. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  7. Chun-Hung Chen & Wei-Choun Yu & Eric Zivot, 2009. "Predicting Stock Volatility Using After-Hours Information," Working Papers UWEC-2009-01, University of Washington, Department of Economics. [Downloadable!]
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