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Stress Testing Banks' Credit Risk Using Mixture Vector Autoregressive Models

Author

Listed:
  • Tom Pak-wing Fong

    (Research Department, Hong Kong Monetary Authority)

  • Chun-shan Wong

    (Department of Finance, The Chinese University of Hong Kong)

Abstract

This paper estimates macroeconomic credit risk of banks¡¦ loan portfolio based on a class of mixture vector autoregressive models. Such class of models can differentiate distributions of default rates and macroeconomic conditions for different market situations and can capture their dynamics evolving over time, including the feedback effect from an increase in fragility back to the macroeconomy. These extensions can facilitate the evaluation of credit risks of loan portfolio based on different credit loss distributions.

Suggested Citation

  • Tom Pak-wing Fong & Chun-shan Wong, 2008. "Stress Testing Banks' Credit Risk Using Mixture Vector Autoregressive Models," Working Papers 0813, Hong Kong Monetary Authority.
    • Handle: RePEc:hkg:wpaper:0813
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    References listed on IDEAS

    as
    1. Markku Lanne & Pentti Saikkonen, 2003. "Modeling the U.S. Short-Term Interest Rate by Mixture Autoregressive Processes," Journal of Financial Econometrics, Oxford University Press, vol. 1(1), pages 96-125.
    2. Jim Wong & Ka-Fai Choi & Tom Pak-Wing Fong, 2008. "A Framework for Stress Testing Banks’ Credit Risk," Palgrave Macmillan Studies in Banking and Financial Institutions, in: Hans Genberg & Cho-Hoi Hui (ed.), The Banking Sector in Hong Kong, chapter 11, pages 240-260, Palgrave Macmillan.
    3. Mr. Armando Méndez Morales & Jose Giancarlo Gasha, 2004. "Identifying Threshold Effects in Credit Risk Stress Testing," IMF Working Papers 2004/150, International Monetary Fund.
    4. Vance L. Martin, 1992. "Threshold Time Series Models As Multimodal Distribution Jump Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(1), pages 79-94, January.
    5. Virolainen, Kimmo, 2004. "Macro stress testing with a macroeconomic credit risk model for Finland," Research Discussion Papers 18/2004, Bank of Finland.
    6. Markku Lanne, 2006. "Nonlinear dynamics of interest rate and inflation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1157-1168, December.
    7. Berchtold, Andre, 2003. "Mixture transition distribution (MTD) modeling of heteroscedastic time series," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 399-411, January.
    8. Marco Sorge, 2004. "Stress-testing financial systems: an overview of current methodologies," BIS Working Papers 165, Bank for International Settlements.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Documents de travail du Centre d'Economie de la Sorbonne 15052, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    2. Dominique Gu�gan & Bertrand Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Working Papers 2015:17, Department of Economics, University of Venice "Ca' Foscari".
    3. Paolo Guarda & Abdelaziz Rouabah & John Theal, 2011. "An MVAR Framework to Capture Extreme Events in Macroprudential Stress Tests," BCL working papers 63, Central Bank of Luxembourg.
    4. Abdelaziz Rouabah & John Theal, 2010. "Stress testing: The impact of shocks on the capital needs of the Luxembourg banking sector," BCL working papers 47, Central Bank of Luxembourg.
    5. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01169537, HAL.
    6. Sergio Edwin Torrico Salamanca, 2014. "Macro credit scoring como propuesta para cuantificar el riesgo de crédito," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 2(14), pages 42-63.
    7. Miora Rakotonirainy & Jean Razafindravonona & Christian Rasolomanana, 2020. "Macro Stress Testing Credit Risk: Case of Madagascar Banking Sector," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(2), pages 199-218.
    8. Jose Ramon Albert & Thiam Hee Ng, 2012. "Assessing the Resilience of ASEAN Banking Systems: The Case of the Philippines," Working Papers on Regional Economic Integration 93, Asian Development Bank.
    9. Andrew McKenna & Rhys Bidder, 2014. "Robust Stress Testing," 2014 Meeting Papers 853, Society for Economic Dynamics.
    10. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Post-Print halshs-01169537, HAL.
    11. Costeiu, Adrian & Neagu, Florian, 2013. "Bridging the banking sector with the real economy: a financial stability perspective," Working Paper Series 1592, European Central Bank.
    12. Alfred Wong & Tom Fong, 2013. "Gauging the Safehavenness of Currencies," Working Papers 132013, Hong Kong Institute for Monetary Research.

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    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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