Volatility and Correlation Modeling for Sector Allocation in International Equity Markets

Abstract

Reliable estimates of volatility and correlation are crucial in asset allocation and risk management. This paper investigates Static, RiskMetrics, and Dynamic Conditional Correlation (DCC) models for estimating volatility and correlation by testing them in an asset allocation context. Optimal allocation weights for one year found using estimates from each model are carried to the subsequent year and the realized Sharpe ratio is computed to assess portfolio performance. We also study cumulative risk-adjusted returns over the entire sample period. Our findings indicate that DCC does not consistently have an advantage over the other two models, although it is optimal in certain scenarios.