We estimate a non-linear diffusion model to capture the dynamics of the VIX index. The model is estimated under the risk neutral and the objective probability measures. The risk neutral dynamics are captured through a novel estimation method applied to futures prices. We find that non-linearity...

Regularizing Bayesian predictive regressions provides a framework for prior sensitivity analysis via the regularization path. We jointly regularize both expectations and variance-covariance matrices using a pair of shrinkage priors. Our methodology applies directly to vector autoregressions...

This paper estimates models of high frequency index futures returns using 'around the clock' 5-minute returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns,...

We propose a new class of non-linear diffusion processes for modeling financial markets data. Our non-linear diffusions are obtained as transformations of affine processes. We show that asset-pricing and estimation is possible and likelihood estimation is straightforward. We estimate a...

Dynamic equilibrium models based on present value computation imply that returns are predictable but also generate particular patterns of predictability in asset returns. I take advantage of this to construct a set of tests of Equilibrium Generated Predictability (EGP). I apply the tests to...