Flexible variational approximations for stochastic volatility-managed portfolios

This paper investigates volatility-managed portfolios through a variational Bayes approach that smooths stochastic volatility forecasts. Our algorithm exhibits competitive performance relative to established methods both in terms of inferential accuracy and computational efficiency. Analyzing equity factors and characteristic-based portfolios, we show that smoothing reduces excess leverage and turnover, significantly improving risk-adjusted returns after transaction costs. Importantly, our smoothed stochastic volatility method achieves positive net performance while avoiding extreme negative outcomes, a combination no competing approach attains. We demonstrate that superior forecasting accuracy does not guarantee superior portfolio performance, revealing a fundamental trade-off between model precision and economic utility.