Scaling behaviors in differently developed markets

By T. Di Matteo, Tomaso Aste, Michel M. Dacorogna. 2003

297 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/882e51c540c47bca8d80aa2ccf1eab098c078e28

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Citations in the corpus

Measuring capital market efficiency: Global and local correlations structure (2012, methodology): “HHCA/GHE is then based on definition of both Barabasi et al. [4] and Di Matteo [17].”
A multifractal approach for stock market inefficiency (2008, unknown): “[27,28] and Cajueiro and Tabak [29,30].”
Commodity futures and market efficiency (2013, background): “For comparison of both time and frequency domain estimators, see Beran (1994), Taqqu et al. (1995), Taqqu and Teverovsky (1996), Robinson (1995), Geweke and Porter-Hudak (1983), Di Matteo et al. (2003), Di Matteo (2007), Barunik and Kristoufek (2010) and Teverovsky et al. (1999).”
Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency (2010, background): “[5,6] have found that emergent markets have greater correlation than developed markets, suggesting more predictability.”
Multi-scaling in finance (2007, methodology): “Viewing market efficiency as a result of the interaction of these agents (Dacorogna et al. 2001b) brings us naturally to believe that it is the presence of many different agents that would characterize a mature market, while the absence of some type of agents should be a feature of less-developed…”
On Hurst exponent estimation under heavy-tailed distributions (2010, methodology): “…analysis (Hurst, 1951) and detrended fluctuation analysis (Peng et al., 1994) as well as quite novel methods of multifractal detrended fluctuation analysis (Kantelhardt et al., 2002), detrending moving average (Alessio et al., 2002) and generalized Hurst exponent approach (Di Matteo et al., 2003).”
Mixed-correlated ARFIMA processes for power-law cross-correlations (2013, methodology): “Kristoufek [27] generalizes the height–height correlation analysis [28,29] and the generalized Hurst exponent approach [1] into the multifractal height cross-correlation analysis (MF-HXA).”
Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series (2011, methodology): “[16,15,23] we have estimated the H(q) as an average of several linear fits of Eq.”
An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA (2014, background): “[11,12] and Cajueiro and Tabak [13,14] have approached the ranking of stock market efficiency via monofractal approach.”