I analyze the cross-section of covariance risk for individual stocks using a new type of multivariate volatility model in which firm characteristics serve as time-varying loadings on fundamental factors. The evidence points to strong linkages between firm characteristics and covariance risk, and...

This paper analyzes the relationship between debt and the production decision of companies active in the exploration and production of oil and gas in the United States. Over the last couple of years, the development and application of innovative extraction methods, like hydraulic fracturing and...

This paper explores the contagious propagation of jumps among international stock market indices by exploiting a rich panel of stock and options data. We propose a multivariate option pricing model designed to allow for, but not superimpose, time and space amplification of jumps in option...

In this article, we link the realized accuracy of predictive panels to changes in distributions that occur between the training (in-sample) phase and the testing (out-of-sample) phase. We obtain polynomial upper bounds for the loss of accuracy between training and testing. We model covariate...

We develop a network-based vector autoregressive approach to uncover the interactions amongfinancial assets by integrating multiple realized measures based on high-frequency data. Undera restricted parameter structure, our approach allows the capture of cross-sectional and time ependencies...

This paper develops and presents the prior adaptive group lasso for generalized linear models. The prior adaptive group lasso is an extension of the prior lasso developed by Jiang, He, and Zhang (2016), which allows for the use of existing information from previous or similar studies in the...

We present a novel approach to analyzing stock return predictability that accommodates (i) arbitrary predictor persistence, (ii) panels with common factors, (iii) multiple predictors, (iv) short- and long-horizon analysis, and relies on standard inference from least-squares estimation of a...

We build on Fackler and King (1990) and propose a general calibration model for implied risk neutral densities. Our model allows for the joint calibration of a set of densities at different maturities and dates. The model is a Bayesian dynamic beta Markov random field which allows for possible...

We study market pricing of fundamentals at the Shanghai Stock Exchange, incorporating possible irrational pricing behavior with adaptive expectation. Using panel data of listed stocks to overcome the limited information in aggregate time series data, we estimated key parameters of the price...

I use eight different metrics as separate objective and systematic measures of the efficiency of the market for a stock. I develop a seven-equation (six- for non-Nasdaq stocks) structural model with market efficiency as a function of exogenous factors (transaction costs & constraints, short...