Arbeitspapier
Testing the martingale difference hypothesis using neural network approximations
The 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.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 601
- Classification
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
- Subject
-
Martingale difference hypothesis , Neural networks , Boosting
Martingale
Neuronale Netze
Nichtparametrisches Verfahren
Monte-Carlo-Methode
Schätztheorie
- Event
-
Geistige Schöpfung
- (who)
-
Kapetanios, George
Blake, Andrew P.
- Event
-
Veröffentlichung
- (who)
-
Queen Mary University of London, Department of Economics
- (where)
-
London
- (when)
-
2007
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Kapetanios, George
- Blake, Andrew P.
- Queen Mary University of London, Department of Economics
Time of origin
- 2007
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