Estimating the Fractal Dimension of the S&P 500 Index using Wavelet Analysis TeX Export

@misc{citeulike:1196601, abstract = {S\&P 500 index data sampled at one-minute intervals over the course of 11.5 years (January 1989- May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. An asymptotically unbiased and efficient estimator using the log-scale spectrum is employed. The estimator is asymptotically Gaussian and the variance of the estimate that is obtained from a data segment of \$N\$ points is of order \$\frac{1}{N}\$. Wavelet analysis is tailor made for the high frequency data set, since it has low computational complexity due to the pyramidal algorithm for computing the detail coefficients. This estimator is robust to additive non-stationarities, and here it is shown to exhibit some degree of robustness to multiplicative non-stationarities, such as seasonalities and volatility persistence, as well. This analysis shows that the market became more efficient in the period 1997-2000.}, archivePrefix = {arXiv}, author = {Bayraktar, Erhan and Poor, Vincent H. and Sircar, Ronnie}, citeulike-article-id = {1196601}, citeulike-linkout-0 = {http://arxiv.org/abs/math.ST/0703834}, citeulike-linkout-1 = {http://arxiv.org/pdf/math.ST/0703834}, day = {28}, eprint = {math.ST/0703834}, keywords = {500, analysis, index, sp, using, wavelet}, month = {Mar}, posted-at = {2007-03-29 19:11:29}, priority = {2}, title = {Estimating the Fractal Dimension of the S\&P 500 Index using Wavelet Analysis}, url = {http://arxiv.org/abs/math.ST/0703834}, year = {2007} }

TY - ELEC ID - citeulike:1196601 L3 - citeulike-article-id:1196601 N2 - S&P 500 index data sampled at one-minute intervals over the course of 11.5 years (January 1989- May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. An asymptotically unbiased and efficient estimator using the log-scale spectrum is employed. The estimator is asymptotically Gaussian and the variance of the estimate that is obtained from a data segment of $N$ points is of order $\frac{1}{N}$. Wavelet analysis is tailor made for the high frequency data set, since it has low computational complexity due to the pyramidal algorithm for computing the detail coefficients. This estimator is robust to additive non-stationarities, and here it is shown to exhibit some degree of robustness to multiplicative non-stationarities, such as seasonalities and volatility persistence, as well. This analysis shows that the market became more efficient in the period 1997-2000. TI - Estimating the Fractal Dimension of the S&P 500 Index using Wavelet Analysis KW - 500 KW - analysis KW - index KW - sp KW - using KW - wavelet AU - Bayraktar, Erhan AU - Poor, Vincent AU - Sircar, Ronnie PY - 2007/Mar/28/ UR - http://arxiv.org/abs/math.ST/0703834 ER -