Why Frequency Matters for Unit Root Testing in Financial Time Series

2012 ◽  
Vol 30 (3) ◽  
pp. 351-357 ◽  
Author(s):  
H. Peter Boswijk ◽  
Franc Klaassen
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Financial Time Series ◽  
Financial Time ◽  
Unit Root Testing

LONG MEMORY IN FINANCIAL TIME SERIES DATA WITH NON-GAUSSIAN DISTURBANCES

2003 ◽  
Vol 06 (02) ◽  
pp. 119-134 ◽  
Author(s):  
LUIS A. GIL-ALANA
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Unit Root ◽  
Time Series Data ◽  
Financial Time Series ◽  
Critical Values ◽  
Series Data ◽  
Finite Sample ◽  
Financial Time ◽  
Non Gaussian

In this article we propose the use of a version of the tests of Robinson [32] for testing unit and fractional roots in financial time series data. The tests have a standard null limit distribution and they are the most efficient ones in the context of Gaussian disturbances. We compute finite sample critical values based on non-Gaussian disturbances and the power properties of the tests are compared when using both, the asymptotic and the finite-sample (Gaussian and non-Gaussian) critical values. The tests are applied to the monthly structure of several stock market indexes and the results show that the if the underlying I(0) disturbances are white noise, the confidence intervals include the unit root; however, if they are autocorrelated, the unit root is rejected in favour of smaller degrees of integration. Using t-distributed critical values, the confidence intervals for the non-rejection values are generally narrower than with the asymptotic or than with the Gaussian finite-sample ones, suggesting that they may better describe the time series behaviour of the data examined.

Testing for random coefficient autoregressive and stochastic unit root models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Daisuke Nagakura
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Autoregressive Model ◽  
Financial Time Series ◽  
Random Coefficient ◽  
Financial Time ◽  
Root Model ◽  
Lagrange Multiplier Tests ◽  
Unit Root Models ◽  
Random Coefficient Autoregressive

AbstractThe random coefficient autoregressive model has been utilized for modeling financial time series because it possesses features that are often observed in financial time series. When the mean of the random coefficient is one, it is called the stochastic unit root model. This paper proposes two Lagrange multiplier tests for the null hypotheses of random coefficient autoregressive and stochastic unit root models against a more general model. We apply our Lagrange multiplier tests to several stock index data, and find that the stochastic unit root model is rejected, whereas the random coefficient autoregressive model is not. This result indicates that it is important to check the validity of the stochastic unit root model prior to applying it to financial time series data, which may be better modeled by the random coefficient autoregressive model with the mean being not equal to one.

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