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Modelling Conditional Correlations in the Volatility of Asian Rubber Spot and Futures Returns

Author

Listed:
  • Tanchanok Khamkaew

    (Faculty of Economics, Maejo University)

  • Roengchai Tansuchat

    (Faculty of Economics, Maejo University)

  • Chia-Lin Chang

    (Department of Applied Economics, National Chung Hsing University)

  • Michael McAleer

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo)

Abstract
Asia is presently the most important market for the production and consumption of natural rubber. World prices of rubber are not only subject to changes in demand, but also to speculation regarding future markets. Japan and Singapore are the major futures 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 of Bollerslev (1990) lie in the low to medium range. The results from the VARMA-GARCH model of Ling and McAleer (2003) and the VARMA-AGARCH model of McAleer et al. (2009) suggest the presence of volatility spillovers and asymmetric effects of positive and negative return shocks on conditional volatility. Finally, the DCC model of Engle (2002) 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

  • Tanchanok Khamkaew & Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2009. "Modelling Conditional Correlations in the Volatility of Asian Rubber Spot and Futures Returns," CARF F-Series CARF-F-175, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, revised Nov 2009.
    • Handle: RePEc:cfi:fseres:cf175
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    References listed on IDEAS

    as
    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.
    2. Nicholas Apergis & Anthony Rezitis, 2003. "Food price volatility and macroeconomic factor volatility: 'heat waves' or 'meteor showers'?," Applied Economics Letters, Taylor & Francis Journals, vol. 10(3), pages 155-160.
    3. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    4. Michael McAleer & Suhejla Hoti & Felix Chan, 2009. "Structure and Asymptotic Theory for Multivariate Asymmetric Conditional Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 28(5), pages 422-440.
    5. Jae H. Kim & Hristos Doucouliagos, 2005. "Realized Volatility and Correlation in Grain Futures Markets: Testing for Spill-Over Effects," Monash Econometrics and Business Statistics Working Papers 22/05, Monash University, Department of Econometrics and Business Statistics.
    6. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    7. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(1), pages 232-261, February.
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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. 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.
    3. Iwatsubo, Kentaro & Watkins, Clinton, 2020. "Who influences the fundamental value of commodity futures in Japan?," International Review of Financial Analysis, Elsevier, vol. 67(C).
    4. 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.
    5. Khalfaoui, R & Boutahar, M, 2012. "Portfolio risk evaluation: An approach based on dynamic conditional correlations models and wavelet multiresolution analysis," MPRA Paper 41624, University Library of Munich, Germany.
    6. 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.
    7. 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.
    8. 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.

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    More about this item

    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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