IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v64y2024i5d10.1007_s10614-023-10545-6.html
   My bibliography  Save this article

Which User-Friendly Model is the Best for BASEL-III? An Emerging Market Study

Author

Listed:
  • Sharif Mozumder

    (University of Dhaka)

  • Mohammad Zoynul Abedin

    (Swansea University)

  • Raad Lalon

    (University of Dhaka)

  • Amjad Hossain

    (University of Chittagong)

Abstract

This paper explores backtesting Value-at-Risk (VaR) and Expected Shortfall (ES) considering ten standard and extended tests in the context of non-technical individual investors trading equities of twenty selected commercial banks listed at the Dhaka Stock Exchange (DSE) using their daily share prices for 11 years (from 2010 to 2020). Following a significant gap in the literature on investigating the efficacy of user-friendly models in quantifying the market risk of banks in emerging economies, this paper adopted four user-friendly models that are relatively straightforward to understand and interpret and are considered representatives of zero, -one, -two, and -three parametric families of all risk models in the literature specifically for the emerging economy of Bangladesh. The popular RiskMetrics™ risk forecast model of JPMorgan, sweeping the world as the most user-friendly conditional alternative to unconditional Gaussian risk forecasts under the framework of VaR, is found not to be adequate under the framework of ES that was recently recommended by Basel-III. The joint score value-based comparison finds the historical simulation (HS) model as the most appropriate model in Bangladesh when models are assessed under a practical user-friendly implementation design. Under this design the Trust Bank Ltd. (TBL), the bank managed and operated by Bangladesh Military, qualifies as the most investor-friendly bank in terms of causing the least frustration to its equity investors over 2010–2020. Overall, augmenting earlier studies in the literature that are mostly for developed markets and are mostly without any ES back-test, we find that the user-friendly model HS is still successful in quantifying market risk over the globe since its relative usefulness gets well established through recent back-tests of VaR and ES in emerging market too.

Suggested Citation

  • Sharif Mozumder & Mohammad Zoynul Abedin & Raad Lalon & Amjad Hossain, 2024. "Which User-Friendly Model is the Best for BASEL-III? An Emerging Market Study," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 3049-3086, November.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:5:d:10.1007_s10614-023-10545-6
    DOI: 10.1007/s10614-023-10545-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10545-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-023-10545-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    2. Atilgan, Yigit & Bali, Turan G. & Demirtas, K. Ozgur & Gunaydin, A. Doruk, 2020. "Left-tail momentum: Underreaction to bad news, costly arbitrage and equity returns," Journal of Financial Economics, Elsevier, vol. 135(3), pages 725-753.
    3. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    4. Thomas J. Linsmeier & Neil D. Pearson, 1996. "Risk Measurement: An Introduction to Value at Risk," Finance 9609004, University Library of Munich, Germany.
    5. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
    6. Alex YiHou Huang, 2009. "A value-at-risk approach with kernel estimator," Applied Financial Economics, Taylor & Francis Journals, vol. 19(5), pages 379-395.
    7. Ergys Islamaj & M. Ayhan Kose & Franziska L. Ohnsorge & Lei Sandy Ye, 2020. "Explaining Recent Investment Weakness: Causes and Implications," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(8), pages 1709-1721, July.
    8. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Proceedings 512, Federal Reserve Bank of Chicago.
    9. Rombouts, Jeroen V.K. & Stentoft, Lars, 2014. "Bayesian option pricing using mixed normal heteroskedasticity models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 588-605.
    10. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    11. Ender Su & Thomas W. Knowles, 2006. "Asian Pacific Stock Market Volatility Modeling and Value at Risk Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 42(2), pages 18-62, April.
    12. Yongwoong Lee & Ser-Huang Poon, 2015. "Loan Portfolio Loss Models With More Flexible Asymmetry and Tails for Korean Banks and a Comparison of Their Regional Concentrations," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(S3), pages 118-139, May.
    13. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    14. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    15. Linsmeier, Thomas J. & Pearson, Neil D., 1996. "Risk measurement: an introduction to value at risk," ACE Reports 14796, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    16. McMillan, David G. & Kambouroudis, Dimos, 2009. "Are RiskMetrics forecasts good enough? Evidence from 31 stock markets," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 117-124, June.
    17. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, vol. 2(Apr), pages 39-69.
    18. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    19. Kenourgios, Dimitris & Samitas, Aristeidis & Paltalidis, Nikos, 2011. "Financial crises and stock market contagion in a multivariate time-varying asymmetric framework," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(1), pages 92-106, February.
    20. Longin, Francois M, 1996. "The Asymptotic Distribution of Extreme Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 69(3), pages 383-408, July.
    21. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    22. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    23. Bi, Jia & Zhu, Yifeng, 2020. "Value at risk, cross-sectional returns and the role of investor sentiment," Journal of Empirical Finance, Elsevier, vol. 56(C), pages 1-18.
    24. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
    25. Nana Chai & Bi Wu & Weiwei Yang & Baofeng Shi, 2019. "A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(11), pages 2523-2543, September.
    26. Susanne Emmer & Marie Kratz & Dirk Tasche, 2013. "What is the best risk measure in practice? A comparison of standard measures," Papers 1312.1645, arXiv.org, revised Apr 2015.
    Full references (including those not matched with items on IDEAS)

