+7 (925) 966 4690
ИД «Финансы и кредит»

ЖУРНАЛЫ

  

АВТОРАМ

  

ПОДПИСКА

    
«Дайджест-Финансы»
 

Включен в перечень ВАК по специальностям

ЭКОНОМИЧЕСКИЕ,
ФИЗИКО-МАТЕМАТИЧЕСКИЕ НАУКИ:
5.2.2. Математические, статистические и инструментальные методы в экономике

ЭКОНОМИЧЕСКИЕ НАУКИ:
5.2.4. Финансы
5.2.5. Мировая экономика
5.2.6. Менеджмент

Реферирование и индексирование

РИНЦ
Referativny Zhurnal VINITI RAS
Google Scholar

Электронные версии в PDF

East View Information Services
eLIBRARY.RU
Biblioclub


Лицензия Creative Commons
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.

The Two-Parameter Formula of Default Probability Term Structure

т. 23, вып. 4, декабрь 2018

PDF  PDF-версия статьи

Получена: 14.06.2018

Получена в доработанном виде: 02.07.2018

Одобрена: 17.07.2018

Доступна онлайн: 24.12.2018

Рубрика: FINANCIAL CONTROL

Коды JEL: C58, G17, G28

Страницы: 419–432

https://doi.org/10.24891/df.23.4.419

Pomazanov M.V. National Research University Higher School of Economics, Moscow, Russian Federation 
m.pomazanov@hse.ru

https://orcid.org/0000-0003-3069-1511
SPIN-код: отсутствует

Subject The article discusses the existing methods to model the term structure of default probability and their drawbacks affecting the practical use.
Objectives The research is aimed to make effective suggestions to creditors on setting the technique to evaluate the probability of the corporate borrower's default, considering a changeable term before the loan deal ends, without contradicting IFRS 9 – Financial Instruments.
Methods The research represents the economic and statistical analysis, optimizes aspects of special distributions based on statistical data of rating agencies.
Results I refer to consolidated empirical data of rating agencies on the corporate sector to substantiate the two-parameter formula of term structure of default probability, which does not contradict IFRS 9 with respect to corporate borrowers. In this case, internal bank data are insufficient to build the separate internal model PD Lifetime or this process is too arduous.
Conclusions and Relevance I substantiate the default probability term structure formula, which is best in the pool of fitting distributions, being calibrated with empirically and statistically representative external data of rating agencies, covering a 44-year period. The formula is explicit, without implying complex calculations. The formula may prove useful in calculating the rate of reserves for loan assets, with their terms being coordinated with the principle lending mechanism (SPPI test) with respect to the second impairment phase under the classification given in IFRS 9.

Ключевые слова: credit risk, IFRS reserves, default probability, default term structure, IFRS 9

Список литературы:

  1. Merton R.C. On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. The Journal of Finance, 1974, vol. 29, iss. 2, pp. 449–470. URL: Link
  2. Vasicek O. Loan Portfolio Value. Risk, 2002, vol. 15, no. 12, pp. 160–162.
  3. Gürtler M., Heithecker D. Multi-Period Defaults and Maturity Effects on Economic Capital in a Ratings-based Default-mode Model. Working Papers Technische Universität Braunschweig, Institute of Finance, 2005, no. FW19V2. URL: Link
  4. Fisher E., Heinkel R., Zechner J. Dynamic Capital Structure Choice: Theory and Tests. The Journal of Finance, 1989, vol. 44, no. 1, pp. 19–40. URL: Link
  5. Duffie D., Lando D. Term Structure of Credit Spreads with Incomplete Accounting Information. Econometrica, 2001, vol. 69, iss. 3, pp. 633–664. URL: Link
  6. Kiefer N.M., Larson C.E. Counting Processes for Retail Default Modeling. Journal of Credit Risk, 2015, vol. 11, iss. 3, pp. 45–72. URL: Link
  7. Cox D.R. Regression Models and Life-Tables. Journal of the Royal Statistical Society. Series B (Methodological), 1972, vol. 34, no. 2, pp. 187–220. URL: Link
  8. Breeden J. Reinventing Retail Lending Analytics – Second Impression – Forecasting, Stress Testing, Capital and Scoring for a World of Crises. London, Risk Books, 2010, 433 p.
  9. Israel R.B., Rosenthal J.S., Wei J.Z. Finding Generators for Markov Chains via Empirical Transition Matrices, with Applications to Credit Ratings. Mathematical Finance, 2001, vol. 11, iss. 2, pp. 245–265. URL: Link
  10. Brunel V., Roger B. Le Risque de Credit: Des Modeles au Pilotage des Banques. Economica, 2014.
  11. Brunel V. Loan Classication under IFRS 9. Risk, 2016, May, pp. 77–80.
  12. Bluhm C., Overbeck L. Calibration of PD Term Structures: To Be Markov or Not To Be. Risk, 2007, vol. 20, no. 11, pp. 98–103. URL: Link
  13. Kristof T., Virag M. Lifetime Probability of Default Modeling for Hungarian Corporate Debt Instruments. URL: Link
  14. VaněkT., Hampel D. The Probability of Default under IFRS 9: Multi-period Estimation and Macroeconomic Forecast. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunesis, 2017, vol. 65, iss. 2, pp. 759–776. URL: Link
  15. Petrov D., Pomazanov M. Validation Method of Maturity Adjustment Formula for Basel II Capital Requirement. The Journal of Risk Model Validation, 2009, vol. 3, iss. 3, pp. 81–97.
  16. Marshall A.W., Olkin I. Life Distributions. New York, Springer, 2007, 783 p.
  17. Karminskii A. [Corporate rating models for emerging markets]. Korporativnye finansy = Journal of Corporate Finance Research, 2011, vol. 5, iss. 3, pp. 19–29. (In Russ.) URL: Link

Посмотреть другие статьи номера »

 

ISSN 2311-9438 (Online)
ISSN 2073-8005 (Print)

Свежий номер журнала

т. 28, вып. 4, декабрь 2023

Другие номера журнала