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Modelling conditional correlations in the volatility of Asian rubber spot and futures returns

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  • Chang, Chia-Lin
  • Khamkaew, Thanchanok
  • McAleer, Michael
  • Tansuchat, Roengchai

Abstract

Asia is presently the most important market for the production and consumption of natural rubber. World prices of rubber are subject to not only to changes in demand, but also speculation regarding future markets. Japan and Singapore are the major future markets for rubber, while Thailand is one of the world's largest producers of rubber. As rubber prices are influenced by external markets, it is important to analyse the relationship between the relevant markets in Thailand, Japan and Singapore. The analysis is conducted using several alternative multivariate GARCH models. The empirical results indicate that the constant conditional correlations arising from the CCC model lie in the low to medium range. The results from the VARMA-GARCH model and the VARMA-AGARCH model suggest the presence of volatility spillovers and asymmetric effects of positive and negative return shocks on conditional volatility. Finally, the DCC model suggests that the conditional correlations can vary dramatically over time. In general, the dynamic conditional correlations in rubber spot and futures returns shocks can be independent or interdependent.

Suggested Citation

  • Chang, Chia-Lin & Khamkaew, Thanchanok & McAleer, Michael & Tansuchat, Roengchai, 2011. "Modelling conditional correlations in the volatility of Asian rubber spot and futures returns," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1482-1490.
    • Handle: RePEc:eee:matcom:v:81:y:2011:i:7:p:1482-1490
    DOI: 10.1016/j.matcom.2010.07.004
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    1. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
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    Cited by:

    1. Yen-Hsien Lee, 2014. "An international analysis of REITs and stock portfolio management based on dynamic conditional correlation models," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 28(2), pages 165-180, May.
    2. Yen-Hsien Lee & Hao Fang & Wei-Fan SU, 2014. "Effectiveness of Portfolio Diversification and the Dynamic Relationship between Stock and Currency Markets in the Emerging Eastern European and Russian Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(4), pages 296-311, September.
    3. Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2009. "Modelling Conditional Correlations for Risk Diversification in Crude Oil Markets," CIRJE F-Series CIRJE-F-640, CIRJE, Faculty of Economics, University of Tokyo.
    4. Chi-Wei Su & Lu Liu & Ran Tao & Oana-Ramona Lobonţ, 2019. "Do natural rubber price bubbles occur?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 65(2), pages 67-73.
    5. Iwatsubo, Kentaro & Watkins, Clinton, 2020. "Who influences the fundamental value of commodity futures in Japan?," International Review of Financial Analysis, Elsevier, vol. 67(C).
    6. R. Khalfaoui & M. Boutahar, 2012. "Portfolio Risk Evaluation: An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis," Working Papers halshs-00793068, HAL.
    7. Tao, Juan & Green, Christopher J., 2012. "Asymmetries, causality and correlation between FTSE100 spot and futures: A DCC-TGARCH-M analysis," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 26-37.
    8. Konstantinos N. Baltas & Robert Mann & Nicholaos C. Baltas, 2024. "The COVID-19 Pandemic and Unsustainable PPE Materials: A Correlation and Causality Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(6), pages 1651-1671, June.

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    More about this item

    Keywords

    Multivariate GARCH; Volatility spillovers; Conditional correlations; Spot returns; Futures returns;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • 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
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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