Dynamic parameters for Brazilian financial time series
In this study we analyse a Brazilian stock index called IBOVESPA using techniques from dynamical systems theory and stochastic processes. We discuss the Lyapunov exponent, the correlation dimension, the LempelZiv complexity, the Hurst exponent and the BDS statistics. We compare this study with othe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Portuguese |
| Published: |
Universidade de São Paulo
2002-02-01
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| Series: | Economia Aplicada |
| Subjects: | |
| Online Access: | https://www.revistas.usp.br/ecoa/article/view/219890 |
| _version_ | 1797399471085060096 |
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| author | Gerson Francisco Cláudio Paiva Rogerio Rosenfeld |
| author_facet | Gerson Francisco Cláudio Paiva Rogerio Rosenfeld |
| author_sort | Gerson Francisco |
| collection | DOAJ |
| description |
In this study we analyse a Brazilian stock index called IBOVESPA using techniques from dynamical systems theory and stochastic processes. We discuss the Lyapunov exponent, the correlation dimension, the LempelZiv complexity, the Hurst exponent and the BDS statistics. We compare this study with other time series including stock prices and deterministic systems. We conclude that the IBOVESPA is a linear stochastic process that exhibits the phenomenon of persistence, thatis, it has long term memory. The stocks are described by nonlinear stochastic processes making it impossible to be simulated with deterministic models such as the usual neural networks architectures.
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| first_indexed | 2024-03-09T01:39:47Z |
| format | Article |
| id | doaj.art-1d72309599524812a592c6c431664428 |
| institution | Directory Open Access Journal |
| issn | 1413-8050 1980-5330 |
| language | Portuguese |
| last_indexed | 2024-03-09T01:39:47Z |
| publishDate | 2002-02-01 |
| publisher | Universidade de São Paulo |
| record_format | Article |
| series | Economia Aplicada |
| spelling | doaj.art-1d72309599524812a592c6c4316644282023-12-08T16:15:05ZporUniversidade de São PauloEconomia Aplicada1413-80501980-53302002-02-0161Dynamic parameters for Brazilian financial time seriesGerson Francisco0Cláudio Paiva1Rogerio Rosenfeld2Universidade Estadual Paulista - Instituto de Fisica TeóricaAnalitix Soluções em Finanças LtdaUniversidade Estadual Paulista. Instituto de Fisica Teórica In this study we analyse a Brazilian stock index called IBOVESPA using techniques from dynamical systems theory and stochastic processes. We discuss the Lyapunov exponent, the correlation dimension, the LempelZiv complexity, the Hurst exponent and the BDS statistics. We compare this study with other time series including stock prices and deterministic systems. We conclude that the IBOVESPA is a linear stochastic process that exhibits the phenomenon of persistence, thatis, it has long term memory. The stocks are described by nonlinear stochastic processes making it impossible to be simulated with deterministic models such as the usual neural networks architectures. https://www.revistas.usp.br/ecoa/article/view/219890deterministicstochasticreconstruction in phase spacecomplexity |
| spellingShingle | Gerson Francisco Cláudio Paiva Rogerio Rosenfeld Dynamic parameters for Brazilian financial time series Economia Aplicada deterministic stochastic reconstruction in phase space complexity |
| title | Dynamic parameters for Brazilian financial time series |
| title_full | Dynamic parameters for Brazilian financial time series |
| title_fullStr | Dynamic parameters for Brazilian financial time series |
| title_full_unstemmed | Dynamic parameters for Brazilian financial time series |
| title_short | Dynamic parameters for Brazilian financial time series |
| title_sort | dynamic parameters for brazilian financial time series |
| topic | deterministic stochastic reconstruction in phase space complexity |
| url | https://www.revistas.usp.br/ecoa/article/view/219890 |
| work_keys_str_mv | AT gersonfrancisco dynamicparametersforbrazilianfinancialtimeseries AT claudiopaiva dynamicparametersforbrazilianfinancialtimeseries AT rogeriorosenfeld dynamicparametersforbrazilianfinancialtimeseries |