Working Paper: NBER ID: w10117
Authors: 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.
Keywords: Volatility; Forecasting; GARCH; High-frequency data
JEL Codes: C22; C32; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| absolute daily returns (G12) | realized volatility (G17) |
| daily high-low range (Y10) | realized volatility (G17) |
| realized volatility (G17) | absolute daily returns (G12) |
| realized volatility (G17) | daily high-low range (Y10) |
| daily high-low range (Y10) | absolute daily returns (G12) |
| absolute daily returns (G12) | daily high-low range (Y10) |