Model-Free Volatility Prediction
The 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).
|Date of creation:||01 Dec 2003|
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- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, vol. 36, pages 394.
- Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
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