Market Efficiency, Roughness and Long Memory in the PSI20 Index Returns: Wavelet and Entropy Analysis

In this study, features of financial returns of PSI20 index, related to market efficiency, are captured using wavelet and entropy based techniques. This characterization includes the following points. First, the detection of long memory, associated to low frequencies, and a global measure of the time series: the Hurst exponent estimated by several methods including wavelets. Second, the degree of roughness, or regularity variation, associated to the Hölder exponent, fractal dimension and estimation based on multifractal spectrum. Finally, the degree of the unpredictability of the series, estimated by approximate entropy. These aspects may also be studied through the concepts of non-extensive entropy and distribution using, for instance, the Tsallis q-triplet. They allow to study the existence of efficiency in the nancial market. On the other hand, the study of local roughness is performed by considering wavelet leaders based entropy. In fact, the wavelet coefficients are computed from a multiresolution analysis, and the wavelet leaders are defined by the local suprema of these coefficients, near the point we are considering. The resulting entropy is more accurate in that detection than the Hölder exponent. These procedures enhance the capacity to identify the occurrence of financial crashes.