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A Multiple Indicators Model for Volatility Using Intra-Daily Data

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  • Robert F. Engle
  • Giampiero M. Gallo

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.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 10117.

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Date of creation: Nov 2003
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Publication status: published as Engle, Robert F. and Gaimpiero M. Gallo. "A Multiple Indicators Model For Volatility Using Intra-Daily Data," Journal of Econometrics, 2006, v131(1-2,Mar-Apr), 3-27.
Handle: RePEc:nbr:nberwo:10117

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