Working Paper: NBER ID: w9056
Authors: Michael W. Brandt; Qiang Kang
Abstract: We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework and without relying on exogenous predictors. We find a strong and robust negative correlation between the innovations to the conditional moments that leads to pronounced counter-cyclical variation in the Sharpe ratio. We document significant lead-lag correlations between the conditional moments that also appear related to business cycles. Finally, we show that although the conditional correlation between the mean and volatility is negative, the unconditional correlation is positive due to the lead-lag correlations.
Keywords: No keywords provided
JEL Codes: G10; G12; C13; C23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| increases in volatility (E32) | decreases in expected returns (G17) |
| changes in volatility (C58) | changes in the conditional mean (C22) |
| conditional correlation between the mean and volatility is negative (C10) | unconditional correlation is positive (C10) |