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Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis


  • Han Hwa Goh

    (Multimedia University, Persiaran Multimedia)

  • Kim Leng Tan

    (Jalan Lagoon Selatan)

  • Chia Ying Khor

    (Multimedia University, Persiaran Multimedia)

  • Sew Lai Ng

    (Multimedia University, Persiaran Multimedia)


The volatility in rubber price is a significant risk to producers, traders, consumers and others who are involved in the production and marketing of natural rubber. Such being the case, forecasting the rubber price volatility is desired to assist in decision-making in this uncertain situation. The 2008 Global Financial Crisis caused some disruptions and uncertainties in the future supply or demand for natural rubber and thus leading to higher rubber price volatility. Using ARCH-type models, this paper intends to model the dynamics of the price volatility of Standard Malaysia Rubber Grade 20 (SMR 20) in the Malaysian market before and after the Global Financial Crisis. Additionally, Value-at-Risk (VaR) approach is implemented to evaluate the market risk of SMR 20. Our empirical result denotes the existence of volatility clustering and long memory volatility in the SMR 20 market for both crisis periods. Leverage effect is also detected in the SMR 20 market where negative innovations (bad news) have a larger impact on the volatility than positive innovations (good news) for post-crisis period. When tested with Superior Predictive Ability (SPA) test, FIGARCH model is the best model across five loss functions for short- and long-term forecasts for pre-crisis period. Meanwhile, over post-crisis period, FIGARCH and GJR GARCH indicate the superior out-of-sample-forecast results and better forecasting accuracy over short- and long-term horizons, respectively. In terms of market risk, the short trading position encounters higher risk or greater losses than the long trading position at both 1 and 5 % VaR quantile for pre-crisis period. In contrast, over post-crisis period, long traders of rubber SMR 20 tend to face limited gains but unlimited losses.

Suggested Citation

  • Han Hwa Goh & Kim Leng Tan & Chia Ying Khor & Sew Lai Ng, 2016. "Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 323-344, December.
  • Handle: RePEc:spr:jqecon:v:14:y:2016:i:2:d:10.1007_s40953-016-0037-4
    DOI: 10.1007/s40953-016-0037-4

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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Ali, Muhammad Fadzli & Akber, Md. Ali & Smith, Carl & Aziz, Ammar Abdul, 2021. "The dynamics of rubber production in Malaysia: Potential impacts, challenges and proposed interventions," Forest Policy and Economics, Elsevier, vol. 127(C).
    3. Olaniyi, Oladokun Nafiu & Szulczyk, Kenneth R., 2022. "Estimating the economic impact of the white root rot disease on the Malaysian rubber plantations," Forest Policy and Economics, Elsevier, vol. 138(C).
    4. Kepulaje Abhaya Kumar & Prakash Pinto & Iqbal Thonse Hawaldar & Cristi Spulbar & Ramona Birau, 2021. "Crude oil futures to manage the price risk of natural rubber: Empirical evidence from India," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(10), pages 423-434.
    5. Negin Entezari & José Alberto Fuinhas, 2024. "Quantifying the Impact of Risk on Market Volatility and Price: Evidence from the Wholesale Electricity Market in Portugal," Sustainability, MDPI, vol. 16(7), pages 1-21, March.
    6. Ali, Muhammad Fadzli & Sulong, Siti Hawa & Julius, Kotir & Smith, Carl & Aziz, Ammar Abdul, 2022. "Using a participatory system dynamics modelling approach to inform the management of Malaysian rubber production," Agricultural Systems, Elsevier, vol. 202(C).
    7. Harshita & Shveta Singh & Surendra S. Yadav, 2018. "Changing Nature of the Value Premium in the Indian Stock Market," Vision, , vol. 22(2), pages 135-143, June.

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    More about this item


    Rubber price volatility; Malaysia; Global financial crisis; Market risk; Forecasting performance; ARCH-type models;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture


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