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...

This paper studies the optimal timing to liquidate credit derivatives in a general intensity-based credit risk model under stochastic interest rate. We incorporate the potential price discrepancy between the market and investors, which is characterized by risk-neutral valuation under different...

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...