Many ways exist to measure and model financial asset volatility. In principle, as the frequency of the data increases, the quality of forecasts should improve. Yet, there is no consensus about a "true" or "best" measure of volatility. In this paper we propose to jointly consider absolute daily returns, daily high-low range and daily realized volatility to develop a forecasting model based on their conditional dynamics. As all are non-negative series, we develop a multiplicative error model that is consistent and asymptotically normal under a wide range of specifications for the error density function. The estimation results show significant interactions between the indicators. We also show that one-month-ahead forecasts match well (both in and out of sample) the market-based volatility measure provided by an average of implied volatilities of index options as measured by VIX.
We model the interrelations of equity market volatility in eight East Asian countries before, during, and after the Asian currency crisis. Using a new class of asymmetric volatility multiplicative error models based on the daily range, we find that dynamic propagation of volatility shocks occurs through a network of interdependencies, and shocks originating in Hong Kong may be amplified in their transmission throughout the system, posing greater risks to the region than shocks originating elsewhere. Although this partly explains the severity of the currency crisis, we also find evidence that parameters shifted, making the system more unstable during the crisis.
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