Hui, Yongchang and Wong, WingKeung and Bai, Zhidong and Zhu, Zhenzhen (2016): A New Nonlinearity Test to Circumvent the Limitation of Volterra Expansion with Applications.

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Abstract
In this paper, we propose a quick, efficient, and easy method to examine whether a time series Yt possesses any nonlinear feature. The advantage of our proposed nonlinearity test is that it is not required to know the exact nonlinear features and the detailed nonlinear forms of Yt. We find that our proposed test can be used to detect any nonlinearity for the variable being examined and detect GARCH models in the innovations. It can also be used to test whether the hypothesized model, including linear and nonlinear, to the variable being examined is appropriate as long as the residuals of the model being used can be estimated. Our simulation study shows that our proposed test is stable and powerful. We apply our proposed statistic to test whether there is any nonlinear feature in the sunspot data and whether the S&P 500 index follows a random walk model. The conclusion drawn from our proposed test is consistent those from other tests.
Item Type:  MPRA Paper 

Original Title:  A New Nonlinearity Test to Circumvent the Limitation of Volterra Expansion with Applications 
Language:  English 
Keywords:  Nonlinearity, Ustatistics, Volterra expansion, sunspots, efficient market 
Subjects:  C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General G  Financial Economics > G1  General Financial Markets > G10  General 
Item ID:  75216 
Depositing User:  WingKeung Wong 
Date Deposited:  22 Nov 2016 05:26 
Last Modified:  02 Oct 2019 18:56 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/75216 