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Evaluating interval forecasts of high-frequency financial data

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  • Michael P. Clements

    (Department of Economics, University of Warwick, Coventry CV4 7AL, UK)

  • Nick Taylor

    (Department of Accounting and Finance, Cardiff University, UK)

Abstract

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression-based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated. Copyright © 2003 John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 18 (2003)
Issue (Month): 4 ()
Pages: 445-456

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Handle: RePEc:jae:japmet:v:18:y:2003:i:4:p:445-456

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  1. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  2. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
  3. Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
  4. Wood, Robert A & McInish, Thomas H & Ord, J Keith, 1985. " An Investigation of Transactions Data for NYSE Stocks," Journal of Finance, American Finance Association, vol. 40(3), pages 723-39, July.
  5. Jose A. Lopez, 1996. "Regulatory Evaluation of Value-at-Risk Models," Center for Financial Institutions Working Papers 96-51, Wharton School Center for Financial Institutions, University of Pennsylvania.
  6. Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-080, New York University, Leonard N. Stern School of Business-.
  7. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-35, April.
  8. McQueen, Grant & Thorley, Steven, 1993. "Asymmetric business cycle turning points," Journal of Monetary Economics, Elsevier, vol. 31(3), pages 341-362, June.
  9. Bollerslev, T. & Ghysels, E., 1994. "Periodic Autoregressive Conditional Heteroskedasticity," Cahiers de recherche 9408, Universite de Montreal, Departement de sciences economiques.
  10. Granger, C. W. J. & White, Halbert & Kamstra, Mark, 1989. "Interval forecasting : An analysis based upon ARCH-quantile estimators," Journal of Econometrics, Elsevier, vol. 40(1), pages 87-96, January.
  11. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038 Elsevier.
  12. Baillie, Richard T. & Bollerslev, Tim, 1992. "Prediction in dynamic models with time-dependent conditional variances," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 91-113.
  13. Harvey, Campbell R & Huang, Roger D, 1991. "Volatility in the Foreign Currency Futures Market," Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 543-69.
  14. Werner, Ingrid M & Kleidon, Allan W, 1996. "U.K. and U.S. Trading of British Cross-Listed Stocks: An Intraday Analysis of Market Integration," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 619-64.
  15. Tim Bollerslev & Jeffrey M. Wooldridge, 1988. "Quasi-Maximum Likelihood Estimation of Dynamic Models with Time-Varying Covariances," Working papers 505, Massachusetts Institute of Technology (MIT), Department of Economics.
  16. Baillie, Richard T & Bollerslev, Tim, 2002. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 60-68, January.
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Citations

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Cited by:
  1. Wallis, Kenneth F., 2002. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," Royal Economic Society Annual Conference 2002 181, Royal Economic Society.
  2. Taylor, Nicholas, 2007. "A note on the importance of overnight information in risk management models," Journal of Banking & Finance, Elsevier, vol. 31(1), pages 161-180, January.
  3. Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," HSC Research Reports HSC/14/09, Hugo Steinhaus Center, Wroclaw University of Technology.
  4. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.
  5. Tao Hong & Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts," HSC Research Reports HSC/14/10, Hugo Steinhaus Center, Wroclaw University of Technology.
  6. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
  7. Tsyplakov, Alexander, 2011. "Evaluating density forecasts: a comment," MPRA Paper 31184, University Library of Munich, Germany.
  8. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Society for Computational Economics, vol. 40(3), pages 245-264, October.
  9. Li, Yushu & Andersson, Jonas, 2014. "A Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting," Discussion Papers 2014/12, Department of Business and Management Science, Norwegian School of Economics.

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