Time-dependent Hurst exponent in financial time series

By Anna Carbone, G. Castelli, H. Eugene Stanley. 2004

400 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fa27b228ced17421ef9c6d5628cd521bb7d6f4a6

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Citations in the corpus

Rescaled Range Analysis and Detrended Fluctuation Analysis: Finite Sample Properties and Confidence Intervals (2010, background): “Introduction Long-range dependence and its presence in the financial time series has been discussed in several recent papers (Czarnecki et al. 2008; Grech and Mazur 2004; Carbone et al. 2004; Matos et al. 2008; Vandewalle et al. 1997; Alvarez-Ramirez et al. 2008; Peters 1994; Di Matteo et al. 2005; ”
Introduction to Multifractal Detrended Fluctuation Analysis in Matlab (2012, background): “, 2009), moving average (Carbone et al., 2004), and empirical mode decomposition (Qian et al.”
The inefficiency of Bitcoin revisited: a dynamic approach (2017, background): “Carbone et al. (2004) find long memory in German stock and sovereign bond markets and McCarthy et al. (2009) find long memory in yields of corporate bonds and in the spread of returns of corporate bonds and treasury bonds.”
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.”
Some stylized facts of the Bitcoin market (2017, background): “With respect to the fixed income market, Carbone [19] finds local variability of the correlation exponent in the German stock and sovereign bond markets.”
On Hurst exponent estimation under heavy-tailed distributions (2010, methodology): “Detrending moving average (DMA) is quite a new technique proposed by (Alessio et al., 2002) and further discussed in (Carbone et al., 2004).”
Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series (2011, methodology): “Although multi-scaling analysis based on the GHE has been already extensively pursued in the literature [9, 10, 11, 14, 15, 16, 17, 18, 19, 20], the dynamics of the GHE has been scarcely investigated [21].”
Measuring capital market efficiency: Global and local correlations structure (2012, background): “Hurst exponent and a presence of long-term memory has been widely analyzed in recent years – in stock indices [17, 29], interest rates [12], bonds [13], exchange rates [45] and others.”
Limit order books (2010, methodology): “2004), the Deutsche Bourse (Carbone et al. 2004), and in FX markets (Gould et al.”
Pricing Currency Option in a Mixed Fractional Brownian Motion with Jumps Environment (2014, background): “Since fractional Brownian motion has two substantial features such as self-similarity and long-range dependence, thus using it is more applicable to capture behavior from financial asset [32–37].”