Nonlinear Cointegration Analysis Based on Support Vector Machines and Its Application to the Connection between Financial Markets
Chuanhe Shen, Zhongwen Liu, Ying Li
Available Online July 2013.
- https://doi.org/10.2991/cse.2013.28How to use a DOI?
- non-stationary time series; nonlinear cointegration analysis; support vector machine; neural network; the connection between financial markets
- With the purpose of analyzing non-stationary time series, this paper introduced a novel nonlinear cointegration discriminate analysis by employing support vector machines to overcome existing limitations of two methods, that is, the statistical approach and the neural network used by the nonlinear cointegration theory. The proposed nonlinear cointegration discriminate analysis can effectively address the key step in the nonlinear cointegration test. Then, the application of the innovated method in the investigation of the connection between financial markets was explored. The empirical analysis demonstrates that the support vector machine is significant in investigating the money demand function and its stability and has advantages in dealing with nonlinear cointegration relations among different financial variables and estimating nonlinear function over the two methods above.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chuanhe Shen AU - Zhongwen Liu AU - Ying Li PY - 2013/07 DA - 2013/07 TI - Nonlinear Cointegration Analysis Based on Support Vector Machines and Its Application to the Connection between Financial Markets BT - 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013) PB - Atlantis Press SP - 119 EP - 123 SN - 1951-6851 UR - https://doi.org/10.2991/cse.2013.28 DO - https://doi.org/10.2991/cse.2013.28 ID - Shen2013/07 ER -