Measuring multiscaling in financial time-series. (July 2016)

• Record Type: Journal Article
• Authors: Buonocore, R.J.; Aste, T.; Di Matteo, T.
• Is Part Of: Chaos, solitons and fractals. Volume 88(2016)
• Page Start: 38
• Page End: 47
• Publication Date: 2016-07
• Subjects: Multiscaling -- Multifractality -- Central limit theorem -- Power law tails -- Autocorrelation; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2015.11.022
• Languages: English
• ISSNs: 0960-0779

Abstract

Abstract: We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analyzing the multi/uni-scaling behavior of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range (2, 5). We discuss the right aggregation horizon to mitigate this bias.