Speaker
Description
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 [2,3]. In this talk I will discuss the origin of multiscaling in financial time-series, investigate how to best quantify it [4,5] and I will introduce a new methodology that provides a robust estimation and tests the multi-scaling property in a statistically significant way [6].
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 [7]. I will also show an empirical relationship, to our knowledge the first on in the literature, which links a univariate property, i.e. the degree of multiscaling behaviour of a time series, to a multivariate one, i.e. the average correlation of the stock log-returns with the other stocks traded in the same market and discuss its implications [8].
[1] T. Di Matteo, Q. Finance 7 (2007) 21
[2] J. W Kantelhardt et al, Physica A 316 (2002) 87
[3] J. Barunik, T. Aste, T. Di Matteo, R. Liu, Physica A 391 (2012) 4234
[4] R. J. Buonocore, T. Aste, T. Di Matteo, Chaos, Solitons and Fractals 88 (2016) 38
[5] R. J. Buonocore, T. Di Matteo, T. Aste, Phys. Rev. E 95 (2017) 042311
[6] G. Brandi, T. Di Matteo, Eur. J. Finance (2021) DOI: 10.1080/1351847X.2021.1908391
[7] I. P. Antoniades, G. Brandi, L. G. Magafas, T. Di Matteo, Physica A 565 (2021) 12556
[8] R. J. Buonocore, G. Brandi, R. N. Mantegna, T. Di Matteo, Q. Finance 20 (2020) 133
