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Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

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  • Duong, Diep
  • Swanson, Norman R.

Abstract

Many recent modeling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility predictability, some from the perspective of the usefulness of jumps in forecasting volatility. Key papers in this area include Andersen et al. (2003), Corsi (2004), Andersen et al. (2007), Corsi et al. (2008), Barndorff et al. (2010), Patton and Shephard (2011), and the references cited therein. In this paper, we review the extant literature and then present new empirical evidence on the predictive content of realized measures of jump power variations (including upside and downside risk, jump asymmetry, and truncated jump variables), constructed using instantaneous returns, i.e., |rt|q,0≤q≤6 in the spirit of Ding et al. (1993) and Ding and Granger (1996). We also present new empirical evidence on the predictive content of realized measures of truncated large jump variations, constructed using truncated squared instantaneous return, i.e., rt2×I|rt|>γ, where γ is the threshold jump size. Our prediction experiments use high frequency price returns constructed using S&P500 futures data as well as stocks in the Dow 30, and our empirical implementation involves estimating linear and nonlinear heterogeneous autoregressive realized volatility (HAR-RV) type models. We find that past “large” jump power variations help less in the prediction of future realized volatility, than past “small” jump power variations. Additionally, we find evidence that past realized signed jump power variations, which have not previously been examined in this literature, are strongly correlated with future volatility, and that past downside jump variations matter in prediction. Finally, incorporation of downside and upside jump power variations does improve predictability, albeit to a limited extent.

Suggested Citation

  • Duong, Diep & Swanson, Norman R., 2015. "Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction," Journal of Econometrics, Elsevier, vol. 187(2), pages 606-621.
  • Handle: RePEc:eee:econom:v:187:y:2015:i:2:p:606-621
    DOI: 10.1016/j.jeconom.2015.02.042
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    Citations

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    Cited by:

    1. Chang, Chia-Lin & McAleer, Michael, 2015. "Econometric analysis of financial derivatives: An overview," Journal of Econometrics, Elsevier, vol. 187(2), pages 403-407.
    2. Konstantinos Gkillas & Rangan Gupta & Mark E. Wohar, 2018. "Volatility Jumps: The Role of Geopolitical Risks," Working Papers 201805, University of Pretoria, Department of Economics.
    3. Konstantinos Gkillas & Rangan Gupta & Mark E. Wohar, 2018. "Oil Shocks and Volatility Jumps," Working Papers 201825, University of Pretoria, Department of Economics.
    4. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
    5. repec:eee:eneeco:v:72:y:2018:i:c:p:321-330 is not listed on IDEAS
    6. Chang, C-L. & McAleer, M.J., 2014. "Econometric Analysis of Financial Derivatives," Econometric Institute Research Papers EI 2015-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Documents de travail du Centre d'Economie de la Sorbonne 17006, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    8. Vortelinos, Dimitrios I. & Saha, Shrabani, 2016. "The impact of political risk on return, volatility and discontinuity: Evidence from the international stock and foreign exchange markets," Finance Research Letters, Elsevier, vol. 17(C), pages 222-226.
    9. repec:eee:eneeco:v:67:y:2017:i:c:p:136-145 is not listed on IDEAS

    More about this item

    Keywords

    Realized volatility; Jump power variations; Downside risk; Semivariances; Market microstructure; Volatility forecasts; Jump test;

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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