Model-Free Volatility Prediction
AbstractThe well-known ARCH/GARCH models with normal errors account only partly for the degree of heavy tails empirically found in the distribution of financial returns series. Instead of resorting to an arbitrary nonnormal distribution for the ARCH/GARCH residuals we propose a different viewpoint via a novel normalizing and varianceâ€“ stabilizing transformation (NoVaS) that can be seen as an alternative to parametric modeling. Some properties of this transformation are discussed, and algorithms for optimizing it are given. Special emphasis is given on the problem of volatility prediction and the issue of a proper measure for quality of prediction. A new prediction algorithm with favorable performance is given based on the NoVaS transformation. For motivation and illustration of this new general methodology, the NoVaS transformation is implemented in connection with three real data series: a foreign exchange series (Yen vs. Dollar), a stock index series (S&P500 index), and a stock price series (IBM).
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Bibliographic InfoPaper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt0648834b.
Date of creation: 01 Dec 2003
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Heteroscedasticity; Kyrtosis; time series;
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