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Range-based multivariate volatility model with double smooth transition in conditional correlation

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  • Chou, Ray Yeutien
  • Cai, Yijie

Abstract

This paper proposes a multivariate model named Double Smooth Transition Conditional Correlation Conditional Autoregressive Range (DSTCC-CARR for short). Determined by two transition variables, the correlations smoothly transit from one state to another. Together with the DSTCC-GARCH model, the model is employed to investigate the interdependence between Hong Kong's and international stock markets. It is proved by the empirical analysis that the DSTCC-CARR model is more credible and efficient than the DSTCC-GARCH model. Linkages among Hong Kong's and other world's markets captured by these two models are testified to be consistent with history, and have meaningful interpretations.

Suggested Citation

  • Chou, Ray Yeutien & Cai, Yijie, 2009. "Range-based multivariate volatility model with double smooth transition in conditional correlation," Global Finance Journal, Elsevier, vol. 20(2), pages 137-152.
    • Handle: RePEc:eee:glofin:v:20:y:2009:i:2:p:137-152
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    Cited by:

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    2. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
    3. Datta, Susanta & Hatekar, Neeraj, 2022. "Range Volatility Spillover across Sectoral Stock Indices during COVID-19 Pandemic: Evidence from Indian Stock Market," MPRA Paper 117285, University Library of Munich, Germany.
    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, August.
    6. Piotr Fiszeder, 2018. "Low and high prices can improve covariance forecasts: The evidence based on currency rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(6), pages 641-649, September.
    7. Fiszeder, Piotr & Fałdziński, Marcin, 2019. "Improving forecasts with the co-range dynamic conditional correlation model," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    8. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.

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