The theory of conditional copulas provides a means of constructing flexible multivariate density models, allowing for time-varying conditional densities of each individual variable, and for time-varying conditional dependence between the variables. Further, the use of copulas in constructing...

This paper proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence,...

We propose a new method to capture changes in hedge funds' exposures to risk factors, exploiting information from relatively high frequency conditioning variables. Using a consolidated database of nearly 15,000 individual hedge funds between 1994 and 2009, we find substantial evidence that hedge...

Using a variety of different definitions of “neutrality,” this study presents significant evidence against the neutrality to market risk of hedge funds in a range of style categories. I generalize standard definitions of “market neutrality,” and propose five different neutrality...

This paper proposes a new class of copula-based dynamic models for high dimension conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high dimension covariance matrices...

We study the accuracy of a wide variety of estimators of asset price variation constructed from high-frequency data (so-called “realized measures”), and compare them with a simple “realized variance” (RV) estimator. In total, we consider almost 400 different estimators, applied to 11...

We propose a new method to model hedge fund risk exposures using relatively high frequency conditioning variables. In a large sample of funds, we find substantial evidence that hedge fund risk exposures vary across and within months, and that capturing within-month variation is more important...