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The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool | | Ioannis P. Antoniades
; Giuseppe Brandi
; M. H. Hanias
; T. Di Matteo
; | | Date: |
18 Oct 2020 | | Abstract: | The dynamical evolution of multiscaling in financial time series is
investigated using time-dependent Generalized Hurst Exponents (GHE), $H_q$, for
various values of the parameter $q$. Using $H_q$, we introduce a new visual
methodology to algorithmically detect critical changes in the scaling of the
underlying complex time-series. The methodology involves the degree of
multiscaling at a particular time instance, the multiscaling trend which is
calculated by the Change-Point Analysis method, and a rigorous evaluation of
the statistical significance of the results. Using this algorithm, we have
identified particular patterns in the temporal co-evolution of the different
$H_q$ time-series. These GHE patterns, distinguish in a statistically robust
way, not only between time periods of uniscaling and multiscaling, but also
among different types of multiscaling: symmetric multiscaling (M) and
asymmetric multiscaling (A). We apply the visual methodology to time-series
comprising of daily close prices of four stock market indices: two major ones
(S&P~500 and NIKKEI) and two peripheral ones (Athens Stock Exchange general
Index and Bombay-SENSEX). Results show that multiscaling varies greatly with
time: time periods of strong multiscaling behavior and time periods of
uniscaling behavior are interchanged while transitions from uniscaling to
multiscaling behavior occur before critical market events, such as stock market
bubbles. Moreover, particular asymmetric multiscaling patterns appear during
critical stock market eras and provide useful information about market
conditions. In particular, they can be used as ’fingerprints’ of a turbulent
market period as well as provide warning signals for an upcoming stock market
’bubble’. The applied visual methodology also appears to distinguish between
exogenous and endogenous stock market crises, based on the observed patterns
before the actual events. | | Source: | arXiv, 2010.08890 | | Services: | Forum | Review | PDF | Favorites | |
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