Post by Sapphire Capital on Aug 7, 2008 23:19:19 GMT 4
Model-Free Versus Model-Based Volatility Prediction
Dimitris N. Politis
University of California, San Diego - Department of Mathematics
Journal of Financial Econometrics, Vol. 5, Issue 3, pp. 358-359, 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.1 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.
papers.ssrn.com/sol3/papers.cfm?abstract_id=1145516
Dimitris N. Politis
University of California, San Diego - Department of Mathematics
Journal of Financial Econometrics, Vol. 5, Issue 3, pp. 358-359, 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.1 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.
papers.ssrn.com/sol3/papers.cfm?abstract_id=1145516