Multifractal Analysis And Multiagent Simulation
For Market Crash Prediction
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.
Related Book
Other papers in this volume
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