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Detecting Linear and Nonlinear Dependence in Stock Returns: New Methods Derived from Chaos Theory
Author(s) -
Gilmore Claire G.
Publication year - 1996
Publication title -
journal of business finance and accounting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.282
H-Index - 77
eISSN - 1468-5957
pISSN - 0306-686X
DOI - 10.1111/1468-5957.00084
Subject(s) - nonlinear system , econometrics , chaos theory , economics , chaos (operating system) , stock (firearms) , relevance (law) , statistical hypothesis testing , series (stratigraphy) , financial economics , computer science , mathematics , statistics , chaotic , physics , engineering , management , mechanical engineering , paleontology , computer security , quantum mechanics , law , political science , biology
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.