The probability of default in Russian banking

Karminsky, Alexander; Kostrov, Alexander

doi:10.1007/s40822-014-0005-2

Cited by 17 publications

(12 citation statements)

References 14 publications

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“…In addition, the probability-based approach to evaluating the accuracy of default probability forecasts has not been, to our knowledge, previously used in the field. With a significant number of type II errors and only 30 failure-type observations in the forecasting set, Karminsky and Kostrov (2014) forecast 60% of banking license revocations in 2011-2012, which is different from bank failure with negative capital in 2014-1H2015. We accurately forecast the probability of bank failure with negative capital.…”

Section: Key Findings and Robustness Of The Forecasting Proceduresmentioning

confidence: 96%

“…In this section, we try to outline development process for the Russian banking sector and stress the most important conditions that could lead to the observed phenomenon of negative capital in banks and the overall vulnerability of the Russian banking sector. We identify the periods of the banking system development, as suggested by Karminsky and Kostrov (2014). (1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999).…”

Section: Literature On the License Revocations From Russian Banksmentioning

confidence: 99%

“…So, in Lanine and Vander Vennet (2006) and Fungacova and Weill (2013), the forecasting performance deteriorates as they increase the horizon from 1 to 4 quarters. Karminsky and Kostrov (2014) declare that the use of longer forecasting horizons decreases the model quality. In prior research, they used a very limited or fixed set of candidate predictors that could explain the tendency observed.…”

Section: Key Findings and Robustness Of The Forecasting Proceduresmentioning

confidence: 99%

“…The phenomenon of failed Russian banks with negative capital was previously uncommon and little studied. The majority of papers on the financial stability of Russian banks examine the factors of bank failure in Russia and try to make predictions (see Fungacova and Weill, 2013;Karminsky and Kostrov, 2014;Lanine and Vander Vennet, 2006). Prior research has some limitations related to the availability of data for analysis.…”

Section: Introductionmentioning

confidence: 99%

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The back side of banking in Russia: forecasting bank failures with negative capital

Karminsky

Kostrov

2017

IJCEE

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Since 2013, we have observed an increasing number of failed Russian banks with negative capital and falsified financial reporting. We use previously unavailable data for the period 2010 -1H2015 to develop a logit model predicting the probability of bank failure with negative capital. In order to do so, we suggest solutions for the class imbalance and variable selection problems. The models chosen are confirmed to be robust and have longer forecasting horizons compared to previous research. Also, we implement a novel probability-based approach to the out-of-sample forecasting evaluation which confirms a good fit of the selected models to data. The model predicts bank failures in three quarters and finds 33% of actual failures among 5% of banks with the highest predicted probability to fail (out-of-sample). In addition, we make available previously unpublished banking data for Russia.

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Section: Key Findings and Robustness Of The Forecasting Proceduresmentioning

confidence: 96%

Section: Literature On the License Revocations From Russian Banksmentioning

confidence: 99%

Section: Key Findings and Robustness Of The Forecasting Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The back side of banking in Russia: forecasting bank failures with negative capital

Karminsky

Kostrov

2017

IJCEE

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“…Данные по российским банкам используются в рабо-тах (Пересецкий, 2007. Из более поздних работ можно отметить работу (Karminsky, Kostrov, 2014), которая опи-рается на российские данные за 1998-2011 гг. Авторы (Young, Torna, 2013) строят свои прогнозы на основании данных по банкам США, а в работе (Betz et al, 2014) Журнал НЭА, № 4 (36), 2017, с.…”

Section: модели дефолта банковunclassified