Based on weekly data of the Dow Jones Country Titans, the CBT-municipal bond, spot and futures prices of commodities for the period 1992-2005, we analyze the implications for portfolio management of accounting for conditional heteroskedasticity and structural breaks in long-term volatility. In doing so, we first proceed to utilize the ICSS algorithm to detect volatility
In this article, we forecast crude oil and natural gas spot prices at a daily frequency based on two classification techniques: artificial neural networks (ANN) and support vector machines (SVM). As a benchmark, we utilize an autoregressive integrated moving average (ARIMA) specification. We evaluate out-of-sample forecast based on encompassing tests and mean-squared prediction error (MSPE).