How Stable are Financial Prediction Models? Evidence from US and International Stock Market Data

This study examines evidence of structural breaks in models of predictable components in stock returns related to state variables such as the lagged dividend yield, Treasury bill rate, term spread and default premium. We examine a large set of size-and-industry-sorted profolios of US stocks as well as 18 international stock market profolios and find systematic evidence of breaks in the vast majority of porfolios. The breakpoints most frequently identified in the US data are 1966, 1974, 1983, and 1990. The 1966 and 1974 breaks appear to have been driven by the T-bill rate and the default premium coefficients, while the 1983 break reflects changes in the coefficient on the T-bill rate and the term spread and the 1990 break was driven by the dividend yield and default premium coeffciencts. Our evidence also suggests that, while the size of the predictable component in stock returns has come down after the most recent break, many predictors continue to be significant. Although in-sample predictability of returns was lower in the 1990s than in some previous decades, it does not seem to hav

High-frequency returns, jumps and the mixture of normals hypothesis

Previous empirical studies find both evidence of jumps in asset prices and that returns standardized by 'realized volatility' are approximately standard normal. These findings appear to be contradictory. Using a sample of high-frequency returns for 20 heavily traded US stocks, we show how microstructure noise distorts the standard deviation and kurtosis of returns normalized using realized variance. When returns are standardized using a recently developed realized kernel estimator, the resulting series is clearly platykurtotic and the standard normal distribution is soundly rejected. Moreover, daily returns standardized using realized bipower variation, an estimator for integrated variance that is robust to the presence of jumps, are more consistent with the standard normal distribution. These results suggest that there is no empirical contradiction: jumps should be included in stock price models.