Smoothing volatility targeting

TL;DR

This paper introduces a novel variational Bayes method for smoothing volatility estimates in portfolios, reducing extreme leverage and improving risk-adjusted returns in volatility targeting strategies.

Contribution

It develops a flexible variational inference approach for stochastic volatility models that enhances portfolio stability and performance by smoothing predictive densities.

Findings

Smoothing volatility estimates reduces extreme leverage and turnover.

Improved risk-adjusted returns in volatility-managed portfolios.

Variational Bayes method outperforms existing Bayesian estimation techniques.

Abstract

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.