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Linear time-varying regression with Copula–DCC–GARCH models for volatility

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  • Kim, Jong-Min
  • Jung, Hojin

Abstract

This paper provides a new linear time-varying regression with dynamic conditional correlation (DCC) estimated by Gaussian and Student-t copulas for forecasting financial volatility. Time-varying parameters will be estimated for nonparametric dependence by using copula functions with United States stock market data. We compare our model with Kim et al.’s (2016) linear time-varying regression (LTVR) with DCC–GARCH in the ex-post volatility forecast evaluations. Empirical study shows that our proposed volatility models are more efficient than the LTVR model. We also use the superior predictive ability and the reality check for data snooping. Evidence can be found supporting that our proposed model with copula functions provides superior forecasts for volatility over the LTVR model.

Suggested Citation

  • Kim, Jong-Min & Jung, Hojin, 2016. "Linear time-varying regression with Copula–DCC–GARCH models for volatility," Economics Letters, Elsevier, vol. 145(C), pages 262-265.
    • Handle: RePEc:eee:ecolet:v:145:y:2016:i:c:p:262-265
    DOI: 10.1016/j.econlet.2016.06.027
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    Cited by:

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    3. Indranil Ghosh & Manas K. Sanyal & R. K. Jana, 2021. "Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 503-527, February.
    4. Zhang, Junlong & Li, Yongping & You, Li & Huang, Guohe & Xu, Xiaomei & Wang, Xiaoya, 2022. "Optimizing effluent trading and risk management schemes considering dual risk aversion for an agricultural watershed," Agricultural Water Management, Elsevier, vol. 269(C).
    5. Amrouk, El Mamoun & Grosche, Stephanie-Carolin & Heckelei, Thomas, 2017. "An analysis of the interdependence between cash crop and staple food futures prices," Discussion Papers 265665, University of Bonn, Institute for Food and Resource Economics.
    6. Asongu, Simplice A. & Tchamyou, Vanessa S. & Minkoua N., Jules R. & Asongu, Ndemaze & Tchamyou, Nina P., 2018. "Fighting terrorism in Africa: Benchmarking policy harmonization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1931-1957.
    7. Lin, Saiyan & Chen, Rongda & Lv, Zhihong & Zhou, Tianqing & Jin, Chenglu, 2019. "Integrated measurement of liquidity risk and market risk of company bonds based on the optimal Copula model," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    8. Bhatia, Vaneet & Das, Debojyoti & Kumar, Surya Bhushan, 2020. "Hedging effectiveness of precious metals across frequencies: Evidence from Wavelet based Dynamic Conditional Correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    9. Iwata, Kiyonori, 2021. "Are High-Quality Earnings Useful for Voting Shareholders? Evidence from the Top Executive Director Election in Japan," Working Paper Series g-1-26, Hitotsubashi University Center for Financial Research.
    10. Kenichiro Shiraya & Kanji Suzuki & Tomohisa Yamakami, 2024. "New approaches of the DCC-GARCH residual: Application to foreign exchange rates," Papers 2411.08246, arXiv.org.
    11. Wang, Yu-Min & Lin, Che-Chun & Tsai, I-Chun, 2023. "State transformation of information spillover in asset markets and effective dynamic hedging strategies," International Review of Financial Analysis, Elsevier, vol. 89(C).
    12. Akhtaruzzaman, Md & Banerjee, Ameet Kumar & Boubaker, Sabri & Moussa, Faten, 2023. "Does green improve portfolio optimisation?," Energy Economics, Elsevier, vol. 124(C).
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    14. Chen, Yufeng & Qu, Fang, 2019. "Leverage effect and dynamics correlation between international crude oil and China’s precious metals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
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    More about this item

    Keywords

    Volatility; Time-varying parameter; Copula; GARCH; Forecasting;
    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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