    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. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, September.
    2. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    3. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    4. Marie Kratz & Yen H Lok & Alexander J Mcneil, 2016. "Multinomial var backtests: A simple implicit approach to backtesting expected shortfall," Working Papers hal-01424279, HAL.
    5. Mr. Michael G. Papaioannou, 2006. "A Primer for Risk Measurement of Bonded Debt from the Perspective of a Sovereign Debt Manager," IMF Working Papers 2006/195, International Monetary Fund.
    6. Matthias Fischer & Thorsten Moser & Marius Pfeuffer, 2018. "A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations," Risks, MDPI, vol. 6(4), pages 1-28, December.
    7. Kratz, Marie & Lok, Y-H & McNeil, Alexander J., 2016. "Multinomial VaR Backtests: A simple implicit approach to backtesting expected shortfall," ESSEC Working Papers WP1617, ESSEC Research Center, ESSEC Business School.
    8. Kratz, Marie & Lok, Yen H. & McNeil, Alexander J., 2018. "Multinomial VaR backtests: A simple implicit approach to backtesting expected shortfall," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 393-407.
    9. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    10. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    11. Asimit, Alexandru V. & Bignozzi, Valeria & Cheung, Ka Chun & Hu, Junlei & Kim, Eun-Seok, 2017. "Robust and Pareto optimality of insurance contracts," European Journal of Operational Research, Elsevier, vol. 262(2), pages 720-732.
    12. Xiongwei Ju & Neil D. Pearson, 1998. "Using Value-at-Risk to Control Risk Taking: How Wrong Can you Be?," Finance 9810002, University Library of Munich, Germany.
    13. Mei-Ling Tang & Trung K. Do, 2019. "In search of robust methods for multi-currency portfolio construction by value at risk," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 26(1), pages 107-126, March.
    14. Maria Stefanova, 2012. "Recovery Risiko in der Kreditportfoliomodellierung," Springer Books, Springer, number 978-3-8349-4226-5, March.
    15. Fracasso, Laís Martins & Müller, Fernanda Maria & Ramos, Henrique Pinto & Righi, Marcelo Brutti, 2023. "Is there a risk premium? Evidence from thirteen measures," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 182-199.
    16. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    17. Huang, Alex YiHou, 2010. "An optimization process in Value-at-Risk estimation," Review of Financial Economics, Elsevier, vol. 19(3), pages 109-116, August.
    18. Marios Nerouppos & David Saunders & Costas Xiouros & Stavros A. Zenios, 2006. "Risk Management in Emerging Markets: Practical Methodologies and Empirical Tests," Multinational Finance Journal, Multinational Finance Journal, vol. 10(3-4), pages 179-221, September.
    19. Ruodu Wang & Ričardas Zitikis, 2021. "An Axiomatic Foundation for the Expected Shortfall," Management Science, INFORMS, vol. 67(3), pages 1413-1429, March.
    20. Tadese, Mekonnen & Drapeau, Samuel, 2020. "Relative bound and asymptotic comparison of expectile with respect to expected shortfall," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 387-399.

    More about this item

    Keywords

    Market risk; BASEL-III; Value at risk; Expected shortfall; Back-test;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    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:kap:compec:v:64:y:2024:i:5:d:10.1007_s10614-023-10545-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.