IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v6y2018i3p62-d154243.html
   My bibliography  Save this article

A Continuous-Time Inequality Measure Applied to Financial Risk: The Case of the European Union

Author

Listed:
  • Guglielmo D’Amico

    (Department of Pharmacy, University of G. D’Annunzio, Chieti 66013, Italy
    These authors contributed equally to this work.)

  • Philippe Regnault

    (U.F.R. Sciences Exactes et Naturelles, Université de Reims Champagne-Ardenne, 51100 Reims, France
    These authors contributed equally to this work.)

  • Stefania Scocchera

    (Department of Pharmacy, University of G. D’Annunzio, Chieti 66013, Italy
    These authors contributed equally to this work.)

  • Loriano Storchi

    (Department of Pharmacy, University of G. D’Annunzio, Chieti 66013, Italy
    These authors contributed equally to this work.)

Abstract

In this paper, we apply information theory measures and Markov processes in order to analyse the inequality in the distribution of the financial risk in a pool of countries. The considered financial variables are sovereign credit ratings and interest rates of sovereign government bonds of European countries. This paper extends the methodology proposed in our previous work, by allowing the possibility to consider a continuous time process for the credit rating evolution so that complete observations of rating histories and credit spreads can be considered in the analysis. Obtained results suggest that the continuous time model fits real data better than the discrete one and confirm the existence of a different risk perception among the three main rating agencies: Fitch, Moody’s and Standard & Poor’s. The application of the model has been performed by a software we developed, the full code is available on-line allowing the replication of all results.

Suggested Citation

  • Guglielmo D’Amico & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2018. "A Continuous-Time Inequality Measure Applied to Financial Risk: The Case of the European Union," IJFS, MDPI, vol. 6(3), pages 1-16, June.
    • Handle: RePEc:gam:jijfss:v:6:y:2018:i:3:p:62-:d:154243
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/6/3/62/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/6/3/62/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nguyet Nguyen, 2018. "Hidden Markov Model for Stock Trading," IJFS, MDPI, vol. 6(2), pages 1-17, March.
    2. Hill, Paula & Brooks, Robert & Faff, Robert, 2010. "Variations in sovereign credit quality assessments across rating agencies," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1327-1343, June.
    3. D’Amico, Guglielmo & Scocchera, Stefania & Storchi, Loriano, 2018. "Financial risk distribution in European Union," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 252-267.
    4. Annika Westphal, 2015. "Systemic Risk in the European Union: A Network Approach to Banks’ Sovereign Debt Exposures," IJFS, MDPI, vol. 3(3), pages 1-36, July.
    5. Robert A. Jarrow & David Lando & Stuart M. Turnbull, 2008. "A Markov Model for the Term Structure of Credit Risk Spreads," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 18, pages 411-453, World Scientific Publishing Co. Pte. Ltd..
    6. Roy Cerqueti & Marcel Ausloos, 2015. "Statistical assessment of regional wealth inequalities: the Italian case," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(6), pages 2307-2323, November.
    7. Bangia, Anil & Diebold, Francis X. & Kronimus, Andre & Schagen, Christian & Schuermann, Til, 2002. "Ratings migration and the business cycle, with application to credit portfolio stress testing," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 445-474, March.
    8. D'Amico, Guglielmo & Di Biase, Giuseppe & Manca, Raimondo, 2014. "Decomposition Of The Population Dynamic Theil'S Entropy And Its Application To Four European Countries," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 55(2), pages 229-239, December.
    9. Marcel Ausloos & Roy Cerqueti, 2016. "Studies on Regional Wealth Inequalities: the case of Italy," Papers 1602.05356, arXiv.org.
    10. Jing-Zhi Huang & Ming Huang, 2012. "How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 2(2), pages 153-202.
    11. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
    12. Guglielmo D'Amico & Jacques Janssen & Raimondo Manca, 2011. "A Non-Homogeneous Semi-Markov Reward Model For The Credit Spread Computation," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 14(02), pages 221-238.
    13. Christensen, Jens H.E. & Hansen, Ernst & Lando, David, 2004. "Confidence sets for continuous-time rating transition probabilities," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2575-2602, November.
    14. D'Amico, Guglielmo & Di Biase, Giuseppe & Manca, Raimondo, 2012. "Income inequality dynamic measurement of Markov models: Application to some European countries," Economic Modelling, Elsevier, vol. 29(5), pages 1598-1602.
    15. Dimitris Gavalas & Theodore Syriopoulos, 2014. "Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle," IJFS, MDPI, vol. 2(1), pages 1-22, March.
    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. Guglielmo D'Amico & Filippo Petroni & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2019. "A copula based Markov Reward approach to the credit spread in European Union," Papers 1902.00691, arXiv.org.

