The latest development in empirical Asset Pricing is the employment of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. I review and...

The influence of past stock price movements on volatilities and correlations is essential for understanding diversification and contagion in financial markets. We develop a model that makes the influence of past returns on volatilities and correlations explicit. Employing information about...

This paper examines the applicability of the bootstrap approach to test for irrelevant risk factors that are potentially useless in misspecified linear stochastic discount factor (SDF) models. In the literature, the misspecification-robust inference with useless factors is known to give rise...

Jumps and cojumps are examined in the covariance matrices of high-frequency financial markets. We propose a new method for identifying intraday volatility jumps in the diffusive covariance matrix of asset pairs. Our method avoids model misspecification errors, is able to identify multiple...

This note examines the dynamic properties of asset price bubbles obtained under the local martingale theory. We establish an explicit link between the bubble process and the quadratic variation of log asset price. Furthermore, the moments of futures price are expressible in terms of dynamic...

The relationship between the level of stock market volatility and public information flow is non-linear, resembling a bell-shaped function. Medium levels of information flow generate heightened volatility, whereas weak and strong information flow do not, regardless of whether news are negative...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

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...

The empirical finding that market movements in stock prices may be correlated with the order flow of other stocks has led to the notion of "cross-impact" and has prompted the development of multivariate models of market impact. These models are parametrized by a matrix of impact coefficients...

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...