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Forecasting financial vulnerability in the USA: 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 forecast the CFSI out‐of‐sample. 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 to 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.

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  • 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.
    • Handle: RePEc:wly:jforec:v:40:y:2021:i:3:p:439-457
    DOI: 10.1002/for.2724
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    3. Santino Del Fava & Rangan Gupta & Christian Pierdzioch & Lavinia Rognone, 2023. "Forecasting International Financial Stress: The Role of Climate Risks," Working Papers 202329, University of Pretoria, Department of Economics.

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    More about this item

    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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