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Forecasting Realized Volatility: A Bayesian Model Averaging Approach

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  • Chun Liu
  • John M Maheu

Abstract

How to measure and model volatility is an important issue in finance. Recent research uses high frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive (HAR) specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility.

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Bibliographic Info

Paper provided by University of Toronto, Department of Economics in its series Working Papers with number tecipa-313.

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Length: 33 pages
Date of creation: 03 Apr 2008
Date of revision:
Handle: RePEc:tor:tecipa:tecipa-313

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Keywords: power variation; bipower variation; Gibbs sampling; model risk;

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  1. Eklund, Jana & Karlsson, Sune, 2005. "Forecast Combination and Model Averaging Using Predictive Measures," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5268, C.E.P.R. Discussion Papers.
  2. Nour Meddahi, 2003. "ARMA representation of integrated and realized variances," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 6(2), pages 335-356, December.
  3. BAUWENs, Luc & LUBRANO , Michel, 1996. "Bayesian Inference on GARCH Models using the Gibbs Sampler," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 1996027, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Tim Bollerslev & Hao Zhou, 2001. "Estimating stochastic volatility diffusion using conditional moments of integrated volatility," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2001-49, Board of Governors of the Federal Reserve System (U.S.).
  5. MEDDAHI, Nour, 2001. "A Theoretical Comparison Between Integrated and Realized Volatilies," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques 2001-26, Universite de Montreal, Departement de sciences economiques.
  6. 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, Elsevier, vol. 1(1), pages 83-106, June.
  7. Jonathan H. Wright, 2009. "Forecasting US inflation by Bayesian model averaging," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 28(2), pages 131-144.
  8. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 26(1), pages 53-90.
  9. Neil Shephard & Ole E. Barndorff-Nielsen, 2006. "Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise," Economics Series Working Papers 2006-W03, University of Oxford, Department of Economics.
  10. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Subsampling realised kernels," Journal of Econometrics, Elsevier, Elsevier, vol. 160(1), pages 204-219, January.
  11. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 79(3), pages 655-692, March.
  12. Lan Zhang & Per A. Mykland & Yacine Ait-Sahalia, 2003. "A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data," NBER Working Papers 10111, National Bureau of Economic Research, Inc.
  13. Pesaran, M Hashem & Zaffaroni, Paolo, 2005. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi-Asset Volatility Models for Risk Management," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5279, C.E.P.R. Discussion Papers.
  14. 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, Elsevier, vol. 12(3), pages 445-475, June.
  15. GORDON, Stephen, 1995. "Stochastic Trends, Deterministic Trends and Business Cycle Turning Points," Cahiers de recherche, Université Laval - Département d'économique 9503, Université Laval - Département d'économique.
  16. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 19(1), pages 3-29, September.
  17. Andreou, Elena & Ghysels, Eric, 2002. "Rolling-Sample Volatility Estimators: Some New Theoretical, Simulation, and Empirical Results," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(3), pages 363-76, July.
  18. Roel Oomen, 2004. "Properties of Bias Corrected Realized Variance Under Alternative Sampling Schemes," Working Papers, Warwick Business School, Finance Group wp04-15, Warwick Business School, Finance Group.
  19. Gary Koop & Simon Potter, 2004. "Forecasting in dynamic factor models using Bayesian model averaging," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 7(2), pages 550-565, December.
  20. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, Elsevier, vol. 131(1-2), pages 97-121.
  21. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(3), pages 326-360, Summer.
  22. Ole E. Barndorff-Nielsen & Neil Shephard, 2000. "Econometric analysis of realised volatility and its use in estimating stochastic volatility models," Economics Papers 2001-W4, Economics Group, Nuffield College, University of Oxford, revised 05 Jul 2001.
  23. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2005. "Roughing it Up: Including Jump Components in the Measurement, Modeling and Forecasting of Return Volatility," NBER Working Papers 11775, National Bureau of Economic Research, Inc.
  24. Brandt, Michael W. & Jones, Christopher S., 2006. "Volatility Forecasting With Range-Based EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 24, pages 470-486, October.
  25. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
  26. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 24, pages 127-161, April.
  27. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, Elsevier, vol. 160(1), pages 246-256, January.
  28. 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.
  29. Tim Bollerslev & Uta Kretschmer & Christian Pigorsch & George Tauchen, 2007. "A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects," CREATES Research Papers 2007-22, School of Economics and Management, University of Aarhus.
  30. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
  31. Meddahi, Nour & Mykland, Per & Shephard, Neil, 2011. "Realized Volatility," Journal of Econometrics, Elsevier, Elsevier, vol. 160(1), pages 1-1, January.
  32. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics, EconWPA 9903003, EconWPA, revised 06 Oct 2001.
  33. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, American Finance Association, vol. 44(5), pages 1115-53, December.
  34. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
  35. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2005. "Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities," Econometrica, Econometric Society, Econometric Society, vol. 73(1), pages 279-296, 01.
  36. 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, Elsevier, vol. 131(1-2), pages 59-95.
  37. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier, Elsevier.
  38. Jacobson, Tor & Karlsson, Sune, 2002. "Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach," Working Paper Series 138, Sveriges Riksbank (Central Bank of Sweden).
  39. Ole E. Barndorff-Nielsen & Neil Shephard, 2003. "Power and bipower variation with stochastic volatility and jumps," Economics Papers 2003-W17, Economics Group, Nuffield College, University of Oxford.
  40. Lars Forsberg & Eric Ghysels, 2007. "Why Do Absolute Returns Predict Volatility So Well?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(1), pages 31-67.
  41. Min, C.K. & Zellner, A., 1992. ""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates"," Papers, California Irvine - School of Social Sciences 90-92-23, California Irvine - School of Social Sciences.
  42. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics, Elsevier, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649 Elsevier.
  43. Koop, Gary & Potter, Simon M., 1998. "Bayes factors and nonlinearity: Evidence from economic time series1," Journal of Econometrics, Elsevier, Elsevier, vol. 88(2), pages 251-281, November.
  44. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 67(3), pages 473-509, March.
  45. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 456-499.
  46. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 61(1), pages 43-76, July.
  47. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, American Finance Association, vol. 57(3), pages 1047-1091, 06.
  48. Vrontos, I D & Dellaportas, P & Politis, D N, 2000. "Full Bayesian Inference for GARCH and EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 18(2), pages 187-98, April.
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Citations

