Testing the Martingale Difference Hypothesis Using Neural Network Approximations
AbstractThe martingale difference restriction is an outcome of many theoretical analyses in economics and finance. A large body of econometric literature deals with tests of that restriction. We provide new tests based on radial basis function neural networks. Our work is based on the test design of Blake and Kapetanios (2000, 2003a,b). However, unlike that work we can provide a formal theoretical justification for the validity of these tests using approximation results from Kapetanios and Blake (2007). These results take advantage of the link between the algorithms of Blake and Kapetanios (2000, 2003a,b) and boosting. We carry out a Monte Carlo study of the properties of the new tests and find that they have superior power performance to all existing tests of the martingale difference hypothesis we consider. An empirical application to the S&P500 constituents illustrates the usefulness of our new test.
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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 601.
Date of creation: Jun 2007
Date of revision:
Martingale difference hypothesis; Neural networks; Boosting;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-06-11 (All new papers)
- NEP-CMP-2007-06-11 (Computational Economics)
- NEP-ECM-2007-06-11 (Econometrics)
- NEP-ETS-2007-06-11 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Blake, Andrew P. & Kapetanios, George, 2000.
"A radial basis function artificial neural network test for ARCH,"
Elsevier, vol. 69(1), pages 15-23, October.
- Andrew P Blake & George Kapetanios, 1999. "A Radial Basis Function Artificial Neural Network Test for ARCH," NIESR Discussion Papers 154, National Institute of Economic and Social Research.
- J. Carlos Escanciano & Carlos Velasco, 2003.
"Generalized Spectral Tests For The Martingale Difference Hypothesis,"
Statistics and Econometrics Working Papers
ws035212, Universidad Carlos III, Departamento de Estadística y Econometría.
- Escanciano, J. Carlos & Velasco, Carlos, 2006. "Generalized spectral tests for the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 134(1), pages 151-185, September.
- Manuel A. Dominguez & Ignacio N. Lobato, 2001. "A Consistent Test for the Martingale Difference Hypothesis," Working Papers 0101, Centro de Investigacion Economica, ITAM.
- D. S. Poskitt, 2005. "Autoregressive Approximation in Nonstandard Situations: The Non-Invertible and Fractionally Integrated Cases," Monash Econometrics and Business Statistics Working Papers 16/05, Monash University, Department of Econometrics and Business Statistics.
- Guay, Alain & Guerre, Emmanuel, 2006. "A Data-Driven Nonparametric Specification Test For Dynamic Regression Models," Econometric Theory, Cambridge University Press, vol. 22(04), pages 543-586, August.
- 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.
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