Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach
44 Pages Posted: 4 May 2017
Date Written: May 2, 2017
We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.
Keywords: Partial Least Squares, Principal Component Analysis, Financial Stress Index, Out-Of-Sample Forecast, RRMSPE, DMW Statistics
JEL Classification: C38, C53, C55, E44, E47, G01, G17
Suggested Citation: Suggested Citation