Distributions of historic market data: relaxation and correlations

M. Dashti Moghaddam, Zhiyuan Liu and R. A. Serota ()
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M. Dashti Moghaddam: University of Cincinnati
Zhiyuan Liu: University of Cincinnati
R. A. Serota: University of Cincinnati

The European Physical Journal B: Condensed Matter and Complex Systems, 2021, vol. 94, issue 4, 1-13

Abstract: Abstract We investigate relaxation and correlations in a class of mean-reverting models for stochastic variances. We derive closed-form expressions for the correlation functions and leverage for a general form of the stochastic term. We also discuss correlation functions and leverage for three specific models— multiplicative, Heston (Cox-Ingersoll-Ross) and combined multiplicative-Heston—whose steady-state probability density functions are Gamma, Inverse Gamma and Beta Prime respectively, the latter two exhibiting “fat” tails. For the Heston model, we apply the eigenvalue analysis of the Fokker-Planck equation to derive the correlation function—in agreement with the general analysis— and to identify a series of time scales, which are observable in relaxation of cumulants on approach to the steady state. We test our findings on a very large set of historic financial markets data. Graphic abstract

Date: 2021
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DOI: 10.1140/epjb/s10051-021-00089-9

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