Berlin 2024 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

SOE: Fachverband Physik sozio-ökonomischer Systeme

SOE 24: Financial Markets and Risk Management

SOE 24.3: Talk

Friday, March 22, 2024, 10:30–10:45, MA 001

Multivariate distributions of correlated returns in nonstationary stock markets — Efstratios Manolakis, •Anton J. Heckens, and Thomas Guhr — Universität Duisburg-Essen, Lotharstr. 1, 47048 Duisburg

Risk assessment for rare events is very important for understanding systemic stability. Since financial markets are highly correlated, it is essential to study multivariate distributions of stocks, i.e. the joint probability density functions. To the best of our knowledge, we are the first to empirically study multivariate distributions for a large number of correlated stocks [1]. To this end, we compare empirical distributions with the results of a random matrix model [2]. First, our model separates different time scales: smaller time intervals, so-called epochs, that are assumed to be stationary and large scales on which the nonstationarity of the market is relevant and strong. Second, our model treats nonstationary fluctuations of measured correlation matrices by averaging over random matrices.

[1] Efstratios Manolakis, Anton J. Heckens and Thomas Guhr, Analysis of Aggregated Return Distributions for Stock Markets. Available at SSRN: https://ssrn.com/abstract=4462276

[2] Thomas Guhr and Andreas Schell 2021 J. Phys. A: Math. Theor. 54 125002

Keywords: Stock Markets; Correlations; Nonstationarity; Multivariate Return Distributions; Random Matrix Model