We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time series models, sequence modeling offers a...

We introduce a new methodology to estimate the latent factors of a multivariate jump diffusion process illustrated with an application to the commodity futures term structure. Specifically, we propose a new state space form and then use a modified Kalman filter to estimate models with latent...

Exponential affine models (EAMs) are factor models popular in financial asset pricing requiring a dynamic term structure, such as for interest rates and commodity futures. When implementing EAMs it is usual to first specify the model in state space form (SSF) and then to estimate it using the...

We document characteristics-based return anomalies in a large cross-section (4,000) of crypto assets. Cryptocurrency returns exhibit momentum in the largest-cap group, reversals in other size groups, and strong crypto value and network adoption premia, from which we derive two novel factors to...