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Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach


  • Hyeongwoo Kim
  • Kyunghwan Ko


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.

Suggested Citation

  • Hyeongwoo Kim & Kyunghwan Ko, 2017. "Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach," Auburn Economics Working Paper Series auwp2017-03, Department of Economics, Auburn University.
  • Handle: RePEc:abn:wpaper:auwp2017-03

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    References listed on IDEAS

    1. Cipollini, A. & Kapetanios, G., 2009. "Forecasting financial crises and contagion in Asia using dynamic factor analysis," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 188-200, March.
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    3. Girton, Lance & Roper, Don, 1977. "A Monetary Model of Exchange Market Pressure Applied to the Postwar Canadian Experience," American Economic Review, American Economic Association, vol. 67(4), pages 537-548, September.
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    6. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency Crashes in Emerging Markets: Empirical Indicators," Center for International and Development Economics Research (CIDER) Working Papers 233424, University of California-Berkeley, Department of Economics.
    7. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    8. Hyeongwoo Kim & Hyun Hak Kim & Wen Shi, 2015. "Forecasting Financial Stress Indices in Korea: A Factor Model Approach," Working Papers 2015-30, Economic Research Institute, Bank of Korea.
    9. Hali J. Edison, 2003. "Do indicators of financial crises work? An evaluation of an early warning system," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 8(1), pages 11-53.
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    12. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
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    More about this item


    Partial Least Squares; Principal Component Analysis; Financial Stress Index; Out-of-Sample Forecast; RRMSPE;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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