Forecasting Volatility of Asset Price

Abstract

The movements of asset prices are very complex and therefore seem to be unpredictable. However, one of the major challenges of econometrics is how to forecast such an apparently unpredictable economical series. This paper investigates forecasting methods for assets prices and determines the optimal model for each asset price; gold and crude oil are used as assets. The forecasting technique used is: ARCH family models, such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH). Analysis of the two ARCH family models were conducted, the two test parameters used are Akaike Information Criterion (AIC) and Schwartz Information Criterion (SIC). The guiding principle is, the lower the values of AIC and SICS, the better the model of the asset. Eventually, the study shows that gold has a better forecasting model and EGARCH model is the best forecasting model fit for both gold and oil. Moreover, gold has strong market value and ability to withstand stress during economic recession.