IDEAS home Printed from https://ideas.repec.org/a/sae/artjou/v23y2024i2p179-201.html

Equity Price Risk of Commercial Banks in India

Author

Listed:
  • Nupur Moni Das
  • Bhabani Sankar Rout

Abstract

This study is directed at gauging the equity price risk of the Indian commercial banks for the period 2003–2020. Parametric value-at-risk (VaR) is employed to estimate the downside risk. Further, the univariate exponential generalized auto regressive conditional heteroskedasticity (EGARCH) model is also used to find out the existence of stylised aspects of volatility. The outcomes point towards the existence of volatility clustering, persistence and asymmetry, but differ from bank to bank. Furthermore, the parametric VaR model that assumes normal distribution and student’s t -distribution is not found to be an accurate model for all the banks. Tail risk is also found to be significant, and thus, justifies the Basel Committee’s decision to shift towards an expected shortfall. However, these conventional VaR models should be supplemented by internal models, taking into consideration, bank-specific characteristics. JEL: G01, G15, I15, G17, G28

Suggested Citation

  • Nupur Moni Das & Bhabani Sankar Rout, 2024. "Equity Price Risk of Commercial Banks in India," Arthaniti: Journal of Economic Theory and Practice, , vol. 23(2), pages 179-201, December.
  • Handle: RePEc:sae:artjou:v:23:y:2024:i:2:p:179-201
    DOI: 10.1177/09767479211057048
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/09767479211057048
    Download Restriction: no

    File URL: https://libkey.io/10.1177/09767479211057048?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
    ---><---

    References listed on IDEAS

    as
    1. Zongrun Wang & Weitao Wu & Chao Chen & Yanju Zhou, 2010. "The exchange rate risk of Chinese yuan: Using VaR and ES based on extreme value theory," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 265-282.
    2. Bhabani Sankar Rout & Nupur Moni Das & K. Chandrasekhara Rao, 2019. "Volatility Spillover Effect in Commodity Derivatives Market: Empirical Evidence Through Generalized Impulse Response Function," Vision, , vol. 23(4), pages 374-396, December.
    3. Robert E. Hoyt & Lawrence S. Powell & David W. Sommer, 2007. "Computing Value at Risk: A Simulation Assignment to Illustrate the Value of Enterprise Risk Management," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 10(2), pages 299-307, September.
    4. Jeremy Berkowitz & James O'Brien, 2002. "How Accurate Are Value‐at‐Risk Models at Commercial Banks?," Journal of Finance, American Finance Association, vol. 57(3), pages 1093-1111, June.
    5. Su Xu, 2017. "A VaR assuming Student t distribution not significantly different from a VaR assuming normal distribution," Risk Management, Palgrave Macmillan, vol. 19(3), pages 189-201, August.
    6. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    7. Cheng-Der Fuh & Inchi Hu & Ya-Hui Hsu & Ren-Her Wang, 2011. "Efficient Simulation of Value at Risk with Heavy-Tailed Risk Factors," Operations Research, INFORMS, vol. 59(6), pages 1395-1406, December.
    8. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    9. Onder Buberkoku, 2019. "Do Long-memory GARCH-type-Value-at-Risk Models Outperform None-and Semi-parametric Value-at-Risk Models?," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 199-215.
    10. Elisa Luciano & Robert Kast, 2001. "A Value at Risk Approach to Background Risk," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 26(2), pages 91-115, September.
    11. Alexander, Gordon J. & Baptista, Alexandre M., 2006. "Does the Basle Capital Accord reduce bank fragility? An assessment of the value-at-risk approach," Journal of Monetary Economics, Elsevier, vol. 53(7), pages 1631-1660, October.
    12. Susan Thomas & Mandira Sarma & Ajay Shah, 2003. "Selection of Value-at-Risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 337-358.
    13. Jianping Li & Xiaoqian Zhu & Cheng-Few Lee & Dengsheng Wu & Jichuang Feng & Yong Shi, 2015. "On the aggregation of credit, market and operational risks," Review of Quantitative Finance and Accounting, Springer, vol. 44(1), pages 161-189, January.
    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. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    2. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2012. "When more is less: Using multiple constraints to reduce tail risk," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2693-2716.
    3. Katherine Uylangco & Siqiwen Li, 2016. "An evaluation of the effectiveness of Value-at-Risk (VaR) models for Australian banks under Basel III," Australian Journal of Management, Australian School of Business, vol. 41(4), pages 699-718, November.
    4. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2007. "Mean-variance portfolio selection with `at-risk' constraints and discrete distributions," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3761-3781, December.
    5. Lazar, Emese & Zhang, Ning, 2019. "Model risk of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 105(C), pages 74-93.
    6. Timo Dimitriadis & iaochun Liu & Julie Schnaitmann, 2023. "Encompassing Tests for Value at Risk and Expected Shortfall Multistep Forecasts Based on Inference on the Boundary," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 412-444.
    7. Elena-Ivona DUMITRESCU, 2011. "Backesting Value-at-Risk: From DQ (Dynamic Quantile) to DB (Dynamic Binary) Tests," LEO Working Papers / DR LEO 262, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    8. Alessandro Avellone & Ilaria Foroni & Chiara Pederzoli, 2025. "Minimum capital requirement portfolios according to the new Basel framework for market risk," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 39(2), pages 171-192, June.
    9. Colletaz, Gilbert & Hurlin, Christophe & Pérignon, Christophe, 2013. "The Risk Map: A new tool for validating risk models," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3843-3854.
    10. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2021. "Regulation of bank proprietary trading post 2007–09 crisis: An examination of the Basel framework and Volcker rule," Journal of International Money and Finance, Elsevier, vol. 119(C).
    11. 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.
    12. Aramonte, Sirio & Giudice Rodriguez, Marius del & Wu, Jason, 2013. "Dynamic factor Value-at-Risk for large heteroskedastic portfolios," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4299-4309.
    13. Alexander, Gordon J. & Baptista, Alexandre M., 2009. "Stress testing by financial intermediaries: Implications for portfolio selection and asset pricing," Journal of Financial Intermediation, Elsevier, vol. 18(1), pages 65-92, January.
    14. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    15. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2015. "A comparison of Expected Shortfall estimation models," Journal of Economics and Business, Elsevier, vol. 78(C), pages 14-47.
    16. Kerkhof, Jeroen & Melenberg, Bertrand, 2004. "Backtesting for risk-based regulatory capital," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 1845-1865, August.
    17. Biswajit Patra & Puja Padhi, 2015. "Backtesting of Value at Risk Methodology: Analysis of Banking Shares in India," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 9(3), pages 254-277, August.
    18. Malin Song & Zixu Sui & Xin Zhao, 2023. "A risk measurement study evaluating the impact of COVID-19 on China's financial market using the QR-SGED-EGARCH model," Annals of Operations Research, Springer, vol. 330(1), pages 787-806, November.
    19. Kerkhof, Jeroen & Melenberg, Bertrand & Schumacher, Hans, 2010. "Model risk and capital reserves," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 267-279, January.
    20. Elena-Ivona Dumitrescu & Christophe Hurlin & Vinson Pham, 2012. "Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests," Finance, Presses universitaires de Grenoble, vol. 33(1), pages 79-112.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • 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:sae:artjou:v:23:y:2024:i:2:p:179-201. 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: SAGE Publications (email available below). General contact details of provider: .

    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.