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Volatility Forecasting Using Explanatory Variables and Focused Selection Criteria

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Author Info
Christian T. Brownlees () (Università degli Studi di Firenze, Dipartimento di Statistica)
Giampiero Gallo () (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti")

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Abstract

This paper assesses the performance of volatility forecasting using focused selection and combination strategies to include relevant explanatory variables in the forecasting model. The focused selection/combination strategies consist of picking up the model that minimizes the estimated risk (e.g. MSE) of a given smooth function of the parameters of interest to the forecaster. The proposed focused methods are compared with other strategies, including the well established AIC and BIC. The methodology is applied to a daily recursive 1--step ahead value--at--risk (VaR) forecasting exercise of 4 widely traded New York Stock Exchange stocks. Results show that VaR forecasts can significantly be improved upon using focused forecast strategies for the selection of relevant exogenous information. The set of explanatory variables that helps improving prediction is stock dependent. Traditional information criteria do not appear to be helpful in suggesting the inclusion of explanatory variables that actually improve prediction significantly. In line with recent theoretical findings, the predictive performance of the BIC appears to be modest.

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Paper provided by Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti" in its series Econometrics Working Papers Archive with number wp2007_04.

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Date of creation: May 2007
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Handle: RePEc:fir:econom:wp2007_04

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Related research
Keywords: Forecasting; Shrinkage Estimation; FIC; MEM; GARCH; ACD;

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  1. Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
  2. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March. [Downloadable!] (restricted)
  3. Lamoureux, Christopher G & Lastrapes, William D, 1994. "Endogenous Trading Volume and Momentum in Stock-Return Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 253-60, April.
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  5. Yacine Aït-Sahalia, 2005. "How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 18(2), pages 351-416. [Downloadable!] (restricted)
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  6. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-29, March. [Downloadable!] (restricted)
  7. 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. [Downloadable!]
  8. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February. [Downloadable!] (restricted)
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  9. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889. [Downloadable!]
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  10. Christian T. Brownlees & Giampiero Gallo, 2006. "Financial Econometric Analysis at Ultra–High Frequency: Data Handling Concerns," Econometrics Working Papers Archive wp2006_03, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
    Other versions:
  11. Gita Persand & Chris Brooks, 2003. "Volatility forecasting for risk management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 1-22. [Downloadable!]
  12. Pong, Shiuyan & Shackleton, Mark B. & Taylor, Stephen J. & Xu, Xinzhong, 2004. "Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2541-2563, October. [Downloadable!] (restricted)
  13. Giampiero M. Gallo, 2001. "Modelling the Impact of Overnight Surprises on Intra-daily Volatility," Econometrics Working Papers Archive wp2001_02, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
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  14. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95. [Downloadable!] (restricted)
  15. Giot,Pierre & Laurent,Sebastien, 2001. "Modelling daily value-at-risk using realized volatility and arch type models," Research Memoranda 014, Maastricht : METEOR, Maastricht Research School of Economics of Technology and Organization. [Downloadable!]
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  16. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June. [Downloadable!] (restricted)
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  17. Ole E. Barndorff-Nielsen & Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280. [Downloadable!] (restricted)
    Other versions:
  18. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March. [Downloadable!] (restricted)
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  19. Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27. [Downloadable!] (restricted)
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  20. Christian T. Brownlees & Giampiero Gallo, 2007. "Flexible Time Series Forecasting Using Shrinkage Techniques and Focused Selection Criteria," Econometrics Working Papers Archive wp2007_02, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
  21. Susan Thomas & Mandira Sarma & Ajay Shah, 2003. "Selection of Value-at-Risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 337-358. [Downloadable!]
  22. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
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  24. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
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  25. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446. [Downloadable!]
  26. Jose A. Lopez, 1999. "Methods for evaluating value-at-risk estimates," Economic Review, Federal Reserve Bank of San Francisco, pages 3-17. [Downloadable!]
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  27. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July. [Downloadable!] (restricted)
  28. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July. [Downloadable!] (restricted)
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