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Robust volatility forecasts in the presence of structural breaks

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Author Info

  • Elena Andreou
  • Eric Ghysels
  • Constantinos Kourouyiannis

Abstract

Financial time series often undergo periods of structural change that yield biased estimates or forecasts of volatility and thereby risk management measures. We show that in the context of GARCH diffusion models ignoring structural breaks in the leverage coefficient and the constant can lead to biased and inefficient AR-RV and GARCH-type volatility estimates. Similarly, we find that volatility forecasts based on AR-RV and GARCH-type models that take into account structural breaks by estimating the parameters only in the post-break period, significantly outperform those that ignore them. Hence, we propose a Flexible Forecast Combination method that takes into account not only information from different volatility models, but from different subsamples as well. This methods consists of two main steps: First, it splits the estimation period in subsamples based on estimated structural breaks detected by a change-point test. Second, it forecasts volatility weighting information from all subsamples by minimizing a particular loss function, such as the Square Error and QLIKE. An empirical application using the S&P 500 Index shows that our approach performs better, especially in periods of high volatility, compared to a large set of individual volatility models and simple averaging methods as well as Forecast Combinations under Regime Switching.

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File URL: http://papers.econ.ucy.ac.cy/RePEc/papers/08-12.pdf
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Bibliographic Info

Paper provided by University of Cyprus Department of Economics in its series University of Cyprus Working Papers in Economics with number 08-2012.

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Length: 75 pages
Date of creation: May 2012
Date of revision:
Handle: RePEc:ucy:cypeua:08-2012

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Web page: http://www.econ.ucy.ac.cy

Related research

Keywords: forecast combinations; volatility; structural breaks;

This paper has been announced in the following NEP Reports:

References

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  1. Elena Andreou & Eric Ghysels, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," CIRANO Working Papers 2004s-25, CIRANO.
  2. Hansen, Bruce E., 2009. "Averaging Estimators For Regressions With A Possible Structural Break," Econometric Theory, Cambridge University Press, vol. 25(06), pages 1498-1514, December.
  3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  4. Foster, Dean P & Nelson, Daniel B, 1996. "Continuous Record Asymptotics for Rolling Sample Variance Estimators," Econometrica, Econometric Society, vol. 64(1), pages 139-74, January.
  5. Graham Elliott & Allan Timmermann, 2005. "Optimal Forecast Combination Under Regime Switching ," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(4), pages 1081-1102, November.
  6. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
  7. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers.
  8. Massimo Guidolin & Allan Timmerman, 2007. "Forecasts of U.S. short-term interest rates: a flexible forecast combination approach," Working Papers 2005-059, Federal Reserve Bank of St. Louis.
  9. Elena Andreou & Eric Ghysels, 2000. "Rolling-Sample Volatility Estimators: Some New Theoretical, Simulation and Empirical Results," CIRANO Working Papers 2000s-19, CIRANO.
  10. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
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  12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
  13. Elliott, Graham & Timmermann, Allan, 2002. "Optimal Forecast Combination Under General Loss Functions and Forecast Error Distributions," University of California at San Diego, Economics Working Paper Series qt15r9t2q2, Department of Economics, UC San Diego.
  14. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
  15. Pesaran, M Hashem & Timmermann, Allan G, 2004. "Small Sample Properties of Forecasts From Autoregressive Models Under Structural Breaks," CEPR Discussion Papers 4401, C.E.P.R. Discussion Papers.
  16. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
  17. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2005. "Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities," Econometrica, Econometric Society, vol. 73(1), pages 279-296, 01.
  18. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
  19. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
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