IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/46728.html
   My bibliography  Save this paper

Assessing Municipal Bond Default Probabilities

Author

Listed:
  • Holian, Matthew
  • Joffe, Marc

Abstract

In response to a request from the California Debt and Investment Advisory Commission, we propose a model to estimate default probabilities for bonds issued by cities. The model can be used with financial data available in Comprehensive Annual Financial Reports that cities are required to publish. The study includes modeled default probability estimates for 261 California cities with population over 25,000. Our model relies on case study evidence, logistic regression analysis of major city financial statistics from the Great Depression – the last time a large number of cities defaulted – as well as logistic regression analysis of more recent city financial statistics. Independent variables in our model include (1) the ratio of interest and pension expenses to total revenue, (2) the annual change in total revenue, (3) the ratio of general fund surplus (or deficit) to general fund revenues and (4) the ratio of general fund balance to general fund expenditures.

Suggested Citation

  • Holian, Matthew & Joffe, Marc, 2013. "Assessing Municipal Bond Default Probabilities," MPRA Paper 46728, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:46728
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/46728/1/MPRA_paper_46728.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francis A. Longstaff & Sanjay Mithal & Eric Neis, 2005. "Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market," Journal of Finance, American Finance Association, vol. 60(5), pages 2213-2253, October.
    2. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "Varieties of Crises and Their Dates," Introductory Chapters, in: This Time Is Different: Eight Centuries of Financial Folly, Princeton University Press.
    3. Wu, Chunchi, 1991. "A Certainty Equivalent Approach to Municipal Bond Default Risk Estimation," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 14(3), pages 241-247, Fall.
    4. Summers, Lawrence H, 1986. "Does the Stock Market Rationally Reflect Fundamental Values?," Journal of Finance, American Finance Association, vol. 41(3), pages 591-601, July.
    5. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    6. Hempel, George H, 1973. "An Evaluation of Municipal "Bankruptcy" Laws and Procedures," Journal of Finance, American Finance Association, vol. 28(5), pages 1339-1351, December.
    7. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    8. Hempel, George H, 1973. "Quantitative Borrower Characteristics Associated with Defaults on Municipal General Obligations," Journal of Finance, American Finance Association, vol. 28(2), pages 523-530, May.
    9. Chunchi Wu, 1991. "A Certainty Equivalent Approach To Municipal Bond Default Risk Estimation," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 14(3), pages 241-247, September.
    10. Chalmers, John M R, 1998. "Default Risk Cannot Explain the Muni Puzzle: Evidence from Municipal Bonds That Are Secured by U.S. Treasury Obligations," The Review of Financial Studies, Society for Financial Studies, vol. 11(2), pages 281-308.
    11. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    12. James Crotty, 2011. "The Realism of Assumptions Does Matter: Why Keynes-Minsky Theory Must Replace Efficient Market Theory as the Guide to Financial Regulation Policy," UMASS Amherst Economics Working Papers 2011-05, University of Massachusetts Amherst, Department of Economics.
    13. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    14. Zorn, Christopher, 2005. "A Solution to Separation in Binary Response Models," Political Analysis, Cambridge University Press, vol. 13(2), pages 157-170, April.
    15. Keim, Donald B. & Stambaugh, Robert F., 1986. "Predicting returns in the stock and bond markets," Journal of Financial Economics, Elsevier, vol. 17(2), pages 357-390, December.
    16. repec:pri:cepsud:91malkiel is not listed on IDEAS
    17. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    18. moon, Choon-Geol & Stotsky, Janet G., 1993. "Municipal bond rating analysis : Sample selectivity and simultaneous equations bias," Regional Science and Urban Economics, Elsevier, vol. 23(1), pages 29-50, March.
    19. Wang, Junbo & Wu, Chunchi & Zhang, Frank X., 2008. "Liquidity, default, taxes, and yields on municipal bonds," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1133-1149, June.
    20. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    21. Carleton, Willard T & Lerner, Eugene M, 1969. "Statistical Credit Scoring of Municipal Bonds," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 1(4), pages 750-764, November.
    22. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    23. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    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. Jerch, Rhiannon & Kahn, Matthew E. & Lin, Gary C., 2023. "Local public finance dynamics and hurricane shocks," Journal of Urban Economics, Elsevier, vol. 134(C).
    2. Gorina, Evgenia & Joffe, Marc & Maher, Craig, 2018. "Using Fiscal Ratios to Predict Local Fiscal Distress," Working Papers 07776, George Mason University, Mercatus Center.
    3. Eymen Errais, 2019. "What Drives Municipalities Default Risk?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(3), pages 49-57, March.
    4. Janda, Karel & Moreira, David, 2016. "Credit risk modelling: default probabilities for Portuguese municipalities," MPRA Paper 74561, University Library of Munich, Germany.
    5. Elena GORI & Silvia FISSI, 2014. "Scoring The Default Risk Of Local Authority," Journal of Public Administration, Finance and Law, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 5(5), pages 7-25, June.
    6. Siodla, James, 2020. "Debt and taxes: Fiscal strain and US city budgets during the Great Depression," Explorations in Economic History, Elsevier, vol. 76(C).

