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Оптимальный размер банковского резерва: прогноз просроченной кредитной задолженности с использованием копулярных моделей. Optimum volume of bank reserve: forecasting of overdue credit indebtedness using copula models

Author

Listed:
  • Казакова К.А.

    (Астраханский государственный университет)

  • Князев А.Г.
  • Лепёхин О.А.

Abstract

В статье рассмотрена возможность применения копулярных моделей семейства RLUF для случая построения совместных распределений рядов задолженности по кредитам с макроэкономическими индикаторами с целью дальнейшего прогнозирования объемов просроченной задолженности и определения оптимальных норм резервных требований на соответствующие потери. В исследовании проводится сравнительный анализ многомерных распределений посредствам оценивания модели RLUF-копулы с такими классическими копулярными моделями, как FGM-копула, копула Франка и копула Гаусса. Для получения оценок параметров моделей использован метод максимального правдоподобия. В случае RLUF-копулы получены байесовские оценки параметров с использованием алгоритма Метрополиса со случайным блужданием. Прогнозирование объемов банковского резерва для всех построенных в исследовании моделей, выполняется посредствам генерирования случайной выборки с помощью алгоритма принятия-отклонения для создания соответствующей выборки из совместного распределения с использованием функции плотности копулярной модели. В результате разыгрывания ста возможных сценариев объемов просроченной задолженности получена 95% граница доверительного интервала для объема просроченной задолженности по кредитам, которая в полной мере может выступать в качестве оптимального объема резервных требований на соответствующие кредитные потери. The article propose to consider the possibility of RLUF-copulas application for the creation of joint distributions of overdue credit indebtedness ranks with macroeconomic indicators for the purpose of indebtedness forecasting and also for the definition of optimum volumes of reserve requirements for the corresponding losses. In this research the comparative analysis of multivariate distributions of RLUF-copula estimation with such classical copulas, as FGM-copula, Frank's copula and Gauss's copula is made. In the article the method of maximum likelihood is used for receiving estimates of model parameters. In case of RLUF-copula Bayesian estimates of parameters are received using the Metropolis algorithm with random volatility. Forecasting of bank reserve volumes for all received models is executed in the form of random sample generation by the means of the algorithm of acceptance-deviation for the creation of the corresponding sample of joint distribution using the copula density function. As the result of playing of hundred possible scenarios of indebtedness volumes is obtained the 95% confidence level for the possible volume of credit indebtedness which can fully act as the optimum volume of reserve requirements for the corresponding credit losses.

Suggested Citation

  • Казакова К.А. & Князев А.Г. & Лепёхин О.А., 2015. "Оптимальный размер банковского резерва: прогноз просроченной кредитной задолженности с использованием копулярных моделей. Optimum volume of bank reserve: forecasting of overdue credit indebtedness usi," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 15(4), pages 59-76.
  • Handle: RePEc:scn:guhrje:2015_4_06
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    References listed on IDEAS

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    1. Fantazzini, Dean, 2008. "Credit Risk Management," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 12(4), pages 84-137.
    2. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    3. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    4. Thierry Ane & Loredana Ureche-Rangau & Chiraz Labidi-Makni, 2008. "Time-varying conditional dependence in Chinese stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 18(11), pages 895-916.
    5. Fantazzini , Dean, 2009. "Credit Risk Management (Cont.)," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 13(1), pages 105-138.
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