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Forecasting Financial Vulnerability in the US: 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, measured by changes in the Cleveland Financial Stress Index (CFSI). We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data in order to out-of-sample forecast the CFSI. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability at least for the 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. We also present a binary choice version factor model that estimates the probability of the high stress regime successfully.

Suggested Citation

  • Hyeongwoo Kim & Wen Shi, 2016. "Forecasting Financial Vulnerability in the US: A Factor Model Approach," Auburn Economics Working Paper Series auwp2016-15, Department of Economics, Auburn University.
  • Handle: RePEc:abn:wpaper:auwp2016-15
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    File URL: http://cla.auburn.edu/econwp/Archives/2016/2016-15.pdf
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    References listed on IDEAS

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    1. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "Varieties of Crises and Their Dates," Introductory Chapters,in: This Time Is Different: Eight Centuries of Financial Folly Princeton University Press.
    2. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    3. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    4. 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.
    5. 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.
    6. Craig S. Hakkio & William R. Keeton, 2009. "Financial stress: what is it, how can it be measured, and why does it matter?," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 5-50.
    7. Evan C Tanner, 2002. "Exchange Market Pressure, Currency Crises, and Monetary Policy; Additional Evidence From Emerging Markets," IMF Working Papers 02/14, International Monetary Fund.
    8. Frankel, Jeffrey & Saravelos, George, 2012. "Can leading indicators assess country vulnerability? Evidence from the 2008–09 global financial crisis," Journal of International Economics, Elsevier, vol. 87(2), pages 216-231.
    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.
    10. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
    11. Carmen M. Reinhart & Kenneth S. Rogoff, 2014. "Recovery from Financial Crises: Evidence from 100 Episodes," American Economic Review, American Economic Association, vol. 104(5), pages 50-55, May.
    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

    Keywords

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

    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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