Multifractality in the stock market: price increments versus waiting times (original) (raw)
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In this paper, we analyse multifractality among Czech, Hungarian and Russian stock exchanges. For this end we perform a method titled multifractal detrended fluctuation analysis (MF-DFA) to investigate the multifractal properties of PX, BUX and RTS indices. By applying the MF-DFA method we first calculate the generalised Hurst exponents, we then deduce the Rényi exponents as well as the singularity spectrum of these indices. Furthermore, we perform shuffling and surrogate techniques to detect the sources of multifractality. We also compute the contribution of two major sources of multifractality that are long-range temporal correlations and fat-tail distribution. This study shows that the Czech, Hungarian and Russian stock exchanges are neither efficient nor fractals, but they are multifractal markets. By comparing spectrum width of these indices, we also find which index has the richer multifractal feature.
Fractality and Multifractality in a Stock Market’s Nonstationary Financial Time Series
Journal of the Korean Physical Society, 2020
A financial time series, such as a stock market index, foreign exchange rate, or a commodity price, fluctuates heavily and shows scaling behaviors. Scaling and multi-scaling behaviors are measured for a nonstationary time series, such as stock market indices, high-frequency stock prices of individual stocks, or the volatility time series of a stock index. We review the fractality, multi-scaling, and multifractality of the financial time series of a stock market. We introduce a detrended fluctuation analysis of the financial time series to extract fluctuation patterns. Multifractality is measured using various methods, such as generalized Hurst exponents, the generalized partition function method, a detrended fluctuation analysis, the detrended moving average method, and a wavelet transformation.