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Transmission of macroeconomic shocks to risk parameters: Their uses in stress testing

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  • Helder Rojas
  • David Dias

Abstract

In this paper, we are interested in evaluating the resilience of financial portfolios under extreme economic conditions. Therefore, we use empirical measures to characterize the transmission process of macroeconomic shocks to risk parameters. We propose the use of an extensive family of models, called General Transfer Function Models, which condense well the characteristics of the transmission described by the impact measures. The procedure for estimating the parameters of these models is described employing the Bayesian approach and using the prior information provided by the impact measures. In addition, we illustrate the use of the estimated models from the credit risk data of a portfolio.

Suggested Citation

  • Helder Rojas & David Dias, 2020. "Transmission of macroeconomic shocks to risk parameters: Their uses in stress testing," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(3), pages 353-380, May.
  • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:3:p:353-380
    DOI: 10.1002/asmb.2493
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    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. Henry, Jérôme & Zimmermann, Maik & Leber, Miha & Kolb, Markus & Grodzicki, Maciej & Amzallag, Adrien & Vouldis, Angelos & Hałaj, Grzegorz & Pancaro, Cosimo & Gross, Marco & Baudino, Patrizia & Sydow, , 2013. "A macro stress testing framework for assessing systemic risks in the banking sector," Occasional Paper Series 152, European Central Bank.
    4. Dent, Kieran & Westwood, Ben & Segoviano, Miguel, 2016. "Stress testing of banks: an introduction," Bank of England Quarterly Bulletin, Bank of England, vol. 56(3), pages 130-143.
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