A multifractal detrended fluctuation description of Iranian rial–US dollar exchange rate

By P. Norouzzadeh. 2005

168 citations recorded by the source corpus.

The miltifractal properties and scaling behaviour of the exchange rate variations of the Iranian rial against the US dollar from a daily perspective is numerically investigated. For this purpose the multifractal detrended fluctuation analysis (MF-DFA) is used. Through multifractal analysis, the scaling exponents, generalized Hurst exponents, generalized fractal dimensions and singularity spectrum are derived. Moreover, contribution of two major sources of multifractality, that is, fat-tailed probability distributions and nonlinear temporal correlations are studied.

Subfields: Condensed Matter Physics, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/90e757e4190888391a4b8d33a6b53b296eab1abb

How researchers see this paper

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

In Condensed Matter Physics

In Economics and Econometrics

In Statistical and Nonlinear Physics

Citations in the corpus

Mixed-correlated ARFIMA processes for power-law cross-correlations (2013, background): “Long-termmemory has been studied for stock indices [1–5], bonds [6,2], exchange rates [7,8], commodities [9–11], interest rates [12,13] and others.”
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”
Multifractal Detrended Fluctuation Analysis of the Chinese Stock Index Futures Market (2013, background): “As a robust and powerful technique for the verification of multifractal behavior, MF-DFA has so far been applied to various markets, including international crude oil markets [5], foreign exchange markets [6], stock markets [7], gold markets [8], and agricultural commodity futures markets [9].”
A multifractal approach for stock market inefficiency (2008, background): “It is well known that empirical data coming from financialmarkets, such as stockmarket indices [1–11], foreign exchange markets [12–14], commodities [6], traded volumes [15] and interest rates [16] have a multifractal nature.”
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].”