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Forecasting Financial Market Vulnerability in the U.S.: A Factor Model Approach

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  • Hyeongwoo Kim
  • Wen Shi

Abstract

This paper presents a factor-based forecasting model for the financial market vulnerability in the U.S. We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data to out-of-sample forecast the Cleveland Financial Stress Index. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability for short-term forecast horizons, which is a desirable feature since financial crises often come to a surprise realization. Interestingly, the first common factor, which plays a key role in predicting the financial vulnerability index, seems to be more closely related with real activity variables rather than nominal variables. The recursive and the rolling window approaches with a 50% split point perform similarly well.

Suggested Citation

  • Hyeongwoo Kim & Wen Shi, 2015. "Forecasting Financial Market Vulnerability in the U.S.: A Factor Model Approach," Auburn Economics Working Paper Series auwp2015-04, Department of Economics, Auburn University.
  • Handle: RePEc:abn:wpaper:auwp2015-04
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    File URL: http://cla.auburn.edu/econwp/Archives/2015/2015-04.pdf
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    References listed on IDEAS

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    1. El-Shagi, M. & Knedlik, T. & von Schweinitz, G., 2013. "Predicting financial crises: The (statistical) significance of the signals approach," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 76-103.
    2. Christian B. Mulder & Matthieu Bussière, 1999. "External Vulnerability in Emerging Market Economies; How High Liquidity Can Offset Weak Fundamentals and the Effects of Contagion," IMF Working Papers 99/88, International Monetary Fund.
    3. Mikhail V. Oet & John M. Dooley & Stephen J. Ong, 2015. "The Financial Stress Index: Identification of Systemic Risk Conditions," Risks, MDPI, Open Access Journal, vol. 3(3), pages 1-25, September.
    4. Christensen, Ian & Li, Fuchun, 2014. "Predicting financial stress events: A signal extraction approach," Journal of Financial Stability, Elsevier, vol. 14(C), pages 54-65.
    5. Miguel Morales & Dairo Estrada, 2010. "A financial stability index for Colombia," Annals of Finance, Springer, vol. 6(4), pages 555-581, October.
    6. Cevik, Emrah Ismail & Dibooglu, Sel & Kenc, Turalay, 2013. "Measuring financial stress in Turkey," Journal of Policy Modeling, Elsevier, vol. 35(2), pages 370-383.
    7. Kevin L. Kliesen & Douglas C. Smith, 2010. "Measuring financial market stress," Economic Synopses, Federal Reserve Bank of St. Louis.
    8. Miroslav Misina & Greg Tkacz, 2009. "Credit, Asset Prices, and Financial Stress," International Journal of Central Banking, International Journal of Central Banking, vol. 5(4), pages 95-122, December.
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    Cited by:

    1. 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.

    More about this item

    Keywords

    Financial Stress Index; Method of the Principal Component; Out-of-Sample Forecast; Ratio of Root Mean Square Prediction Error; Diebold-Mariano-West Statistic;

    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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