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Cited by:
  1. Chew Lian Chua & Sandy Suardi & Sarantis Tsiaplias, 2011. "Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement?," Melbourne Institute Working Paper Series, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne wp2011n01, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  2. Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Working Papers, Department of Research, Ipag Business School 2014-053, Department of Research, Ipag Business School.
  3. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes & Simone Grose, 2010. "Probabilistic Forecasts of Volatility and its Risk Premia," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 22/10, Monash University, Department of Econometrics and Business Statistics.
  4. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
  5. Nima Nonejad, 2013. "A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory," CREATES Research Papers 2013-24, School of Economics and Management, University of Aarhus.
  6. Elena Andreou & Constantinos Kourouyiannis & Andros Kourtellos, 2012. "Volatility Forecast Combinations using Asymmetric Loss Functions," University of Cyprus Working Papers in Economics, University of Cyprus Department of Economics 07-2012, University of Cyprus Department of Economics.
  7. Chua, Chew Lian & Suardi, Sandy & Tsiaplias, Sarantis, 2013. "Predicting short-term interest rates using Bayesian model averaging: Evidence from weekly and high frequency data," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(3), pages 442-455.
  8. Tian Xie, 2012. "Least Squares Model Averaging by Prediction Criterion," Working Papers, Queen's University, Department of Economics 1299, Queen's University, Department of Economics.
  9. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
  10. Shawn Ni & Antonello Loddo & Dongchu Sun, 2009. "Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search," Working Papers, Department of Economics, University of Missouri 0911, Department of Economics, University of Missouri.

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