    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. Guglielmo D'Amico & Filippo Petroni & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2019. "A copula based Markov Reward approach to the credit spread in European Union," Papers 1902.00691, arXiv.org.
    2. D’Amico, Guglielmo & Scocchera, Stefania & Storchi, Loriano, 2018. "Financial risk distribution in European Union," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 252-267.
    3. Jeffrey R. Stokes, 2023. "A nonlinear inversion procedure for modeling the effects of economic factors on credit risk migration," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 855-878, October.
    4. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    5. Figlewski, Stephen & Frydman, Halina & Liang, Weijian, 2012. "Modeling the effect of macroeconomic factors on corporate default and credit rating transitions," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 87-105.
    6. Xing, Haipeng & Sun, Ning & Chen, Ying, 2012. "Credit rating dynamics in the presence of unknown structural breaks," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 78-89.
    7. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "On sovereign credit migration: A study of alternative estimators and rating dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3448-3469, April.
    8. Thomas Lagner & Dodozu Knyphausen‐Aufseß, 2012. "Rating Agencies as Gatekeepers to the Capital Market: Practical Implications of 40 Years of Research," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 21(3), pages 157-202, August.
    9. Jafry, Yusuf & Schuermann, Til, 2004. "Measurement, estimation and comparison of credit migration matrices," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2603-2639, November.
    10. Brian BARNARD, 2017. "Rating Migration and Bond Valuation: Decomposing Rating Migration Matrices from Market Data via Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 5(1), pages 49-72.
    11. Til Schuermann & Yusuf Jafry, 2003. "Measurement and Estimation of Credit Migration Matrices," Center for Financial Institutions Working Papers 03-08, Wharton School Center for Financial Institutions, University of Pennsylvania.
    12. Brian BARNARD, 2018. "Rating Migration and Bond Valuation: Ahistorical Interest Rate and Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 6(1), pages 16-30.
    13. Tomáš Vaněk & David Hampel, 2017. "The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(2), pages 759-776.
    14. Brian BARNARD, 2017. "Rating Migration and Bond Valuation: Decomposing Rating Migration Matrices from Market Data via Default Probability Term Structures," Expert Journal of Finance, Sprint Investify, vol. 5, pages 49-72.
    15. Kim, Yoonseong & Sohn, So Young, 2008. "Random effects model for credit rating transitions," European Journal of Operational Research, Elsevier, vol. 184(2), pages 561-573, January.
    16. Frydman, Halina & Schuermann, Til, 2008. "Credit rating dynamics and Markov mixture models," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1062-1075, June.
    17. Tsaig, Yaakov & Levy, Amnon & Wang, Yashan, 2011. "Analyzing the impact of credit migration in a portfolio setting," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3145-3157.
    18. Greig Smith & Goncalo dos Reis, 2017. "Robust and Consistent Estimation of Generators in Credit Risk," Papers 1702.08867, arXiv.org, revised Oct 2017.
    19. Kadam, Ashay & Lenk, Peter, 2008. "Bayesian inference for issuer heterogeneity in credit ratings migration," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2267-2274, October.
    20. Schechtman, Ricardo, 2013. "Default matrices: A complete measurement of banks’ consumer credit delinquency," Journal of Financial Stability, Elsevier, vol. 9(4), pages 460-474.

    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:gam:jijfss:v:6:y:2018:i:3:p:62-:d:154243. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.