In stark contrast to financial markets, relatively little attention has been given to modeling agricultural commodity price volatility. In recent years, numerous methodologies with various strengths have been proposed for modeling price volatility in financial markets. We propose using a mixture of normals with unique GARCH processes in each component for modeling agricultural commodity prices. While a normal mixture model is quite flexible and allows for time varying skewness and kurtosis, its biggest strength is that each component can be viewed as a different market regime and thus estimated parameters are more readily interpreted. We apply the proposed model to ten different agricultural commodity weekly cash prices. Both in-sample fit and out-of-sample forecasting tests confirm that the two-state NM-GARCH approach performs better than the traditional normal GARCH model. For each commodity, it is found that an expected negative price change corresponds to a higher volatility persistence, while an expected positive price change arises in conjunction with a greater responsiveness of volatility. A significant and statedependent inverse leverage effect is detected only for corn in a highly volatile regime that occurs with a lower probability, indicating the volatility in this regime tends to increase more following a realized price rise than a realized price drop.
Tuesday, September 20, 2016