  We propose an alternative approach towards cost mitigation in
volatility-managed portfolios based on smoothing the predictive density of an
otherwise standard stochastic volatility model. Specifically, we develop a
novel variational Bayes estimation method that flexibly encompasses different
smoothness assumptions irrespective of the persistence of the underlying latent
state. Using a large set of equity trading strategies, we show that smoothing
volatility targeting helps to regularise the extreme leverage/turnover that
results from commonly used realised variance estimates. This has important
implications for both the risk-adjusted returns and the mean-variance
efficiency of volatility-managed portfolios, once transaction costs are
factored in. An extensive simulation study shows that our variational inference
scheme compares favourably against existing state-of-the-art Bayesian
estimation methods for stochastic volatility models.
