Volatility filters for dynamic portfolio optimization

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 dynamic rebalancing approaches and a constant equally weighted portfolio.