Multiscaling in Finance

Abstract

The multiscaling behaviour of financial time-series is one of the acknowledged stylized facts in the literature [1]. The source of the measured multifractality in financial markets has been long debated and it has been attributed to mainly two sources: the power law tails and the non linear autocorrelation of the analysed time-series [2,3]. In this talk I will discuss the origin of multiscaling in financial time-series and investigate how to best quantify it [4,5]. In particular I will show results on the application of the Generalized Hurst exponent tool to different financial time series and I will show the powerfulness of such tool to detect changes in markets’ behaviours, to differentiate markets accordingly to their degree of development, to asses risk and to provide a new tool for forecasting.

[1] T. Di Matteo, Quantitative Finance 7(1) (2007) 21.

[2] J. W Kantelhardt, Stephan A Zschiegner, Eva Koscielny-Bunde, Shlomo Havlin, Armin Bunde, and H Eugene Stanley, Physica A 316 (2002)87-114

[3] Jozef Barunik, Tomaso Aste, T. Di Matteo, Ruipeng Liu, Physica A 391 (2012) 4234–4251.

[4] R. J. Buonocore, T. Aste, T. Di Matteo, Chaos, Solitons and Fractals 88 (2016) 38-47.

[5] R. J. Buonocore, T. Di Matteo, T. Aste, (2017), Phys.Rev.E, 95 (4) (2017) 042311.