We develop and implement asymptotic theory to conduct inference on continuous-time asset pricing models using individual equity returns sampled at high frequencies over an increasing time horizon. We study the identification and estimation of risk premia for the continuous and jump components of...

This paper describes a simple yet powerful methodology to decompose asset returns sampled at high frequency into their base components (continuous, small jumps, large jumps), determine the relative magnitude of the components, and analyze the finer characteristics of these components such as the...

We analyze an environment where the uncertainty in the equity market return and its volatility are both stochastic, and may be potentially disconnected. We solve a representative investor's optimal asset allocation and derive the resulting conditional equity premium and risk-free rate in...

Adverse shocks to stock markets propagate across the world, with a jump in one region of the world seemingly causing an increase in the likelihood of a different jump in another region of the world. To capture this effect mathematically, we introduce a model for asset return dynamics with a...

This paper examines the impact of macroeconomic and financial sector policy announcements in the United States, the United Kingdom, the euro area, and Japan during the recent crisis on interbank credit and liquidity risk premia. Announcements of interest rate cuts, liquidity support, liability...

We propose an empirical implementation of the consumption-investment problem using the martingale representation alternative to dynamic programming. Our method is based on the direct observation of state prices from options data. This greatly simplifies the investor's task of specifying the...

We develop the necessary methodology to conduct principal component analysis at high frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal components and provide the asymptotic distribution of these estimators. Empirically, we study the high frequency covariance...

We propose a model of dynamic trading where a strategic high frequency trader receives an imperfect signal about future order flows, and exploits his speed advantage to optimize his quoting policy. We determine the provision of liquidity, order cancellations, and impact on low frequency traders...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

This paper relates jumps in high frequency stock prices to firm-level, industry and macroeconomic news, in the form of machine-readable releases from Thomson Reuters News Analytics. We find that most relevant news, both idiosyncratic and systematic, lead quickly to price jumps, as market...