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Financial time series
Author(s) -
Politis Dimitris N.
Publication year - 2009
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.24
Subject(s) - heteroscedasticity , series (stratigraphy) , autoregressive conditional heteroskedasticity , nonlinear system , computer science , econometrics , finance , transformation (genetics) , financial market , autoregressive model , economics , volatility (finance) , paleontology , biochemistry , chemistry , physics , quantum mechanics , gene , biology
The evolution of financial markets is a complicated real‐world phenomenon that ranks at the top in terms of difficulty of modeling and/or prediction. One reason for this difficulty is the well‐documented nonlinearity that is inherent at work. The state‐of‐the‐art on the nonlinear modeling of financial returns is given by the popular auto‐regressive conditional heteroscedasticity (ARCH) models and their generalizations but they all have their short‐comings. Foregoing the goal of finding the ‘best’ model, it is possible to simply transform the problem into a more manageable setting such as the setting of linearity. The form and properties of such a transformation are given, and the issue of one‐step‐ahead prediction using the new approach is explicitly addressed. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Applications of Computational Statistics > Computational Finance