This paper makes specifics contributions in the methodology of event studies. First, it develops a financial econometrics framework for understanding, measuring and testing the impact of outlier returns on the estimated parameters of stock return models. Second, it presents a maximum likelihood...

We propose to model the joint distribution of bid-ask spreads and log returns of a stock portfolio by using Autoregressive Conditional Double Poisson and GARCH processes for the marginals and vine copulas for the dependence structure. By estimating the joint multivariate distribution of both...

This paper investigates the informational content of aggregate prices in the fine arts auction market. A Mixed Data Sampling (MIDAS) modeling approach is proposed to forecast year-end art prices, using higher frequency variables related to the stock and bond markets and to art market sentiment....

The emergence of algorithmic high-frequency trading in the market for credit risk affords accurate inference of new risk measures. When combined with machine learning predictive methods, these measures forecast substantial future changes in firms' credit and equity risk premiums in...

A large set of macroeconomic variables have been suggested as equity risk premium predictors in the literature. This paper proposes a forecasting approach for the equity risk premium with two novel features. First, individual month-ahead forecasts are obtained from parsimonious threshold...

We improve upon the power of the statistical arbitrage test in Hogan, Jarrow, Teo, and Warachka (2004). Our methodology also allows for the evaluation of return anomalies under weaker assumptions. We then compare strategies based on their convergence rates to arbitrage and identify strategies...

Conventional tests of present-value models over-reject the null of no predictability. In order to better account for the intrinsic probability of detecting predictive relations by chance alone, we develop a new nonparametric Monte Carlo testing method, which does not rely on distributional...

Large-scale inference has become increasingly popular in financial economics. I explore an empirical Bayes approach to large-scale multiple testing. The proposed approach bases its inference on the posterior probability that the null is true given the observed data. It provides a convenient way...

When evaluating a trading strategy, it is routine to discount the Sharpe ratio from a historical backtest. The reason is simple: there is inevitable data mining by both the researcher and by other researchers in the past. Our paper provides a statistical framework that systematically accounts...

The present paper introduces new sign tests for testing equality of conditional distributions of two (arbitrary) adapted processes as well as for testing conditionally symmetric martingale-difference assumptions. Our analysis is based on results that demonstrate randomization over ties in sign...