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 shifts, and incorporate that information into PGARCH models fitted to the returns series. At the next stage, we simulate returns series and compute a wavelet-based value at risk, which takes into consideration the investor’s time horizon. We repeat the same procedure for artificial data generated from distribution functions fitted to the returns by a semi-parametric procedure, which accounts for fat tails. Our estimation results show that neglecting GARCH effects and volatility shifts may lead us to overestimate financial risk at different time horizons. In addition, we conclude that investors benefit from holding commodities as their low or even negative correlation with stock indices contribute to portfolio diversification.
Keywords: value at risk., volatility shifts, wavelets