Downside Risk and Portfolio Allocation for Individual Investors in China
The standard deviation is a widly used measure for financial risk management and typically assumes symmetric normal distributions. Traditionally, the normal distribution has played an important role in explaining the statistical properties of asset returns. However, a large body of literature has documented that the normal distributions cannot adequately describe such common features as skewness and excess kurtosis of asset returns. Moreover, investors are more concerned with the downside than the upside risk. Vinod (2001) proposed a new measure of risk that attempts to correct these fundamental flaws in the standard measures. He defines a downside standard deviation (DSD) that does not rely on symmetric or normally distributed asset returns. It also better reflects the preferences of risk-averse investors. Focusing only on below normal deviations, he defines a Downside Beta, a Down Sharpe ratio, and a Down Treynor index, which incorporates DSD for portfolio choice. This dissertation explores how downside standard deviation, Down Sharpe ratios, and down Treynor indices would affect portfolio choice. We focus on how downside risk measures would affect optimal allocations of portfolios by looking at the asset returns of hypothetical portfolios. A novel contribution of this dissertation is that it analyzed not only the stock market, but also the bond market and the housing market. The hypothetical portfolios fall in two different categories: one is a stock only portfolio and the other is a more diversified portfolio with stocks, bond and real estate. Our conclusions lend support to the hypothesis that the DSD theory can provide a better strategy than the traditional method for risk averse investors. As a result, our methodology and evaluation results can guide portfolio managers to better risk measurement and portfolio selection.
Hu, Wei, "Downside Risk and Portfolio Allocation for Individual Investors in China" (2012). ETD Collection for Fordham University. AAI3512405.