Wavelet methods in (financial) time-series processing
We briefly describe the major advantages of using the wavelet transform for the processing of financial time series on the example of the S&P index. In particular, we show how to uncover local the scaling (correlation) characteristics of the S&P index with the wavelet based effective H'older exponent [1, 2]. We use it to display the local spectral (multifractal) contents of the S&P index. In addition to this, we analyse the collective properties of the local correlation exponent as perceived by the trader, exercising various time horizon analyses of the index. We observed an intriguing interplay between such (different) time horizons. Heavy oscillations at shorter time horizons which seem to be accompanied by a steady decrease of correlation level for longer time horizons, seem to be characteristic patterns before the biggest crashes of the index. We find that this way of local presentation of scaling properties may be of economic importance.
|MODELS AND PRINCIPLES (acm H.1), PATTERN RECOGNITION (acm I.5), MISCELLANEOUS (acm J.m), PHYSICAL SCIENCES AND ENGINEERING (acm J.2), DATA STORAGE REPRESENTATIONS (acm E.2)|
|Fractals (msc 28A80), Pattern recognition, speech recognition (msc 68T10), Searching and sorting (msc 68P10), None of the above, but in MSC2010 section 82Dxx (msc 82D99)|
|Information (theme 2)|
|Information Systems [INS]|
Struzik, Z.R. (2000). Wavelet methods in (financial) time-series processing. Information Systems [INS]. CWI.