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Modeling Earnings Announcement Effects in GARCH Variance

Article Quant Q&A · Author: Fortranner
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Summary

The document explains how to account for scheduled earnings announcements when modeling an asset’s conditional volatility with GARCH. It proposes adding announcement indicators as exogenous terms in the variance equation, allowing past announcements to have distinct estimated effects. If equal effects are plausible, a single indicator can replace the separate terms, reducing the number of parameters and potentially lowering estimation variance.

That simplification trades flexibility for a risk of bias. The response cautions that earnings releases can differ, so assuming an identical volatility effect may be inappropriate. The document offers a model specification and reasoning, but no empirical results, fitting procedure, or diagnostics for assessing the announcement effects. It also points to a working paper on forecasting volatility around earnings announcements without summarizing its findings.

Key ideas

  • Announcement indicators can be added as exogenous terms in a GARCH conditional variance equation.
  • Separate indicators allow each past announcement to have a distinct estimated effect.
  • A shared announcement effect reduces the number of parameters but may introduce bias.
  • The document gives a modeling suggestion but no empirical evaluation or implementation details.

Tags

EquitiesVolatilityStatistics
Full text
# GARCH models for assets with scheduled announcements


# GARCH models for assets with scheduled announcements












How do you fit a GARCH model to the returns of a stock given the dates of past earnings announcements? Volatility will tend to higher than a GARCH model would predict on the announcement day.

## Answer by Richard Hardy (score 2, accepted)

https://quant.stackexchange.com/a/73558

You can fit a GARCH model with exogenous dummy variables included in the equation for the conditional variance. E.g. if there have been $m$ announcements in the past, then $$ \sigma_{t}^2 = \omega + \alpha_1\varepsilon_{t-1}^2 + \beta_1\sigma_{t-1}^2 + \sum_{i=1}^m\gamma_i d_i $$ where $d_i$ is a dummy variable corresponding to the $i$th announcement.

(If you have reason to believe each announcement had the same effect on the conditional variance, then you can substitute $\sum_{i=1}^m\gamma_i d_i$ with $\tilde d_i$ where $\tilde d_i$ is a dummy that equals one on the announcement days and zero otherwise. This way you would reduce estimation variance at a risk of introducing some bias. But if you are modeling earnings announcements, the assumption of equal effects does not seem realistic, as the announcements are not all the same. Then the bias introduced this way might well outweigh any reduction in variance.)

## Answer by Fortranner (score 1)

https://quant.stackexchange.com/a/73644

A relevant working paper is Forecasting Market Volatility: The Role of Earnings Announcements

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.