Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series
In this paper, three new algorithms are introduced in order to explore long memory in financial time series. They are based on a new concept of fractal dimension of a curve. A mathematical support is provided for each algorithm and its accuracy is tested for different length time series by Monte Car...
| Main Authors: | , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
THE EUROPEAN PHYSICAL JOURNAL
2017
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| Online Access: | http://hdl.handle.net/10835/4866 |
| _version_ | 1789406643758301184 |
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| author | Sánchez-Granero, M.A Fernández-Martínez, M. Trinidad Segovia, J.E |
| author_facet | Sánchez-Granero, M.A Fernández-Martínez, M. Trinidad Segovia, J.E |
| author_sort | Sánchez-Granero, M.A |
| collection | DSpace |
| description | In this paper, three new algorithms are introduced in order to explore long memory in financial time series. They are based on a new concept of fractal dimension of a curve. A mathematical support is provided for each algorithm and its accuracy is tested for different length time series by Monte Carlo simulations. In particular, in the case of short length series, the introduced algorithms perform much better than the classical methods. Finally, an empirical application for some stock market indexes as well as some individual stocks is presented. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-4866 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2017 |
| publisher | THE EUROPEAN PHYSICAL JOURNAL |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-48662023-04-12T19:39:04Z Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series Sánchez-Granero, M.A Fernández-Martínez, M. Trinidad Segovia, J.E In this paper, three new algorithms are introduced in order to explore long memory in financial time series. They are based on a new concept of fractal dimension of a curve. A mathematical support is provided for each algorithm and its accuracy is tested for different length time series by Monte Carlo simulations. In particular, in the case of short length series, the introduced algorithms perform much better than the classical methods. Finally, an empirical application for some stock market indexes as well as some individual stocks is presented. 2017-06-16T09:42:33Z 2017-06-16T09:42:33Z 2012 info:eu-repo/semantics/article http://hdl.handle.net/10835/4866 10.1140/epjb/e2012-20803-2 en https://www.epj.org/articles/epjb/abs/2012/03/b110803/b110803.html Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess THE EUROPEAN PHYSICAL JOURNAL |
| spellingShingle | Sánchez-Granero, M.A Fernández-Martínez, M. Trinidad Segovia, J.E Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series |
| title | Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series |
| title_full | Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series |
| title_fullStr | Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series |
| title_full_unstemmed | Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series |
| title_short | Introducing fractal dimension algorithms to calculate the Hurst exponent of financial time series |
| title_sort | introducing fractal dimension algorithms to calculate the hurst exponent of financial time series |
| url | http://hdl.handle.net/10835/4866 |
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