Multiscale Analysis of Stock Index Return Volatility
We present a study where wavelet approximation techniques and some related computational algorithms are applied to non-stationary high frequency financial times series. Wavelets represent a novel instrument as far as concerned applications in the finance setting, but have a great relevance in many domains, from physics to statistics. Thus, while one goal of the paper is to compare the numerical performance of global and local function optimizers, another goal is to try to show that ad hoc wavelet-based function dictionaries are very useful for financial modeling through signal decomposition and approximation. Detecting the latent dependence features which are typically found in high frequency financial returns is particularly important for the scope of proposing models which are able to achieve reliable results in parameter estimation and pointwise function prediction. We show that by pre-processing data with wavelet dictionaries we effectively account for hidden periodic components, whose discovery allows to attain and improve the feature extraction power. We refer to sparse approximation through the Matching Pursuit algorithm, thus handling the negative effects of covariance non-stationarity at very high frequencies.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 23 (2004)
Issue (Month): 3 (04)
|Contact details of provider:|| Web page: http://www.springer.com|
|Order Information:||Web: http://www.springer.com/economics/economic+theory/journal/10614/PS2|