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Forecasting Financial Stress Indices in Korea: A Factor Model Approach

Author

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  • Hyeongwoo Kim
  • Wen Shi
  • Hyun Hak Kim
Abstract
We propose factor-based out-of-sample forecast models for Korea's financial stress index and its 4 sub-indices that are developed by the Bank of Korea. We extract latent common factors by employing the method of the principal components for a panel of 198 monthly frequency macroeconomic data after differencing them. We augment an autoregressive-type model of the financial stress index with estimated common factors to formulate out-of-sample forecasts of the index. Our models overall outperform both the stationary and the nonstationary benchmark models in forecasting the financial stress indices for up to 12-month forecast horizons. The first common factor that represents not only financial market but also real activity variables seems to play a dominantly important role in predicting the vulnerability in the financial markets in Korea.

Suggested Citation

  • Hyeongwoo Kim & Wen Shi & Hyun Hak Kim, 2019. "Forecasting Financial Stress Indices in Korea: A Factor Model Approach," Auburn Economics Working Paper Series auwp2019-02, Department of Economics, Auburn University.
    • Handle: RePEc:abn:wpaper:auwp2019-02
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    2. Kim, Hyeongwoo & Ko, Kyunghwan, 2020. "Improving forecast accuracy of financial vulnerability: PLS factor model approach," Economic Modelling, Elsevier, vol. 88(C), pages 341-355.
    3. 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.
    4. Kim, Hyeongwoo & Son, Jisoo, 2024. "What charge-off rates are predictable by macroeconomic latent factors?," Journal of Financial Stability, Elsevier, vol. 74(C).
    5. Hyeongwoo Kim & Wen Shi, 2021. "Forecasting financial vulnerability in the USA: A factor model approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 439-457, April.
    6. Kaelo Ntwaepelo & Grivas Chiyaba, 2022. "Financial Stability Surveillance Tools: Evaluating the Performance of Stress Indices," Economics Discussion Papers em-dp2022-06, Department of Economics, University of Reading.
    7. Tang, Pan & Tang, Tiantian & Lu, Chennuo, 2024. "Predicting systemic financial risk with interpretable machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    8. Youngjin Yun, 2018. "Cross-Border Bank Flows through Foreign Branches: Evidence from Korea," Working Papers 2018-23, Economic Research Institute, Bank of Korea.
    9. Hyeongwoo Kim & Jisoo Son, 2023. "Forecasting Net Charge-Off Rates of Large U.S. Bank Holding Companies using Macroeconomic Latent Factors," Auburn Economics Working Paper Series auwp2023-02, Department of Economics, Auburn University.
    10. Haddou, Samira, 2022. "International financial stress spillovers to bank lending: Do internal characteristics matter?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    11. Jinsoo Lee & Bok-Keun Yu, 2018. "What Drives the Stock Market Comovements between Korea and China, Japan and the US?," Working Papers 2018-2, Economic Research Institute, Bank of Korea.
    12. Young Sik Kim & Ohik Kwon, 2019. "Central Bank Digital Currency and Financial Stability," Working Papers 2019-6, Economic Research Institute, Bank of Korea.
    13. Ohik Kwon & Jaevin Park, 2018. "E-money: Legal Restrictions Theory and Monetary Policy," Working Papers 2018-17, Economic Research Institute, Bank of Korea.
    14. Sung Ho Park, 2018. "Fixed-Rate Loans and the Effectiveness of Monetary Policy," Working Papers 2018-20, Economic Research Institute, Bank of Korea.
    15. Yishuai Tian & Yifan Wu, 2024. "Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN," Mathematics, MDPI, vol. 12(11), pages 1-31, May.

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    More about this item

    Keywords

    Financial Stress Index; Principal Component Analysis; PANIC; In-Sample Fit; Out-of-Sample Forecast; Diebold-Mariano-West Statistic;
    All these keywords.

    JEL classification:

    • 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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