We develop likelihood-based tests for autocorrelation and predictability in a first order non-Gaussian and noninvertible ARMA model. Tests based on a special case of the general model, referred to as an all-pass model, are also obtained. Data generated by an all-pass process are uncorrelated...

We develop likelihood-based tests for autocorrelation and predictability in a first order non-Gaussian and noninvertible ARMA model. Tests based on a special case of the general model, referred to as an all-pass model, are also obtained. Data generated by an all-pass process are uncorrelated...

This paper presents and compares several time-series models for returns of broadbased stock indices. These models nest a nonlinear asymmetric GARCH (NGARCH) model as a special case. Some of these models are empirically motivated ad-hoc specifications others are derived from a representative...

We analyze the announcement risk premia on the US market between September 1987 and March 2023 and find that the market index exhibits average excess returns of 8.3 bps for macroeconomic announcement days. This strongly contrasts with 1.4 bps returns for non-announcement days. We further measure...

The paper investigates the ability of oil price returns, oil price shocks and oil price volatility to provide predictive information on the state (high/low risk environment) of the US stock market returns and volatility. The disaggregation of oil price shocks according to their origin allows us...

This study evaluates whether exchange traded funds (ETFs) threaten fnancial market stability by testing two hypotheses relating the growing importance of ETFs to increased market volatility and rising equity valuations. We estimate quantile cointegration models using Standard & Poor's 500 Index...

This paper investigates the impact of macroeconomic news on the dynamics of interest rates and stock returns during "low" and "high" volatility periods. These periods are determined by estimating asset dynamics using a SWARCH process. Our results suggest that securities volatility is higher...

We propose a nonparametric method to test which characteristics provide independent information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large...

Neither existing theory nor prior empirical work can tell us the impact of non-normality on required sample sizes for Student-t tests of the mean in U.S. stock returns. Prior empirical work and bounds from a modified Berry-Esseen theorem do suggest, however, that the answer should vary with...

This paper develops and estimates a Quadratic-Gaussia model of the U.S. term structure that can accommodate the rich dynamics of inflation risk premia over the 1983-2013 period by allowing for time-varying market prices of inflation risk and incorporating survey information on inflation...