Measuring and Testing the Impact of News on Volatility

TLDR

The paper defines a news impact curve to quantify how new information is incorporated into volatility estimates and introduces diagnostic tests that highlight asymmetry in volatility response to news. The authors compare and estimate various ARCH models—including a partially nonparametric version—using daily Japanese stock return data and apply the new diagnostic tests. Results indicate that the GJR‑GARCH model is the best parametric choice, while EGARCH captures most asymmetry but overstates the variability of conditional variance.

Abstract

ABSTRACT This paper defines the news impact curve which measures how new information is incorporated into volatility estimates. Various new and existing ARCH models including a partially nonparametric one are compared and estimated with daily Japanese stock return data. New diagnostic tests are presented which emphasize the asymmetry of the volatility response to news. Our results suggest that the model by Glosten, Jagannathan, and Runkle is the best parametric model. The EGARCH also can capture most of the asymmetry; however, there is evidence that the variability of the conditional variance implied by the EGARCH is too high.