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

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Abstract

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.

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References

  • Angabini, A., and S. Wasiuzzaman. 2010. GARCH models and the financial crisis—a study of the Malaysia stock markets. The International Journal of Applied Economics and Finance 5(3): 226–236.

    Google Scholar 

  • Bai, J., and P. Perron. 1998. Estimating and testing linear models with multiple structural changes. Econometrica 66(1): 47–78.

    Article  Google Scholar 

  • Bai, J., and P. Perron. 2003. Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18(1): 1–22.

    Article  Google Scholar 

  • Baillie, T.R., T. Bollerslev, and H.O. Mikkelsen. 1996. Modeling and pricing long-memory in stock market volatility. Journal of Econometrics 73(1): 151–184.

    Article  Google Scholar 

  • Barlow, C., S. Jayasuriya, and C.S. Tan. 1994. The World rubber industry. London: Routledge.

    Google Scholar 

  • Bhattacharya, S.N., and M. Bhattacharya. 2012. Long memory in stock returns: A study of emerging markets. Iranian Journal of Management Studies 5(2): 67–88.

    Google Scholar 

  • Bollerslev, T. 1986. Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 31(3): 307–327.

    Article  Google Scholar 

  • Burger, K., and H.P. Smith. 1997. The natural rubber market—review, analysis, policies and outlook, vol. 368. Cambridge: Woodhead Publishing Limited.

    Google Scholar 

  • Burger, K., Smit H. P., and B. Vogalvang. 2002. Exchange rates and natural rubber prices, the effect of the Asian crisis. In 10th EAAE Congress: exploring diversity in the European agri-food system. Retrieved June 25, 2014. http://ageconsearch.umn.edu/bitstream/24958/1/cp02bu31.pdf.

  • Daud, M.N. 1984. A forecasting methodology as applied to rubber prices. Journal of Rubber Institute Malaysia 3: 188–203.

    Google Scholar 

  • Daud, M.N., and M.Y. Shahabuddin. 1988. A statistical forecast of natural rubber. Journal of Natural Rubber Research 3: 69–80.

    Google Scholar 

  • Engle, R.F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4): 987–1007.

    Article  Google Scholar 

  • Glosten, R.L., J. Ravi, and D.E. Runkle. 1993. On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48(5): 1779–1801.

    Article  Google Scholar 

  • Hansen, P.R. 2005. A test for superior predictive ability. Journal of Business and Economic Statistics 23(4): 365–380.

    Article  Google Scholar 

  • Lim, J. Y. 2002. An evaluation of alternative forecasting models for natural rubber prices. Unpublished PhD dissertation, Curtin University of Technology, Australia.

  • Ludwig, R.S. 1974. Forecasting short-term saving deposit flows: an application of time series models and a regional analysis. Unpublished Master’s thesis, Massachusetts Institute of Technology.

  • Manda, K. 2010. Stock market volatility during the 2008 Financial Crisis. Retrieved September 2, 2015. http://w4.stern.nyu.edu/glucksman/docs/Manda2010.pdf.

  • McKenzie, M., and H. Mitchell. 2002. Generalized asymmetric power ARCH modelling of exchange rate volatility. Applied Financial Economics 12(8): 555–564.

    Article  Google Scholar 

  • Meyler, A., Kenny, G., and T. Quinn. 1998. Forecasting Irish inflation using ARIMA models, Central Bank of Ireland Technical Paper. Retrieved June 25, 2014. http://mpra.ub.uni-muenchen.de/11359/1/cbi_3RT98_inflationarima.pdf.

  • Ministry of Plantation Industries and Commodities (MPIC). 2013. Malaysia to launch new strategies for natural rubber industry: reports. Retrieved August 9, 2014. http://www.platts.com/RSSFeedDetailedNews/RSSFeed/Petrochemicals/7662549.

  • Natural Rubber Industry Report—BSC. 2013. Retrieved August 9, 2014. http://www.bsc.com.vn/Handlers/DownloadReport.ashx?ReportID=674295.

