tailieunhanh - Volatility timing in the Vietnamese stock market

In this paper, we evaluate the economic value that arise from incorporating conditional volatility when forecasting the covariance matrix of returns for both short and long horizons in the Vietnamese stock market, using the volatility timing framework of Fleming et al. (2001). We report three main findings. First, investors are willing to pay to switch from the static to a dynamic volatility timing strategy. Second, there is negligible difference in forecast performance among short and memory volatility models. | VOLATILITY TIMING IN THE VIETNAMESE STOCK MARKET Nguyen Thi Hoang Anh1 Abstract In this paper we evaluate the economic value that arise from incorporating conditional volatility when forecasting the covariance matrix of returns for both short and long horizons in the Vietnamese stock market using the volatility timing framework of Fleming et al. 2001 . We report three main findings. First investors are willing to pay to switch from the static to a dynamic volatility timing strategy. Second there is negligible difference in forecast performance among short and memory volatility models. However the more parsimonious EWMA family models tend to produce better forecasts of the covariance matrix than those produced by the GARCH family volatility models at all investment horizons. Third when transaction costs are taken into account the gains from daily rebalanced dynamic portfolios deteriorate. However it is still worth implementing the dynamic strategies at lower rebalancing frequencies. Our results are robust to estimation error in expected returns the choice of risk aversion coefficient and estimation windows. Keywords Conditional variance-covariance matrix Volatility timing Asset allocation Economic value Vietnamese stock markets. JEL code G11 G17 C58 Date of receipt 29th Nov 2016 Date of revision 2 7th December 2016 Date pf approval 30th Dec 2016 1 Introduction Extensive research suggests that multivariate conditional volatility models produce better forecasts of the covariance matrix than those produced by the unconditional covariance matrix estimator see for example Engle and Colacito 2006 . Exploiting the predictability of volatility and covariance has become a key driver in many applied areas of finance including asset allocation asset pricing and risk management. Fleming et al. 2001 are among the first to study the economic value of predicting and timing volatility for risk averse investors in an asset allocation setting. Expected returns are treated as constant