A forecast comparison of volatility models: does anything beat a GARCH(1,1)?

TLDR

The study compares 330 ARCH‑type models out‑of‑sample on exchange‑rate and IBM return data using DM, SPA, and RC tests to assess conditional‑variance forecasting. The results show that GARCH(1,1) is not outperformed on exchange rates but is outperformed by leverage‑effect models on IBM returns, and that the RC test lacks power to discriminate model quality. © 2005 John Wiley & Sons, Ltd.

Abstract

Abstract We compare 330 ARCH‐type models in terms of their ability to describe the conditional variance. The models are compared out‐of‐sample using DM–$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in our analysis of exchange rates, whereas the GARCH(1,1) is clearly inferior to models that can accommodate a leverage effect in our analysis of IBM returns. The models are compared with the test for superior predictive ability (SPA) and the reality check for data snooping (RC). Our empirical results show that the RC lacks power to an extent that makes it unable to distinguish ‘good’ and ‘bad’ models in our analysis. Copyright © 2005 John Wiley & Sons, Ltd.