  • Obi, P., and S. Sil. 2013. VaR and time-varying volatility: A comparative study of three international portfolios. Managerial Finance 39(7): 625–640.

    Article  Google Scholar 

  • Shamsuddin, M.N. 1992. A short note on forecasting natural rubber prices using a MARMA. The Malaysian Journal of Agricultural Economic 9: 59–68.

    Google Scholar 

  • Shamsuddin, M.N., and F.M. Arshad. 1990. Composite model for short term forecasting for natural rubber prices. Pertanika 12(2): 283–288.

    Google Scholar 

  • Sullivan, R., A. Timmermann, and H. White. 1999. Data-snooping, technical trading rule performance, and the bootstrap. Journal of Finance 54(5): 1647–1691.

    Article  Google Scholar 

  • Tsay, R.S. 2002. Analysis of financial time series. New York: Wiley.

    Book  Google Scholar 

  • Tse, Y.K. 1998. The conditional heteroscedasticity of the yen-dollar exchange rate. Journal of Applied Economics 13(1): 49–55.

    Article  Google Scholar 

  • Wei, Y., Y.D. Wang, and D.S. Huang. 2010. Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Policy 32(6): 1477–1484.

    Google Scholar 

  • Wen, X., Y. Wei, and D. Huang. 2012. Measuring contagion between energy market and stock market during financial crisis: A copula approach. Energy Economics 34(5): 1435–1446.

    Article  Google Scholar 

  • White, H. 2000. A reality check for data snooping. Econometrica 68(5): 1097–1126.

    Article  Google Scholar 

  • Zhang, Y., and Y. He. 2005. Study of prediction model on grey relational BP neural network. In Proceedings of the fourth international conference on machine learning and cybernetics, 18–21 August 2005, Guangzhou, China.

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Acknowledgments

The authors would like to thank anonymous reviewers for their valuable suggestions and helpful comments which have greatly enhanced the quality of this paper. All remaining errors are of ours.

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Authors and Affiliations

  1. Faculty of Management, Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor Darul Ehsan, Malaysia

    Han Hwa Goh

  2. School of Business, Monash University Sunway Campus, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia

    Kim Leng Tan

  3. Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, 63100, Cyberjaya, Selangor Darul Ehsan, Malaysia

    Chia Ying Khor & Sew Lai Ng

Authors
  1. Han Hwa Goh
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  2. Kim Leng Tan
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  3. Chia Ying Khor
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  4. Sew Lai Ng
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Corresponding author

Correspondence to Han Hwa Goh.

Appendix

Appendix

Pre-crisis

See Tables 3, 4, 5, 6 and 7.

Table 3 Estimation result for rubber SMR 20 from 01 June 2000 to 29 February 2008
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Table 4 Out of sample forecasts for rubber SMR 20 from 01 June 2000 to 29 February 2008
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Table 5 SPA result (normal distributed) for rubber SMR 20 from 01 June 2000 to 29 February 2008
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Table 6 SPA result (GED distributed) for rubber SMR 20 from 01 June 2000 to 29 February 2008
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Table 7 VaR quantile forecasts for rubber SMR 20 from 01 June 2000 to 29 February 2008
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Post-crisis

See Tables 8, 9, 10, 11 and 12.

Table 8 Estimation result for rubber SMR 20 from 01 April 2009 to 31 December 2014
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Table 9 Out of sample forecasts for rubber SMR 20 from 01 April 2009 to 31December 2014
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Table 10 SPA result (normal distributed) for rubber SMR 20 from 01 April 2009 to 31 December 2014
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Table 11 SPA result (GED distributed) for rubber SMR 20 from 01 April 2009 to 31December 2014
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Table 12 VaR quantile forecasts for rubber SMR 20 from 01 April 2009 to 31 December 2014
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Goh, H.H., Tan, K.L., Khor, C.Y. et al. Volatility and Market Risk of Rubber Price in Malaysia: Pre- and Post-Global Financial Crisis. J. Quant. Econ. 14, 323–344 (2016). https://doi.org/10.1007/s40953-016-0037-4

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  • DOI: https://doi.org/10.1007/s40953-016-0037-4

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