We model the conditional mean and volatility of stock returns as a latent VAR process to study their contemporaneous and intertemporal relationships 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 leading to pronounced countercyclical variation in the Sharpe ratio. We document significant lead-lag correlations between the 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 these lead-lag correlations.

We examine the role of price discovery in the U.S. Treasury market through the empirical relationship between orderflow, liquidity, and the yield curve. We find that orderflow imbalances (excess buying or selling pressure) account for up to 26% of the day‐to‐day variation in yields on days without major macroeconomic announcements. The effect of orderflow on yields is permanent and strongest when liquidity is low. All of the evidence points toward an important role of price discovery in understanding the behavior of the yield curve.