Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy Analysis
In this study, features of the financial returns of the PSI20index, related to market efficiency, are captured using wavelet- and entropy-based techniques. This characterization includes the following points. First, the detection of long memory, associated with low frequencies, and a global measure...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-05-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/16/5/2768 |
| _version_ | 1798002661366169600 |
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| author | Rui Pascoal Ana Margarida Monteiro |
| author_facet | Rui Pascoal Ana Margarida Monteiro |
| author_sort | Rui Pascoal |
| collection | DOAJ |
| description | In this study, features of the financial returns of the PSI20index, related to market efficiency, are captured using wavelet- and entropy-based techniques. This characterization includes the following points. First, the detection of long memory, associated with low frequencies, and a global measure of the time series: the Hurst exponent estimated by several methods, including wavelets. Second, the degree of roughness, or regularity variation, associated with the H¨older exponent, fractal dimension and estimation based on the multifractal spectrum. Finally, the degree of the unpredictability of the series, estimated by approximate entropy. These aspects may also be studied through the concepts of non-extensive entropy and distribution using, for instance, the Tsallis q-triplet. They allow one to study the existence of efficiency in the financial market. On the other hand, the study of local roughness is performed by considering wavelet leader-based entropy. In fact, the wavelet coefficients are computed from a multiresolution analysis, and the wavelet leaders are defined by the local suprema of these coefficients, near the point that we are considering. The resulting entropy is more accurate in that detection than the H¨older exponent. These procedures enhance the capacity to identify the occurrence of financial crashes. |
| first_indexed | 2024-04-11T11:55:42Z |
| format | Article |
| id | doaj.art-dfe23742ba43419da18d0ac8f19672ec |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T11:55:42Z |
| publishDate | 2014-05-01 |
| publisher | MDPI AG |
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| series | Entropy |
| spelling | doaj.art-dfe23742ba43419da18d0ac8f19672ec2022-12-22T04:25:11ZengMDPI AGEntropy1099-43002014-05-011652768278810.3390/e16052768e16052768Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy AnalysisRui Pascoal0Ana Margarida Monteiro1School of Economics, University of Coimbra, Avenida Doutor Dias da Silva 165, 3004-512 Coimbra, Coimbra, PortugalSchool of Economics, University of Coimbra, Avenida Doutor Dias da Silva 165, 3004-512 Coimbra, Coimbra, PortugalIn this study, features of the financial returns of the PSI20index, related to market efficiency, are captured using wavelet- and entropy-based techniques. This characterization includes the following points. First, the detection of long memory, associated with low frequencies, and a global measure of the time series: the Hurst exponent estimated by several methods, including wavelets. Second, the degree of roughness, or regularity variation, associated with the H¨older exponent, fractal dimension and estimation based on the multifractal spectrum. Finally, the degree of the unpredictability of the series, estimated by approximate entropy. These aspects may also be studied through the concepts of non-extensive entropy and distribution using, for instance, the Tsallis q-triplet. They allow one to study the existence of efficiency in the financial market. On the other hand, the study of local roughness is performed by considering wavelet leader-based entropy. In fact, the wavelet coefficients are computed from a multiresolution analysis, and the wavelet leaders are defined by the local suprema of these coefficients, near the point that we are considering. The resulting entropy is more accurate in that detection than the H¨older exponent. These procedures enhance the capacity to identify the occurrence of financial crashes.http://www.mdpi.com/1099-4300/16/5/2768efficiencylong memoryfractal dimensionunpredictabilityq-tripletentropywavelets |
| spellingShingle | Rui Pascoal Ana Margarida Monteiro Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy Analysis Entropy efficiency long memory fractal dimension unpredictability q-triplet entropy wavelets |
| title | Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy Analysis |
| title_full | Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy Analysis |
| title_fullStr | Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy Analysis |
| title_full_unstemmed | Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy Analysis |
| title_short | Market Efficiency, Roughness and Long Memory in PSI20 Index Returns: Wavelet and Entropy Analysis |
| title_sort | market efficiency roughness and long memory in psi20 index returns wavelet and entropy analysis |
| topic | efficiency long memory fractal dimension unpredictability q-triplet entropy wavelets |
| url | http://www.mdpi.com/1099-4300/16/5/2768 |
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