Title: A Multiple Indicators Model for Volatility Using Intra-Daily Data
Abstract: 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.