Empirical Testing of Multifractality of Financial Time Series Based on WTMM

Yalamova, Rossitsa

doi:10.1142/s0218348x09004508

“…There are also significant differences in the multifractal behavior before and after crashes. Yalamova investigated the multifractal behavior of seven stock market indices (DJIA, S&P500, AUS, TSX, NIKKEI, NASDAQ and FTSE) before and after the 1987 crash [993]. The most probable singularity exponent α 0 decreases for DJIA, S&P500, TSX, NASDAQ and FTSE, increases for AUS, and remains stable for NIKKEI.…”

Section: Impact Of Broad Distributionsmentioning

confidence: 99%

Multifractal analysis of financial markets: a review

Jiang

¹

,

Xie

²

,

Zhou

³

et al. 2019

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Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originated, multifractal analysis of financial markets has bloomed, forming one of the main directions of econophysics. We review the multifractal analysis methods and multifractal models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. We survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality. The usefulness of multifractal analysis in quantifying market inefficiency, in supporting risk management and in developing other applications is presented. We finally discuss open problems and further directions of multifractal analysis.

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“…There are also significant differences in the multifractal behavior before and after crashes. Yalamova investigated the multifractal behavior of seven stock market indices (DJIA, S&P500, AUS, TSX, NIKKEI, NASDAQ and FTSE) before and after the 1987 crash [937]. The most probable singularity exponent α 0 decreases for DJIA, S&P500, TSX, NASDAQ and FTSE, increases for AUS, and remains stable for NIKKEI.…”

Section: Impact Of Extreme Valuesmentioning

confidence: 99%

Multifractal analysis of financial markets

Jiang,

Xie,

Zhou

et al. 2018

Preprint

View full text Add to dashboard Cite

Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originated, multifractal analysis of financial markets has bloomed, forming one of the main directions of econophysics. We review the multifractal analysis methods and multifractal models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. We survey the cumulating evidence for the presence of multifractality in financial time series in different markets and at different time periods and discuss the sources of multifractality. The usefulness of multifractal analysis in quantifying market inefficiency, in supporting risk management and in developing other applications is presented. We finally discuss open problems and further directions of multifractal analysis.

show abstract