IDEAS home Printed from https://ideas.repec.org/p/hkg/wpaper/0813.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.info.gov.hk/hkma/eng/research/working/pdf/HKMAWP13_08_full.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. repec:zbw:bofrdp:2004_018 is not listed on IDEAS
    5. 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.
    6. Mr. Armando Méndez Morales & Jose Giancarlo Gasha, 2004. "Identifying Threshold Effects in Credit Risk Stress Testing," IMF Working Papers 2004/150, International Monetary Fund.
    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

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Albert, Jose Ramon & Ng, Thiam Hee, 2012. "Assessing the Resilience of ASEAN Banking Systems: The Case of the Philippines," Working Papers on Regional Economic Integration 93, Asian Development Bank.
    2. 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.
    3. 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".
    4. 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.
    5. 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.
    6. Andrew McKenna & Rhys Bidder, 2014. "Robust Stress Testing," 2014 Meeting Papers 853, Society for Economic Dynamics.
    7. Dominique Guegan & Bertrand K. Hassani & Kehan Li, 2015. "The Spectral Stress VaR (SSVaR)," Post-Print halshs-01169537, HAL.
    8. 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.
    9. Sergio Edwin Torrico Salamanca, 2014. "Macro credit scoring as a proposal for quantifying credit risk," Investigación & Desarrollo, Universidad Privada Boliviana, vol. 2(1), pages 42-64.
    10. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Vazquez, Francisco & Tabak, Benjamin M. & Souto, Marcos, 2012. "A macro stress test model of credit risk for the Brazilian banking sector," Journal of Financial Stability, Elsevier, vol. 8(2), pages 69-83.
    3. Abdelaziz Rouabah, 2007. "Mesure de la vulnérabilité du secteur bancaire luxembourgeois," BCL working papers 24, Central Bank of Luxembourg.
    4. Bo Jiang & Bruce Philp & Zhongmin Wu, 2018. "Macro stress testing in the banking system of China," Journal of Banking Regulation, Palgrave Macmillan, vol. 19(4), pages 287-298, November.
    5. Grigori Fainstein & Igor Novikov, 2011. "The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 20-45, June.
    6. Javier Gómez Pineda, 2004. "A Framework for Macroeconomic Stability in Emerging Market Economies," Borradores de Economia 320, Banco de la Republica de Colombia.
    7. 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.
    8. Arie Preminger & Uri Ben-zion & David Wettstein, 2007. "The extended switching regression model: allowing for multiple latent state variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 457-473.
    9. Mandler, Martin, 2007. "The Taylor rule and interest rate uncertainty in the U.S. 1955-2006," MPRA Paper 2340, University Library of Munich, Germany.
    10. Claudio Borio & Mathias Drehmann, 2011. "Toward an Operational Framework for Financial Stability: “Fuzzy” Measurement and Its Consequences," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Alfaro (ed.),Financial Stability, Monetary Policy, and Central Banking, edition 1, volume 15, chapter 4, pages 063-123, Central Bank of Chile.
    11. Stéphane Albert & Hervé Alexandre, 2018. "Banks’ earnings: Empirical evidence of the influence of economic and financial market factors," Review of Financial Economics, John Wiley & Sons, vol. 36(2), pages 97-116, April.
    12. 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.
    13. Martin Èihák, 2005. "Stress Testing of Banking Systems (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 55(9-10), pages 418-440, September.
    14. repec:bgu:wpaper:0605 is not listed on IDEAS
    15. Juan Solorzano-Margain & Serafin Martinez-Jaramillo & Fabrizio Lopez-Gallo, 2013. "Financial contagion: extending the exposures network of the Mexican financial system," Computational Management Science, Springer, vol. 10(2), pages 125-155, June.
    16. Morone, Marco & Cornaglia, Anna, 2010. "An econometric model to quantify benchmark downturn LGD on residential mortgages," MPRA Paper 25588, University Library of Munich, Germany.
    17. Anahí Montserrat Torres Oliveros & Arturo Morales Castro & José Luis Alcalá Villarreal, 2017. "Análisis del impacto por nueva regulación a las sofomes mexicanas," Revista de Economía del Caribe 17151, Universidad del Norte.
    18. Kalliovirta, Leena & Meitz, Mika & Saikkonen, Pentti, 2016. "Gaussian mixture vector autoregression," Journal of Econometrics, Elsevier, vol. 192(2), pages 485-498.
    19. Mr. Rodolfo Maino & Mr. Kalin I Tintchev, 2012. "From Stress to Costress: Stress Testing Interconnected Banking Systems," IMF Working Papers 2012/053, International Monetary Fund.
    20. Jesús Clemente & María Dolores Gadea & Antonio Montañés & Marcelo Reyes, 2017. "Structural Breaks, Inflation and Interest Rates: Evidence from the G7 Countries," Econometrics, MDPI, vol. 5(1), pages 1-17, February.
    21. Dovern, Jonas & Meier, Carsten-Patrick & Vilsmeier, Johannes, 2010. "How resilient is the German banking system to macroeconomic shocks?," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1839-1848, August.

    More about this item

    Keywords

    Stress test; Hong Kong Banking; Credit risk; Mixture autoregressive models; Macroeconomic shocks; Value-at-risk.;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hkg:wpaper:0813. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Simon Chan (email available below). General contact details of provider: https://edirc.repec.org/data/magovhk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.