IDEAS home Printed from https://ideas.repec.org/a/bkr/journl/v82y2023i4p108-125.html
   My bibliography  Save this article

Impact of Market Changes and Regulatory Measures on Accuracy of Bond Valuation in Portfolios of Russian Credit Institutions

Author

Listed:
  • Evgeny Danilov

    (Bank of Russia)

Abstract

This paper uses conditional discrete information entropy to study of how changes in the market and the regulatory measures modifying the procedure for reporting the fair value of securities in financial statements affect the accuracy of bond valuation in the portfolios of Russian credit institutions. It considers the degree of price dispersion in the period from April 2018 to March 2023. The estimated measure of uncertainty in pricing gives a numerical description of the consensus of the banking sector regarding the value of debt securities. The results obtained allow the conclusion that Russian credit institutions follow the international financial reporting standards and tend to value their bond portfolios according to the market value, instead of taking advantage of regulatory relief. At the same time, changes in the market itself due to the recent sanctions pressure have led to increased uncertainty and a decrease in the concentration of valuations.

Suggested Citation

  • Evgeny Danilov, 2023. "Impact of Market Changes and Regulatory Measures on Accuracy of Bond Valuation in Portfolios of Russian Credit Institutions," Russian Journal of Money and Finance, Bank of Russia, vol. 82(4), pages 108-125, December.
    • Handle: RePEc:bkr:journl:v:82:y:2023:i:4:p:108-125
    as

    Download full text from publisher

    File URL: https://rjmf.econs.online/upload/iblock/5b4/ax5u5t3qodm6o5xh1yac8voa1xfeci79/Impact-Regulatory-Measures-Bond-Valuation.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sarig, Oded & Warga, Arthur, 1989. "Bond Price Data and Bond Market Liquidity," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(3), pages 367-378, September.
    2. Cici, Gjergji & Gibson, Scott & Merrick Jr., John J., 2011. "Missing the marks? Dispersion in corporate bond valuations across mutual funds," Journal of Financial Economics, Elsevier, vol. 101(1), pages 206-226, July.
    3. T. Clifton Green & Stephen Figlewski, 1999. "Market Risk and Model Risk for a Financial Institution Writing Options," Journal of Finance, American Finance Association, vol. 54(4), pages 1465-1499, August.
    4. Green, Richard C. & Hollifield, Burton & Schurhoff, Norman, 2007. "Dealer intermediation and price behavior in the aftermarket for new bond issues," Journal of Financial Economics, Elsevier, vol. 86(3), pages 643-682, December.
    5. Kenji Matsui, 2011. "Accounting year-end dispersion and seasonality in the Japanese corporate bond market," Applied Economics, Taylor & Francis Journals, vol. 43(26), pages 3733-3744.
    6. Jankowitsch, Rainer & Nashikkar, Amrut & Subrahmanyam, Marti G., 2011. "Price dispersion in OTC markets: A new measure of liquidity," Journal of Banking & Finance, Elsevier, vol. 35(2), pages 343-357, February.
    7. Marco Avellaneda, 1998. "Minimum-Relative-Entropy Calibration of Asset-Pricing Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 1(04), pages 447-472.
    8. 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, December.
    9. Yuhong Xu, 2014. "Robust valuation and risk measurement under model uncertainty," Papers 1407.8024, arXiv.org.
    10. Paul Glasserman & Xingbo Xu, 2014. "Robust risk measurement and model risk," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 29-58, January.
    11. Garbade, Kenneth D & Silber, William L, 1976. "Price Dispersion in the Government Securities Market," Journal of Political Economy, University of Chicago Press, vol. 84(4), pages 721-740, August.
    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. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
    2. Sebastian Jaimungal & Silvana M. Pesenti & Leandro S'anchez-Betancourt, 2022. "Minimal Kullback-Leibler Divergence for Constrained L\'evy-It\^o Processes," Papers 2206.14844, arXiv.org, revised Aug 2022.
    3. Meucci, A. & Nicolosi, M., 2016. "Dynamic portfolio management with views at multiple horizons," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 495-518.
    4. Díaz, Antonio & Escribano, Ana, 2020. "Measuring the multi-faceted dimension of liquidity in financial markets: A literature review," Research in International Business and Finance, Elsevier, vol. 51(C).
    5. Valeriane Jokhadze & Wolfgang M. Schmidt, 2020. "Measuring Model Risk In Financial Risk Management And Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 23(02), pages 1-37, April.
    6. Jason Allen & Milena Wittwer, 2023. "Centralizing Over-the-Counter Markets?," Journal of Political Economy, University of Chicago Press, vol. 131(12), pages 3310-3351.
    7. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    8. Bidder, R.M. & Smith, M.E., 2018. "Doubts and variability: A robust perspective on exotic consumption series," Journal of Economic Theory, Elsevier, vol. 175(C), pages 689-712.
    9. Soumyadip Ghosh & Henry Lam, 2019. "Robust Analysis in Stochastic Simulation: Computation and Performance Guarantees," Operations Research, INFORMS, vol. 67(1), pages 232-249, January.
    10. Thibaut Lux & Antonis Papapantoleon, 2016. "Model-free bounds on Value-at-Risk using extreme value information and statistical distances," Papers 1610.09734, arXiv.org, revised Nov 2018.
    11. Larcker, David F. & Watts, Edward M., 2020. "Where's the greenium?," Journal of Accounting and Economics, Elsevier, vol. 69(2).
    12. Mingbin Ben Feng & Eunhye Song, 2020. "Efficient Nested Simulation Experiment Design via the Likelihood Ratio Method," Papers 2008.13087, arXiv.org, revised May 2024.
    13. Schneider, Judith C. & Schweizer, Nikolaus, 2015. "Robust measurement of (heavy-tailed) risks: Theory and implementation," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 183-203.
    14. Anthony Coache & Sebastian Jaimungal, 2024. "Robust Reinforcement Learning with Dynamic Distortion Risk Measures," Papers 2409.10096, arXiv.org.
    15. Spiridon Penev & Pavel Shevchenko & Wei Wu, 2019. "Myopic robust index tracking with Bregman divergence," Papers 1908.07659, arXiv.org, revised Jul 2021.
    16. Alexander, Gordon J. & Edwards, Amy K. & Ferri, Michael G., 2000. "The determinants of trading volume of high-yield corporate bonds," Journal of Financial Markets, Elsevier, vol. 3(2), pages 177-204, May.
    17. O’ Hara, Maureen & Wang, Yihui & (Alex) Zhou, Xing, 2018. "The execution quality of corporate bonds," Journal of Financial Economics, Elsevier, vol. 130(2), pages 308-326.
    18. Paul Glasserman & Wanmo Kang, 2014. "OR Forum—Design of Risk Weights," Operations Research, INFORMS, vol. 62(6), pages 1204-1220, December.
    19. Goldstein, Michael A. & Namin, Elmira Shekari, 2023. "Corporate bond liquidity and yield spreads: A review," Research in International Business and Finance, Elsevier, vol. 65(C).
    20. Steven Kou & Xianhua Peng, 2016. "On the Measurement of Economic Tail Risk," Operations Research, INFORMS, vol. 64(5), pages 1056-1072, October.

    More about this item

    Keywords

    entropy; information theory; probability density; uncertainty measure; pricing; securities;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - 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:bkr:journl:v:82:y:2023:i:4:p:108-125. 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: Olga Kuvshinova (email available below). General contact details of provider: https://edirc.repec.org/data/cbrgvru.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.