Detecting Linear and Nonlinear Dependence in Stock Returns: New Methods Derived from Chaos Theory
Interest in the relevance of nonlinear dynamics to fields such as finance and economics has spurred the development of new methods of analysis for time series data. Early tests for chaos led to problems when applied to financial and economic data. This motivated development of the BDS family of stat...
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| Veröffentlicht in: | Journal of business finance & accounting 1996-12, Vol.23 (9-10), p.1357-1377 |
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| container_end_page | 1377 |
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| container_issue | 9-10 |
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| container_title | Journal of business finance & accounting |
| container_volume | 23 |
| creator | Gilmore, Claire G. |
| description | Interest in the relevance of nonlinear dynamics to fields such as finance and economics has spurred the development of new methods of analysis for time series data. Early tests for chaos led to problems when applied to financial and economic data. This motivated development of the BDS family of statistics to test for nonlinearity generally. More recently, another method of analysis has been introduced into the scientific literature. It uses a test for chaos which is relatively simple and appropriate for financial data. A quantitative version of this test is developed here and is used to analyze stock return data. |
| doi_str_mv | 10.1111/1468-5957.00084 |
| format | Article |
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| ispartof | Journal of business finance & accounting, 1996-12, Vol.23 (9-10), p.1357-1377 |
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| language | eng |
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| source | RePEc; EBSCOhost Business Source Complete; Access via Wiley Online Library |
| subjects | Accounting chaos Chaos theory Datasets Efficient markets heteroscedasticity Hypotheses Linear models nonlinearity Random variables Rates of return Securities prices Stock exchange Stock exchanges Stock prices Stock returns Time series |
| title | Detecting Linear and Nonlinear Dependence in Stock Returns: New Methods Derived from Chaos Theory |
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