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Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach

Author

Listed:
  • Hyeongwoo Kim

    () (Department of Economics, Auburn University)

  • Kyunghwan Ko

    () (Economic Research Team, Jeju Branch, The Bank of Korea)

Abstract

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: Partial Least Squares Factor Model Approach," Working Papers 2017-14, Economic Research Institute, Bank of Korea.
  • Handle: RePEc:bok:wpaper:1714
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    File URL: http://papers.bok.or.kr/RePEc_attach/wpaper/english/wp-2017-14.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. James Barth & Sunghoon Joo & Hyeongwoo Kim & Kang Bok Lee & Stevan Maglic & Xuan Shen, 2018. "Forecasting Net Charge-Off Rates of Banks: A PLS Approach," Auburn Economics Working Paper Series auwp2018-03, Department of Economics, Auburn University.

    More about this item

    Keywords

    Partial least squares; Principal component analysis; Financial stress index; Out-of-sample forecast; RRMSPE; DMW statistics;

    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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