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The Impact of Jumps and Leverage in Forecasting Co-Volatility

Author

Listed:
  • Manabu Asai

    (Soka University, Japan)

  • Michael McAleer

    (National Tsing Hua University, Taiwan, Erasmus University Rotterdam, the Netherlands, Complutense University of Madrid, Spain)

Abstract

The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013)such that the estimated matrix is positive definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. Empirical results for three stocks traded on the New York Stock Exchange indicate that the co-jumps of two assets have a significant impact on future co-volatility, but that the impact is negligible for forecasting weekly and monthly horizons.

Suggested Citation

  • Manabu Asai & Michael McAleer, 2015. "The Impact of Jumps and Leverage in Forecasting Co-Volatility," Tinbergen Institute Discussion Papers 15-018/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20150018
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    2. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Energies, MDPI, vol. 12(17), pages 1-17, September.
    3. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
    4. Rangan Gupta & Christian Pierdzioch, 2024. "Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices," Mathematics, MDPI, vol. 12(18), pages 1-26, September.
    5. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2017. "Realized stochastic volatility with general asymmetry and long memory," Journal of Econometrics, Elsevier, vol. 199(2), pages 202-212.
    6. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    7. Yaojie Zhang & Yu Wei & Li Liu, 2019. "Improving forecasting performance of realized covariance with extensions of HAR-RCOV model: statistical significance and economic value," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1425-1438, September.
    8. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    9. Manabu Asai & Michael McAleer, 2022. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 103-123, January.
    10. Asai, Manabu & Chang, Chia-Lin & McAleer, Michael, 2022. "Realized matrix-exponential stochastic volatility with asymmetry, long memory and higher-moment spillovers," Journal of Econometrics, Elsevier, vol. 227(1), pages 285-304.
    11. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
    12. Qu, Hui & Zhang, Yi, 2022. "Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies," Economic Modelling, Elsevier, vol. 106(C).

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    More about this item

    Keywords

    Co-Volatility; Forecasting; Jump; Leverage Effects; Realized Covariance; Threshold;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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