    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. Marc D. Joffe, 2012. "Rating Government Bonds: Can We Raise Our Grade?," Econ Journal Watch, Econ Journal Watch, vol. 9(3), pages 350-365, September.
    2. Pernagallo, Giuseppe & Torrisi, Benedetto, 2020. "Blindfolded monkeys or financial analysts: Who is worth your money? New evidence on informational inefficiencies in the U.S. stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    3. Charron Jacques-Olivier, 2017. "Inefficient Debate. The EMH, the “Remarkable Error” and a Question of Point of View," Accounting, Economics, and Law: A Convivium, De Gruyter, vol. 7(3), pages 1-24, December.
    4. Thomas Delcey & Francesco Sergi, 2019. "The Efficient Market Hypothesis and Rational Expectations. How Did They Meet and Live (Happily?) Ever After," Working Papers hal-02187362, HAL.
    5. Bianchi, Sergio & Pianese, Augusto, 2018. "Time-varying Hurst–Hölder exponents and the dynamics of (in)efficiency in stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 64-75.
    6. Daniel Martin Katz & Michael J Bommarito II & Tyler Soellinger & James Ming Chen, 2015. "Law on the Market? Abnormal Stock Returns and Supreme Court Decision-Making," Papers 1508.05751, arXiv.org, revised May 2017.
    7. Thomas Delcey & Francesco Sergi, 2019. "The Efficient Market Hypothesis and Rational Expectations. How Did They Meet and Live (Happily?) Ever After," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02187362, HAL.
    8. David M. Ritzwoller & Joseph P. Romano, 2019. "Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences," Papers 1908.01406, arXiv.org, revised Apr 2021.
    9. Jitka Veselá & Alžběta Zíková, 2022. "Are the Czech, Polish, German and Dutch markets taking a random walk? [Konají český, polský, německý a nizozemský trh náhodnou procházku?]," Český finanční a účetní časopis, Prague University of Economics and Business, vol. 2022(2), pages 19-38.
    10. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    11. Cristi Spulbar & Ramona Birau & Lucian Florin Spulbar, 2021. "A Critical Survey on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) Considering Their Implication on Stock Markets Behavior," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1161-1165, December.
    12. Mahata, Ajit & Rai, Anish & Nurujjaman, Md. & Prakash, Om, 2021. "Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    13. Rešovský, Marcel & Gróf, Marek & Horváth, Denis & Gazda, Vladimír, 2014. "Analysis of the Lead-Lag Relationship on South Africa capital market," MPRA Paper 57309, University Library of Munich, Germany.
    14. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).
    15. Svitlana Galeshchuk, 2017. "Technological bias at the exchange rate market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(2-3), pages 80-86, April.
    16. Yardley, Ben, 2020. "The Effects of Donald Trump’s Tweets on The Stock Exchange," MPRA Paper 102578, University Library of Munich, Germany.
    17. Angelini, Giovanni & De Angelis, Luca & Singleton, Carl, 2022. "Informational efficiency and behaviour within in-play prediction markets," International Journal of Forecasting, Elsevier, vol. 38(1), pages 282-299.
    18. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    19. Thibaut Mastrolia & Tianrui Xu, 2024. "Clearing time randomization and transaction fees for auction market design," Papers 2405.09764, arXiv.org, revised Oct 2024.
    20. Kevin Primicerio & Damien Challet & Stanislao Gualdi, 2017. "Wisdom of the institutional crowd," Working Papers hal-01484914, HAL.

    More about this item

    Keywords

    municipal bonds; municipal bankruptcy; default probability model;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • H74 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Borrowing
    • R51 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Finance in Urban and Rural Economies

    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:pra:mprapa:46728. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.