Volatility and Correlation Modeling for Sector Allocation in International Equity Markets
By Melanie Fan
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 ndings indicate that DCC does not consistently have an advantage over the other two models, although it is optimal in certain scenarios.
Advisor: Aino Levonmaa, Emma Rasiel | JEL Codes: C32, C51, G11, G15 | Tagged: Asset Allocation, Dynamic Correlation, Emerging Markets, Volatilita
Crisis Period Forecast Evaluation of the DCC-GARCH Model
By Yang Ding
The goal of this paper is to investigate the forecasting ability of the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH). We estimate the DCC’s forecasting ability relative to unconditional volatility in three equity-based crashes: the S&L Crisis, the Dot-Com Boom/Crash, and the recent Credit Crisis. The assets we use are the S&P 500 index, 10-Year US Treasury bonds, Moody’s A Industrial bonds, and the Dollar/Yen exchange rate. Our results suggest that the choice of asset pair may be a determining factor in the forecasting ability of the DCC-GARCH model.
Advisor: Aino Levonmaa, Emma Rasiel
Rebalancing, Conditional Value at Risk, and t-Copula in Asset Allocation
By Irving Wang
Traditional asset allocation methods for modeling the tradeo between risk and return do not fully reect empirical distributions. Thus, recent research has moved away from assumptions of normality to account for risk by looking at fat tails and asymmetric distributions. Other studies have also considered multiple period frameworks to include asset rebalancing. We investigate the use of rebalancing with fat tail distributions and optimizing with downside risk as a consideration. Our results verify the underperformance of traditional methods in the single period framework and also demonstrate the underperformance of traditional methods in a multiple period rebalancing
framework.
Advisor: Aino Levonmaa, Emma Rasiel
