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The Use of Trimming to Improve the Performance of Tests for Nonlinear Serial Dependence with Application to the Australian National Electricity Market

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Abstract

In this article, we build on the results reported in Wild, Hinich and Foster (2008) for the National Electricity Market (NEM) of Australia by testing for episodic nonlinearity in the dynamics governing weekly cycles in spot price time series data. We apply the portmanteau correlation, bicorrelation and tricorrelation tests introduced in Hinich (1996) and the Engle (1982) ARCH LM test to the time series of half hourly spot prices from 7/12/1998 to 29/02/2008. We use trimming to improve the finite sample performance of the various test statistics mentioned above given the presence of significant skewness and leptokurtosis in the source datasets which may adversely affect the convergence properties of the test statistics in finite samples. With trimming, we still find the presence of significant third and fourth order (non-linear) serial dependence in the weekly spot price data, pointing to the presence of �deep� nonlinear structure in this data.

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  • Phillip Wild & Melvin J. Hinich & John Foster, 2008. "The Use of Trimming to Improve the Performance of Tests for Nonlinear Serial Dependence with Application to the Australian National Electricity Market," Discussion Papers Series 367, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uq2004:367
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    File URL: https://economics.uq.edu.au/files/44577/367.pdf
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    1. Ammermann, Peter A. & Patterson, Douglas M., 2003. "The cross-sectional and cross-temporal universality of nonlinear serial dependencies: Evidence from world stock indices and the Taiwan Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 11(2), pages 175-195, April.
    2. John Foster & Melvin J. Hinich & Phillip Wild, 2008. "Randomly Modulated Periodic Signals in Australias National Electricity Market," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 105-130.
    3. Claudio Bonilla & Rafael Romero-Meza & Melvin Hinich, 2007. "GARCH inadequacy for modelling exchange rates: empirical evidence from Latin America," Applied Economics, Taylor & Francis Journals, vol. 39(19), pages 2529-2533.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Kian-Ping Lim & Melvin J. Hinich & Venus Khim-Sen Liew, 2005. "Statistical Inadequacy of GARCH Models for Asian Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 4(3), pages 263-279, December.
    6. repec:ebl:ecbull:v:7:y:2005:i:6:p:1-5 is not listed on IDEAS
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    8. Christopher Brooks & Melvin Hinich, 1998. "Episodic nonstationarity in exchange rates," Applied Economics Letters, Taylor & Francis Journals, vol. 5(11), pages 719-722.
    9. Hinich, Melvin J. & Serletis, Apostolos, 2007. "Episodic Nonlinear Event Detection in the Canadian Exchange Rate," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 68-74, March.
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    11. Hinich, Melvin A. & Wild, Phillip, 2001. "Testing Time-Series Stationarity Against An Alternative Whose Mean Is Periodic," Macroeconomic Dynamics, Cambridge University Press, vol. 5(3), pages 380-412, June.
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    Cited by:

    1. Kian‐Ping Lim & Robert Brooks, 2011. "The Evolution Of Stock Market Efficiency Over Time: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 69-108, February.

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