This paper examines a mechanism of liquidity-preference fluctuations caused by changes in people's belief about a random liquidity shock. When observing the shock, they rationally update their belief so that the shock probability is higher; consequently they raise liquidity preference and reduce...

In a simple continuous-time model where the learning process affects the willingness to hold liquidity, we provide an intuitive explanation of business cycle asymmetry and post-crisis slow recovery. When observing a liquidity shock, individuals rationally increase their subjective probability of...

In a simple continuous-time model where the learning process affects the willingness to hold liquidity, we provide an intuitive explanation of business cycle asymmetry and post-crisis slow recovery. When observing a liquidity shock, individuals rationally increase their subjective probability of...

This paper tries to draw on the relative merits of both the jump risk models and the long-run risk models with a linkage established by Bayesian learning, in an attempt to improve both asset pricing approaches in producing a better mechanism for understanding asset prices regularities.Rather...

We consider a simple market environment in which traders with finite memory update forecasting rules at random intervals by OLS. In this context, changes in the perception of market risk can trigger volatility and bubbles. Consequently, higher degrees of risk response among traders can have a...

We explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true...

This paper studies the interaction of agents' collateral price beliefs, credit constraint and aggregate economic activity over the business cycle. Learning strengthens the role of collateral constraints in aggregate fluctuations. Under heterogeneous learning rules, numerical simulations...

The paper presents a model of housing and credit cycles featuring distorted beliefs and comovement and mutual reinforcement between house price expectations and price developments via credit expansion/contraction. Positive (negative) development in house prices fuels optimism (pessimism) and...

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...

We consider an environment in which traders with finite memory update their forecast rules at random intervals by OLS. In this context, overparameterization of the forecast rules can destabilize the learning dynamics. This instability tends to be attenuated by greater memory and less frequent...