Persistence characteristics of Latin American financial markets |
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| Affiliation: | 1. School of Business, Iona College, New Rochelle, NY 10801, USA;2. International School of Economics, Kazakh-British Technical University, Almaty 050000, Kazakhstan;3. College of Business Administration, California State University, Stanislaus, Turlock, CA 95832, USA;1. Department of Quantitative Methods in Economics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;2. Department of Finance, Auckland University of Technology, Auckland, New Zealand;3. Complutense Institute for Economic Analysis, Universidad Complutense de Madrid. Madrid, Spain;1. Department of Physics, Section of Solid State Physics, University of Athens, Panepistimiopolis, GR-15784, Zografos, Athens, Greece;2. Department of Electronics Engineering, Technological Education Institute (TEI) of Piraeus, 250 Thivon & P. Ralli, GR-12244, Aigaleo, Athens, Greece;1. DRM Finance, University Paris Dauphine, Place du Maréchal de Lattre de Tassigny, 75775 Paris Cedex 16, France;2. IPAG Business School (IPAG Lab), 184 Boulevard Saint-Germain, 75006 Paris, France;1. Aristotle University of Thessaloniki, Thessaloniki 54124, Greece;2. Michigan Technological University, Houghton MI 49931, USA;3. Beijing University of Civil Engineering and Architecture, Beijing 100044, China;4. ITMO University, St. Petersburg 197101, Russia;5. Togliatti State University, Togliatti 445020, Russia;1. Universidade Federal Rural de Pernambuco, Departamento de Estatística e Informática, Rua Dom Manoel de Medeiros s/n, Dois Irmãos, 52171-900, Recife/PE, Brazil;2. Department of Biological and Agricultural Engineering, Texas A&M University, Scoates Hall, 2117 TAMU, College Station, TX 77843, USA;3. Zachry Department of Civil Engineering, Texas A&M University, Scoates Hall, 2117 TAMU, College Station, TX 77843, USA |
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| Abstract: | The financial rates of return from Latin American stock and currency markets are found to be non-normal, non-stationary, non-ergodic, and long-term dependent, i.e., they have long memory. The degree of long-term dependence is measured by monofractal (global) Hurst exponents from wavelet multiresolution analysis (MRA). Scalograms and scalegrams provide the respective visualizations of these wavelet coefficients and the power spectrum of the rates of return. The slope of the power spectrum identifies the Hurst exponent and thereby the degree of time-scaling dependence that cannot be determined by Box–Jenkins type, stationarity-based, time series analysis. Our long-term dependence and time–frequency scaling results are consistent with similar empirical findings from American, European, and Asian financial markets. They extend the domain of the empirical investigation of the dynamics and risk characteristics of the global financial markets and refute the hypothesis of perfectly efficient financial markets. |
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