Forecasting price volatility cluster of commodity futures index by using standard deviation with dynamic data sampling based on significant interval mined from historical data

Forecasting price volatility of financial time series has been a major challenge confronting investors, speculators, businesses and also governmental organization in view of its impacts, not only on financial aspect, but also social and possibly political aspects. While businesses have been struggling in making financial decision to hedge their risk against possible future price fluctuation, governmental bodies and policy makers often caught in the midst of severe volatility. This paper presents a standard deviation approach with dynamic data sampling to forecast the price volatility cluster of a commodity futures index in Malaysia derivative market. Data sampling to derive the mean of standard deviation is taken dynamically based on the last price reversal mined from the historical data. Experiment was conducted on historical price data for the period of twenty seven years to assess the competency of this standard deviation approach with mean values through dynamic data sampling in comparison to static mean values through fixed data sampling. The outcome of the experiment reveals a promising performance demonstrating the relevancy of the proposed approach. This study constitutes a novel approach using standard deviation to quantify price equilibrium, and subsequently forecasting possible future price volatility to allow better decision making with a more reliable analysis.