Author(s)

V. Romanov, V. Slepov, M. Badrina & A. Federyakov

Abstract

In this paper the results of multifractal analysis by means of partitions and scaling function calculation are described, as well as wavelet analysis, which were applied to USA 1987 October Black Monday DJ data. For the partition calculation and Legendre transform a special program was elaborated. As our aim is predicting crash situations, we are trying to find out the best indicator that uses multifractal analysis and wavelet analysis methodology. With this aim in mind we have tested different methods of preprocessing the original time series to discover the best indicator. The wavelet analysis data were calculated on a 256 day moving window. The changes in the multifractal analysis features were studied while approaching crisis point and after the crisis. From the multiagent market model we can observe the crisis evolution and the dynamic of changing parameters such as share prices, trading volumes, price increments and statistical distribution dependent on traders’ strategies. Keywords: stock market, market dynamics, multiagent simulation, wavelet analysis. 1 Introduction The prediction of tough crucial changes in the finance market is very difficult because of the non-linear structure of the processes. It doesn’t allow us to effectively use common statistical methods. Hurst exponent values estimation can be used for time series properties determination and for choosing the proper method of data processing. Unfortunately in the case of the multifractal time series, the Hurst exponent value is not constant and at different time scales takes different values, generating a spectrum of values. Multifractal analyses potentially give us the possibility of predicting sharp changing market states. In this paper we are making an attempt to apply

Keywords

stock market, market dynamics, multiagent simulation, wavelet analysis.