Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
There is growing interest in utilizing the range data of asset prices to study the role of volatility in financial markets. In this paper, a new range-based volatility model was used to examine the economic value of volatility timing in a mean-variance framework. We compared its performance with a return-based dynamic volatility model in both in-sample and out-of-sample volatility timing strategies. For a risk-averse investor, it was shown that the predictable ability captured by the dynamic volatility models is economically significant, and that a range-based volatility model performs better than a return-based one.