Financial Conditions Index Construction Through Weighted Lp-Norm Support Vector Regression
Ya-Fen Ye*,**, Yue-Xiang Jiang*, Yuan-Hai Shao**, and Chun-Na Li**
*College of Economics, Zhejiang University
38 Zheda Road, Hangzhou 310027, China
**Zhijiang College, Zhejiang University of Technology
182 Zhijiang Road, Hangzhou 310024, China
-  J. Hatzius, P. Hooper, F. S. Mishkin, K. L. Schoenholtz, M. W. Watson, “Financial Conditions Indexes: A Fresh Look after the Financial Crisis,” Working Paper, National Bureau of Economic Research, 2010.
-  C. Goodhart and B. Hofmann, Financial Variables and the Conduct of Monetary Policy, Sveriges Riksbank, 2000.
-  Y. Wang, B. Wang, X. Y. Zhang, “A new application of the support vector regression on the construction of financial conditions index to CPI prediction,” Procedia Computer Science, Vol.9, pp. 1263-1272, 2012.
-  M. Song, C. Breneman, J. Bi, N. Sukumar, K. Bennett, S. Cramer, N. Tugcu, “Prediction of protein retention times in anion-exchange chromatography systems using support vector regression,” J. of Chemical Information and Computer Sciences, Vol.42, pp. 1347-1357, 2002.
-  Ya-Fen Ye, Hui Cao, Lan Bai, Zhen Wang, Yuan-Hai Shao, “Exploring determinants of inflation in China based on L1- ε- twin support vector regression,” Procedia Computer Science, Vol.17, pp. 514-522, 2013.
-  X. Peng, D. Xu, “A local information-based feature-selection algorithm for data regression,” Pattern Recognition, Vol.46, pp. 2519-2530, 2013.
-  C. Cortes, V. N. Vapnik, “Support vector networks,” Mach Learn, Vol.20, pp. 273-297, 1995.
-  V. Vapnik, “Statistical learning theory,” Wiley, New York, 1998.
-  V. Vapnik, “The Nature of Statistical Learning Theory,” 2nd Ed., Springer-Verlag, New York, 2000.
-  C. H. Zhang, D. W. Li, and J. Y. Tan, “The support vector regression with adaptive norms,” Procedia Computer Science, Vol.18, pp. 1730-1736, 2013.
-  W. J. Chen and Y. J. Tian, “Lp--norm proximal support vector machine and its applications,” Procedia Computer Science, Vol.1, pp. 2417-2423, 2012.
-  P. Bradley, O. Mangasarian, and W. Street, “Feature selection via mathematical programming,” INFORMS J. on Computing, doi:10.1287/ijoc.10.2.209, 1998.
-  C. W. J. Granger, “Investigating causal relations by econometric and cross-spectral methods,” Econometrica, Vol.37, pp. 424-438, 1969.
-  Y. H. Shao, C. H. Zhang, Z. M. Yang, L. Jing, N. Y. Deng, “An ε-twin support vector machine for regression,” Neural Computing and Applications, Vol.23, pp. 175-185, 2013.
-  J. A. K. Suykens, J. D. Brabanter, L. Lukas, and J. Vandewalle, “Weighted least squares support vector machines: robustness and sparse approximation,” Neurocomputing, Vol.48, pp. 85-105, 2002.
-  H. S. Tang, S. T. Xue, R. Chen, T. Sato, “Online weighted LS-SVM for hysteretic structural system identification,” Engineering Structures, Vol.28, No.12, pp. 1728-1735, 2006.
-  Y. F. Ye, Y. H. Shao, and W. J. Chen, “Comparing inflation forecasts using an ε-wavelet twin support vector regression,” J. of Information Computational Science, Vol.10, pp. 2041-2049, 2013.
-  W. Cui and X. Yan, “Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR,” Chemometrics and Intelligent Laboratory Systems, Vol.98, pp. 130-135, 2009.
-  D. Dickey and W. Fuller, “The likelihood ratio statistics for autoregressive time series with a unit root,” Econometrica, Vol.49, pp. 1057-1072, 1981.
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