Volatility and market risk of rubber price in Malaysia: Pre-and post-global financial crisis

HH Goh, KL Tan, CY Khor, SL Ng - Journal of Quantitative Economics, 2016 - Springer
HH Goh, KL Tan, CY Khor, SL Ng
Journal of Quantitative Economics, 2016Springer
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 …
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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