We examine the effects of parameter uncertainty and Bayesian learning on equilibrium asset prices when all the structural parameters of the aggregate consumption and dividend growth rate processes are unknown. With realistic calibration of a parsimonious set of prior parameters, the model...

The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a Bayesian learning approach to implement real time sequential analysis. We find evidence of self-exciting jump clustering since the...

I show in a setting of a buyer and seller with the same preferences trading two related assets so as to share volatility risk that illiquidity and virtually all impediments to trade cannot be priced in the absence of excess short-selling costs. This is because the buyer values the asset at the...

This paper shows that when an investor optimally rebalances her portfolio, learning about the parameter of the return process still induces a large negative hedging demand even after observing 83 years of asset market data. An investor with a 5-year investment horizon decreases the percentage of...

Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial markets. In contrast, model-based RL is able to capture some fundamental and dynamical concepts of the environment but...

The variance risk premium represents the compensation paid to index option sellers for the risk of losses following upward movements in realized market return volatility. Common wisdom connects these spikes with elevated uncertainty on economic fundamentals. I incorporate this link within a...

Recent discussions on the volatility of agricultural prices have been drawing on factors as low short term elasticities of supply and demand, climatic risk, market uncertainty, central banks monetary policies, trade barriers, biofuel development and, finally, speculation. Much debate has aroused...

We use learning in an equilibrium model to explain the puzzling predictive power of the volatility risk premium (VRP) for option returns. In the model, a representative agent follows a rational Bayesian learning process in an economy under incomplete information with the objective of pricing...

I examine the implications of learning-based asset pricing in a model in which firms face credit constraints that depend partly on their market value. Agents learn about stock prices, but have conditionally model-consistent expectations otherwise. The model jointly matches key asset price and...