DeMiguel, Garlappi, and Uppal (2009) report that naïve diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and extreme turnover. We find that mean-variance optimization often outperforms naïve diversification, but turnover can erode its advantage in the presence of transaction costs. To address this issue, we develop 2 new methods of mean-variance portfolio selection (volatility timing and reward-to-risk timing) that deliver portfolios characterized by low turnover. These timing strategies outperform naïve diversification even in the presence of high transaction costs.

Recent work suggests that intradaily returns can be used to construct estimates of daily return volatility that are more precise than those constructed using daily returns. We measure the economic value of this “realized” volatility approach in the context of investment decisions. Our results indicate that the value of switching from daily to intradaily returns to estimate the conditional covariance matrix can be substantial. We estimate that a risk-averse investor would be willing to pay 50 to 200 basis points per year to capture the observed gains in portfolio performance. Moreover, these gains are robust to transaction costs, estimation risk regarding expected returns, and the performance measurement horizon.

We investigate the nature of volatility linkages in the stock, bond, and money markets. We develop a simple model of speculative trading that predicts strong volatility linkages in these markets due to common information, which simultaneously affects expectations across markets, and information spillover caused by cross-market hedging. To measure these linkages, we estimate a stochastic volatility representation of our trading model using GMM. The results indicate that our specification explains many of the observed characteristics of the data, and that the volatility linkages between the three markets are indeed strong. Moreover, we find that the linkages have become stronger since the 1987 stock market crash.

We find that trading‐ versus nontrading‐period variance ratios in weather‐sensitive markets are lower than those in the equity market and higher than those in the currency market. The variance ratios are also substantially lower during periods of the year when prices are most sensitive to the weather. Moreover, the comovement of returns and volatilities for related commodities is stronger during the weather‐sensitive season, largely due to stronger comovement during nontrading periods. These results are consistent with a strong link between prices and public information flow and cannot be explained by pricing errors or changes in trading activity.

Numerous studies report that standard volatility models have low explanatory power, leading some researchers to question whether these models have economic value. We examine this question by using conditional meanm‐variance analysis to assess the value of volatility timing to short‐horizon investors. We find that the volatility timing strategies outperform the unconditionally efficient static portfolios that have the same target expected return and volatility. This finding is robust to estimation risk and transaction costs.

Stocks that hedge sustained market downturns should have low expected returns, but they do not. We use ex ante firm characteristics and covariances to construct a tradable safe minus risky (SMR) portfolio that hedges market downturns out of sample. Although downturns (peaks to troughs in market index levels at the business-cycle frequency) predict significant declines in gross domestic product growth, SMR has significant positive average returns and 4-factor alphas (both around 0.8% per month). Risk-based models do not explain SMR’s returns, but mispricing does. Risky stocks are overpriced when sentiment is high, resulting in subsequent returns of -0.9% per month.