Volatility filters for dynamic portfolio optimization

Abstract

It is well known that volatilities and correlations of international stock markets tend to increase in times of financial instability. A dynamic rebalancing scheme is proposed where the underlying market volatility functions as a timing device and portfolio is only rebalanced when the underlying volatility regime changes. In addition, the traditional Markowitz mean variance (MV) optimization can lead to an ‘inefficient frontier’ with wrong expected returns. A risk-adjusted expected return (RAER) approach is proposed where expected returns are expressed as a linear function of the risk incurred through a risk-aversion coefficient. The results show that the addition of volatility filters adds value to the portfolio performance in terms of annualized return, maximum drawdown, risk-adjusted Sharpe ratio in the whole out-of-sample period as well as all the sub-periods. Moreover, the proposed RAER approach produces most consistent performance with and without the constraint on short-selling compared to other dy...