IDEAS home Printed from https://ideas.repec.org/p/nsr/niesrd/154.html

A Radial Basis Function Artificial Neural Network Test for ARCH

Author

Abstract

We propose a test for ARCH that uses a radial basis function artificial neural network. It outperforms alternative neural network tests in a variety of Monte Carlo experiments.

Suggested Citation

  • Andrew Blake, 1999. "A Radial Basis Function Artificial Neural Network Test for ARCH," National Institute of Economic and Social Research (NIESR) Discussion Papers 154, National Institute of Economic and Social Research.
    • Handle: RePEc:nsr:niesrd:154
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. George Kapetanios & Andrew P. Blake, 2007. "Testing the Martingale Difference Hypothesis Using Neural Network Approximations," Working Papers 601, Queen Mary University of London, School of Economics and Finance.
    2. Blake, Andrew P. & Kirsanova, Tatiana, 2004. "A note on timeless perspective policy design," Economics Letters, Elsevier, vol. 85(1), pages 9-16, October.
    3. Jensen, Christian, 2016. "Discretion Rather than Rules? Binding Commitments versus Discretionary Policymaking," MPRA Paper 76838, University Library of Munich, Germany.
    4. Benigno, Pierpaolo & Woodford, Michael, 2012. "Linear-quadratic approximation of optimal policy problems," Journal of Economic Theory, Elsevier, vol. 147(1), pages 1-42.
    5. George A. Waters, 2015. "Careful Price Level Targeting," International Symposia in Economic Theory and Econometrics, in: William A. Barnett & Fredj Jawadi (ed.), Monetary Policy in the Context of the Financial Crisis: New Challenges and Lessons, volume 24, pages 29-40, Emerald Publishing Ltd.
    6. Hong, Seung Hyun & Phillips, Peter C. B., 2010. "Testing Linearity in Cointegrating Relations With an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 96-114.
    7. Nikoleta Anesti & Eleni Kalamara & George Kapetanios, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    8. Di Bartolomeo, Giovanni & Di Pietro, Marco & Giannini, Bianca, 2016. "Optimal monetary policy in a New Keynesian model with heterogeneous expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 373-387.
    9. Brendon, Charles & Ellison, Martin, 2018. "Time-consistently undominated policies," LSE Research Online Documents on Economics 87176, London School of Economics and Political Science, LSE Library.
    10. Andrew P. Blake & George Kapetanios, 2003. "Pure Significance Tests of the Unit Root Hypothesis Against Nonlinear Alternatives," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(3), pages 253-267, May.
    11. Blake, Andrew P. & Kapetanios, George, 2007. "Testing for ARCH in the presence of nonlinearity of unknown form in the conditional mean," Journal of Econometrics, Elsevier, vol. 137(2), pages 472-488, April.
    12. Paez-Farrell, Juan, 2011. "Timeless perspective versus discretionary policymaking when the degree of inflation persistence is unknown," Economic Modelling, Elsevier, vol. 28(6), pages 2432-2438.
    13. George Kapetanios & Andrew P. Blake, 2007. "Boosting Estimation of RBF Neural Networks for Dependent Data," Working Papers 588, Queen Mary University of London, School of Economics and Finance.
    14. Lee Tae-Hwy, 2001. "Neural Network Test and Nonparametric Kernel Test for Neglected Nonlinearity in Regression Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(4), pages 1-15, January.
    15. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    16. Andrew P. Blake & George Kapetanios, 2007. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 807-826, November.
    17. Yen-Ming Chiang & Wei-Guo Cheng & Fi-John Chang, 2012. "A hybrid artificial neural network-based agri-economic model for predicting typhoon-induced losses," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 769-787, September.
    18. Kanazawa, Nobuyuki, 2020. "Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks," Journal of Macroeconomics, Elsevier, vol. 64(C).
    19. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nsr:niesrd:154. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Library & Information Manager (email available below). General contact details of provider: https://edirc.repec.org/data/niesruk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.