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Do Jumps Matter? Forecasting Multivariate Realized Volatility Allowing for Common Jumps

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  • Yin Liao
  • Heather Anderson
  • Farshid Vahid

Abstract

Realized volatility of stock returns is often decomposed into two distinct components that are attributed to continuous price variation and jumps. This paper proposes a tobit multivariate factor model for the jumps coupled with a standard multivariate factor model for the continuous sample path to jointly forecast volatility in three Chinese Mainland stocks. Out of sample forecast analysis shows that separate multivariate factor models for the two volatility processes outperform a single multivariate factor model of realized volatility, and that a single multivariate factor model of realized volatility outperforms univariate models.

Suggested Citation

  • Yin Liao & Heather Anderson & Farshid Vahid, 2010. "Do Jumps Matter? Forecasting Multivariate Realized Volatility Allowing for Common Jumps," ANU Working Papers in Economics and Econometrics 2010-520, Australian National University, College of Business and Economics, School of Economics.
    • Handle: RePEc:acb:cbeeco:2010-520
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    File URL: https://www.cbe.anu.edu.au/researchpapers/econ/wp520.pdf
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    1. Lanne, Markku, 2007. "Forecasting realized exchange rate volatility by decomposition," International Journal of Forecasting, Elsevier, vol. 23(2), pages 307-320.
    2. 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.
    3. Bollerslev, Tim & Law, Tzuo Hann & Tauchen, George, 2008. "Risk, jumps, and diversification," Journal of Econometrics, Elsevier, vol. 144(1), pages 234-256, May.
    4. 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.
    5. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 1-30.
    6. Fulvio Corsi & Stefan Mittnik & Christian Pigorsch & Uta Pigorsch, 2008. "The Volatility of Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 46-78.
    7. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    8. Ole E. Barndorff‐Nielsen & Neil 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, May.
    9. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    10. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    11. 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.
    12. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    13. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    14. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    15. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    16. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 456-499.
    17. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    18. Anderson, Heather M. & Vahid, Farshid, 2007. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 76-90, January.
    19. 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.
    20. Kim, Myung-Jig & Oh, Young-Ho & Brooks, Robert, 1994. "Are Jumps in Stock Returns Diversifiable? Evidence and Implications for Option Pricing," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 29(4), pages 609-631, December.
    21. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    22. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
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    Cited by:

    1. Yin Liao, 2012. "Does Modeling Jumps Help? A Comparison of Realized Volatility Models for Risk Prediction," CAMA Working Papers 2012-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Mohammad Abu Sayeed & Mardi Dungey & Wenying Yao, 2018. "High-frequency Characterisation of Indian Banking Stocks," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(2_suppl), pages 213-238, August.
    3. Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2017. "Inference on Self‐Exciting Jumps in Prices and Volatility Using High‐Frequency Measures," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 504-532, April.
    4. Liao, Yin & Anderson, Heather M., 2019. "Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 252-274.
    5. Jin, Xiaoye, 2015. "Volatility transmission and volatility impulse response functions among the Greater China stock markets," Journal of Asian Economics, Elsevier, vol. 39(C), pages 43-58.
    6. Liao, Yin, 2013. "The benefit of modeling jumps in realized volatility for risk prediction: Evidence from Chinese mainland stocks," Pacific-Basin Finance Journal, Elsevier, vol. 23(C), pages 25-48.
    7. Yuta Koike, 2014. "An estimator for the cumulative co-volatility of asynchronously observed semimartingales with jumps," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 460-481, June.

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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