Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development

By T. Di Matteo, Tomaso Aste, Michel M. Dacorogna. 2004

437 citations recorded by the source corpus.

The scaling properties encompass in a simple analysis many of the volatility characteristics of financial markets. That is why we use them to probe the different degree of markets development. We empirically study the scaling properties of daily Foreign Exchange rates, Stock Market indices and fixed income instruments by using the generalized Hurst approach. We show that the scaling exponents are associated with characteristics of the specific markets and can be used to differentiate markets in their stage of development. The robustness of the results is tested by both Monte-Carlo studies and a computation of the scaling in the frequency-domain.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/48bf122cd1954ed970a5db4222c3157f1b1132ed

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Citations in the corpus

Multi-scaling in finance (2007, methodology): “This behaviour holds for all other data (Di Matteo et al. 2005).”
Patterns in high-frequency FX data: discovery of 12 empirical scaling laws (2008, background): “[1996], Gabaix et al. [2003], Sornette [2000], Di Matteo [2007]) even though there has been no consensus amongst researchers for why the scaling law exists (e.”
Commodity futures and market efficiency (2013, background): “In a series of papers, Di Matteo et al. (2003, 2005) and Di Matteo (2007) study long-termmemory and multi-scaling of a wide portfolio of stock indices, foreign exchange rates, Treasury rates and Eurodollar interbank interest rates using various estimators of longterm memory.”
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.”
Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series (2011, methodology): “Previous works [14, 15] have highlighted that the value of the GHE allows to characterize the stage of development of a market, with values of the GHE greater than 0.”
On Hurst exponent estimation under heavy-tailed distributions (2010, background): “…in financial time series have been widely discussed in several recent research papers, e.g. (Alvarez-Ramirez et al., 2008; Carbone et al., 2004; Czarnecki et al., 2008; Di Matteo, 2007; Di Matteo et al., 2005; Grech and Mazur, 2004; Matos et al., 2008; Peters, 1994; Vandewalle et al., 1997).”
The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature (2011, methodology): “…analysis (Cajueiro and Tabak, 2005b), detrended fluctuation analysis (Oh et al., 2008; Serletis et al., 2008), detrending moving average (Serletis and Rosenberg, 2009), wavelet analysis (Kyaw et al., 2006; Brooks et al., 2008) and the generalized Hurst exponent approach (Di Matteo et al., 2005).”
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.”
Rescaled Range Analysis and Detrended Fluctuation Analysis: Finite Sample Properties and Confidence Intervals (2010, background): “…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; Di Matteo 2007).”
Long-range dependence and multifractality in the term structure of LIBOR interest rates (2007, background): “[18] to measure the degree of market development of several financial markets.”
Measuring capital market efficiency: Global and local correlations structure (2012, methodology): “we apply jackknife which is standardly done for this method [19].”