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)
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Open Access publications from Universidad Carlos III de Madrid
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