The standard mean-variance trade-off, as well as many asset pricing models and predictions such as CAPM, assume investors care equally about up- and downward movements. However, studies dating back to at least Roy (1952) and Markowitz (1959) have argued that investors primarily care about negative returns and downside risk. Indeed, arguments rooted in prospect theory and loss aversion suggest that up and downside risks are not treated the same by investors. This has large implications for return volatility and asset prices.
This realization has led to the development of `good’ and `bad’ volatilities, usually based on so-called semivariances, the volatility stemming from negative and positive returns only. Most problems in finance are however inherently multivariate in nature, suggesting the need for a similar definition for covariances, which play a much more prominent role in finance, related to non-diversifiable risk, and hedging.
The speaker, Rogier Quaedvlieg, will provide a selective overview of recent developments related to measuring, modelling and pricing `good’ and `bad’ volatilities based on realized semicovariance measures. The talk builds on two recent papers; Realized Semicovariances (Econometrica, 2020) and Realized Semibetas (Journal of Financial Economics, 2021). These multivariate measures decompose the covariance matrix into four terms dependent on the sign of the underlying returns. This decomposition allows for better volatility forecasting, and a deeper understanding of the pricing of systematic market risk.