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
This study proposes weighted Lp-norm support vector regression (WLp-SVR) robust against both noise and outliers. Using Lp-norm enables WLp-SVR to select financial variables for creating the financial conditions index (FCI) reliably. We use a weighted sum method to construct a Chinese FCI. We then evaluate our FCI’s ability to forecast real output based on the Granger-causality and Engle-Granger cointegration tests. Regression results show that our FCI has strong predictive power in forecasting real output, indicating that our FCI is a potential leading indicator of the future state of the economy.
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