Forecasting stock market volatility on Bursa Malaysia Plantation Index - Lee, Hui Shan; Ng, David Ching Yat; Lau, Teck Chai; Ng, Chee Hong (Scientific & Academic Publishing, 2016)
This research applies the Bursa Malaysia Plantation Index to examine the most suitable forecasting model. The Plantation Index is studied because Malaysia is the world second largest in oil palm producer. Additionally, volatile crude palm oil price has resulted in the Plantation Index becoming more volatile as earnings of plantation companies depend heavily on crude palm oil prices. The forecasting techniques applied were random walk, moving average, simple regression and historical mean. The error in forecasting was measured by symmetric and asymmetric error statistics. The most suitable volatility forecasting technique for Bursa Malaysia Plantation Index was simple regression technique. The findings to a very large extent indicate that although there are different sophisticated forecasting technique, investor, managers and regulators could employ the less costly simple regression method to forecast oil palm related stocks and make their wise decision in investment, management and regulation in oil palm industry.