I assess time-series return predictability using a weighted least squares estimator that is around 25% more efficient than ordinary least squares (OLS) because it incorporates timevarying volatility into its point estimates. Traditional predictors, such as the dividend yield, perform better in-and out-of-sample when using my estimator, indicating the insignificant OLS estimates may be false negatives driven by a lack of power. Some newer predictors, such as the variance risk premium and the president's political party, are insignificant when using my estimator, indicating the significant OLS estimates may be false positives driven by a few periods with high expected volatility.

Portfolio performance measures using holdings data are panel regressions. The returns of a fund’s stocks are regressed on its lagged portfolio weights. Stock fixed effects isolate average performance from time-series predictive ability. Control variables condition for fund performance on the characteristics of the stocks held. The long-term performance of average holdings drives some of the classical measures, while predictive ability drives others. A “buy-and-hold drift,” where portfolio weights increase over time in the higher alpha stocks, affects performance measures. Investor flows respond to average performance net of the buy-and-hold drift.

This paper shows that Edgeworth expansions for option valuation are equivalent to approximating option payoffs using Hermite polynomials. Consequently, the value of an option is the value of an infinite series of replicating polynomials. The resultant formulas express option values in terms of skewness, kurtosis, and higher moments. Unfortunately, the Hermite series diverges for fat-tailed models, so we provide alternative moment-based formulas. These formulas are a computationally efficient alternative to Fourier transform valuation and can value options even when the characteristic function is unknown. Applications include the first convergent solution for Hull and White’s stochastic volatility model.