A large literature studies the predictability of stock returns by other lagged nancialvariables in a predictive regression setting. A common feature of widely used testingprocedures is a failing robustness, which may lead to misleading conclusions determinedby the particular features of a small...

We introduce a new class of flexible and tractable matrix a±ne jump-diffusions (AJD) to modelmultivariate sources of financial risk. We first provide a complete transform analysis of this model class,which opens a range of new potential applications to, e.g., multivariate option pricing with...

This paper analyzes the relation between correlation risk and the cross-section of hedge fund returns.Legal framework and investment mandate imply that hedge funds can be severely exposed tocorrelation risk: Hedge funds ability to enter long-short positions can be useful to reduce marketbeta,...

In a Lucas orchard with heterogeneous beliefs, we study the link between market-wide uncertainty, difference of opinionsand co-movement of stock returns. We show that this link plays an important role in explaining the dynamics of equilibriumvolatility and correlation risk premia. In our...

A random variable dominates another random variable with respectto the covariance order if the covariance of any two monotone increasingfunctions of this variable is smaller. We characterize completely thecovariance order, give strong sufficient conditions for it, present a numberof examples in...

We study how the interaction of agents with different beliefs about a firm’s future cash flows determines the jointbehavior of credit spreads, option implied volatilities, and stock returns. Beliefs heterogeneity influences the pricing kernelin a way that supports more realistic credit spreads...

A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to...

We introduce a new multivariate GARCH model with multivariate thresholds in conditional correlations and develop a two-step estimation procedure that is feasible in large dimensional applications. Optimal threshold functions are estimated endogenously from the data and the model conditional...

We propose a new multivariate GARCH model with Dynamic Conditional Correlations that extends previous models by admitting multivariate thresholds in conditional volatilitiesand correlations. The model estimation is feasible in large dimensions and the positive definiteness of the conditional...