No abstract

We compare the predictive ability and economic value of implied, realized, and GARCH volatility models for 13 equity indices from 10 countries. Model ranking is similar across countries, but varies with the forecast horizon. At the daily horizon, the Heterogeneous Autoregressive model offers the most accurate predictions, whereas an implied volatility model that corrects for the volatility risk premium is superior at the monthly horizon. Widely used GARCH models have inferior performance in almost all cases considered. All methods perform significantly worse over the 2008-09 crisis period. Finally, implied volatility offers significant improvements against historical methods for international portfolio diversification.

Investors often adopt mean-variance efficient portfolios for achieving superior risk-adjusted returns. However, such portfolios are sensitive to estimation errors, which affect portfolio performance. To understand the impact of estimation errors, I develop simple and intuitive formulas of the squared Sharpe ratio that investors should expect from estimated efficient portfolios. The new formulas show that the expected squared Sharpe ratio is a function of the length of the available data, the number of assets and the maximum attainable Sharpe ratio. My results enable the portfolio manager to assess the value of efficient portfolios as investment vehicles, given the investment environment.

No abstract

No abstract

I jointly treat two critical issues in the application of mean-variance portfolios, i.e., estimation risk and portfolio instability. I find that theory-based portfolio strategies known to outperform naive diversification (1/ N) in the absence of transaction costs, heavily underperform it under transaction costs. This is because they are highly unstable over time. I propose a generic method to stabilize any given portfolio strategy while maintaining or improving its efficiency. My empirical analysis confirms that the new method leads to stable and efficient portfolios that offer equal or lower turnover than 1/ N and larger Sharpe ratio, even under high transaction costs.

No abstract

We compare the performance of popular covariance forecasting models in the context of a portfolio of major European equity indices. We find that models based on high-frequency data offer a clear advantage in terms of statistical accuracy. They also yield more theoretically consistent predictions from an empirical asset pricing perspective, and, lead to superior out-of-sample portfolio performance. Overall, a parsimonious Vector Heterogeneous Autoregressive (VHAR) model that involves lagged daily, weekly and monthly realised covariances achieves the best performance out of the competing models. A promising new simple hybrid covariance estimator is developed that exploits option-implied information and high-frequency data while adjusting for the volatility risk premium. Relative model performance does not change during the global financial crisis, or, if a different forecast horizon, or, intraday sampling frequency is employed. Finally, our evidence remains robust when we consider an alternative sample of U.S. stocks.