Time Varying Risk Aversion and Asset Returns

Prices and investors' behavior are heavily influenced by risk aversion. As it is unobservable, estimating risk aversion has been challenging for a long time. This paper proposes using a Machine Learning approach (a combination of Autoencoder and Long-Short Term Memory) to estimate the time-varying risk aversion. I show that credit spread, dividend yield, next-period recession probability, and expectation of inflation change are the most influential variables for estimating risk aversion. The estimated risk aversion is positively (negatively) correlated with market uncertainty (consumption growth).<br><br>This paper extends the long run risk model by considering a stochastic risk aversion process. By solving the model numerically, I show that a time varying risk aversion helps us to get closer to matching the price moments.