Model-Free Versus Model-Based Volatility Prediction

Posted: 16 Jun 2008

See all articles by Dimitris N. Politis

Dimitris N. Politis

University of California, San Diego (UCSD) - Department of Mathematics

Date Written: Summer 2007

Abstract

The well-known ARCH/GARCH models for financial time series have been criticized of late for their poor performance in volatility prediction, that is, prediction of squared returns. Focusing on three representative data series, namely a foreign exchange series (Yen vs. Dollar), a stock index series (the S&P500 index), and a stock price series (IBM), the case is made that financial returns may not possess a finite fourth moment. Taking this into account, we show how and why ARCH/GARCH models when properly applied and evaluated actually do have nontrivial predictive validity for volatility. Furthermore, we show how a simple model-free variation on the ARCH theme can perform even better in that respect. The model-free approach is based on a novel normalizing and variance stabilizing transformation (NoVaS, for short) that can be seen as an alternative to parametric modeling. Properties of this transformation are discussed, and practical algorithms for optimizing it are given.

Keywords: ARCH/GARCH models, forecasting, L methods, volatility

Suggested Citation

Politis, Dimitris, Model-Free Versus Model-Based Volatility Prediction (Summer 2007). Journal of Financial Econometrics, Vol. 5, Issue 3, pp. 358-359, 2007, Available at SSRN: https://ssrn.com/abstract=1145516 or http://dx.doi.org/10.1093/jjfinec/nbm004

Dimitris Politis (Contact Author)

University of California, San Diego (UCSD) - Department of Mathematics ( email )

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La Jolla, CA 92093-0112
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