Multifractal detrended fluctuation analysis of derivative and spot markets

By Gyuchang Lim, S. Kim, H. Lee, Kyungsik Kim, Dong-In Lee. 2007

67 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Finite-size effect and the components of multifractality in financial volatility (2009, background): “To understand the impact of the distribution, one can either remove large returns [64] or generate surrogate data having a Gaussian distribution while keeping the linear correlation of the original data [29,30,65].”
Asymmetric multifractal scaling behavior in the Chinese stock market: Based on asymmetric MF-DFA (2013, methodology): “[42] to generate the surrogate data with Gaussian distribution while maintaining the linear correlation of the original data [43,44].”
Statistical properties and multifractality of Bitcoin (2017, methodology): “The MF–DFA, which is an extended method of DFA, can investigate multifractal properties of non-stationary time series, and has been successfully applied for a variety of financial markets, such as stock [16, 17, 18, 19, 20, 21, 22, 23], commodity [16, 24, 25, 26, 27], tanker [28], derivative [29], f”
p-exponent and p-leaders, Part II: Multifractal Analysis. Relations to Detrended Fluctuation Analysis (2015, background): “, [57, 58, 59, 60, 61, 62]), thus empirically justifying a posteriori the choice of Tmfd as a relevant multiresolution quantity for multifractal analysis.”
A multifractal approach for stock market inefficiency (2008, methodology): “A similar q-range was recently chosen for the multifractal analysis of traded volume [15] and derivative and spot market [45].”