In emerging economies, the effect of excessive credit growth and non - performing loans on banking crisis

Tunay, K. Batu; Yuceyılmaz, Hasan Ferit; Çilesiz, Ahmet

doi:10.22201/fca.24488410e.2020.2215

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…According to previous studies around that time, such as that conducted by Corsetti, Pesenti and Roubini (1999), the sharp currency depreciation and investors' panic were reasons for the crises, as demonstrated in Model 3 and Model 4 for emerging economies. Our results are consistent with a recent study by Tunay et al (2020) which argues that banking crises in emerging economies are likely caused by systemic risk with high credit default. Thus, the effect of house price coupled with household debt is more pronounced in advanced economies.…”

Section: Resultssupporting

confidence: 93%

“…In their study of an emerging country, Garber et al (2019) proved that the tremendous increase in household debt preceded Brazil's economic recession. Nevertheless, in a more recent study of 23 emerging economies, Tunay et al (2020) found opposite evidence showing that crises in the banking sector were headed by the current account deficit and systemic risk as consequence from credit default which explained the twin crisis hypothesis.…”

Section: The Role Of Household Debt and House Price In Banking Crisesmentioning

confidence: 92%

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Early Warning Indicators for Systemic Banking Crises: Household Debt and Property Prices

Samad¹,

Daud²,

Dali³

2020

JEM

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The 2008 financial crisis was the result of escalating house prices and a hasty increase in household debt. In a sample of 41 advanced and emerging countries, this paper employs a logit estimation model to examine the role of household debt and house price as indicators of systemic banking for the period of 1980 until 2018. The results confirm that a high growth in household debt and house price increase enhances the probability for crises to erupt. While this is a consistent evidence for advanced economies, an observation of emerging economies suggests that only a change in household debt and not a change in house prices may cause banking crises to erupt. Policymakers can thus design predictive EWS models based on the surge in household debt and house prices prior to the crises which would lessen, if not alleviate, the effect of upcoming economic shocks by monitoring the macroeconomic changes.

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Section: Resultssupporting

confidence: 93%

Section: The Role Of Household Debt and House Price In Banking Crisesmentioning

confidence: 92%

Early Warning Indicators for Systemic Banking Crises: Household Debt and Property Prices

Samad¹,

Daud²,

Dali³

2020

JEM

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“…Antunes et al [6] proposed the use of fuzzy support vector machine (FSVM) algorithm to improve traditional SVM and obtained correct results. Tunay et al [7] constructed a Partial Least-Squares Logistic model and conducted an empirical study on ST listed companies in stock markets. Misman and Bhatti [8] used multivariate discriminant analysis, Logistic regression, and neural network methods to establish a combined classifier model.…”

Section: Related Workmentioning

confidence: 99%

Financial Crisis Early Warning Based on Panel Data and Dynamic Dual Choice Model

2021

Complexity

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Based on the research of currency crisis pressure index, bank crisis pressure index, and asset bubble crisis pressure index, this paper introduces an external shock pressure index reflecting the impact of global economic changes on economy and synthesizes systemic financial crisis pressure based on the above four pressure indexes; then, all the alternative early warning indicators and the systemic risk pressure index constructed in this paper were tested for Granger causality. We build financial systemic risk pressure indexes, including currency crisis pressure (CCP) banking crisis pressure (BCP) index, bubble crisis pressure (PBP) index, and external shock pressure (ESP) index to predict financial crises. Finally, four indicators that have a significant impact on the systemic financial crisis pressure index were selected, namely, the stock price index change rate, industrial added value growth rate, domestic and foreign real deposit interest rate differential, and foreign direct investment as a percentage of GDP. A dynamic Logit model with lagging binary variables is constructed, and compared with the traditional static Logit line, the actual dynamic fitting effect is better than the static Logit model. The dynamic Logit model is used to predict the early warning status of systemic financial crisis in 2020, and the forecast of various early warning indicators is realized by the ARIMA model. The final prediction results show that the probability of a systemic financial crisis in China in 2020 is extremely low, almost zero. This is in line with the overall improvement in the international economic situation in 2020 and the steady growth of the domestic economy.

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“…In relation to Non Performing Loans, there are several studies that discuss Non Performing Loans. Research [8] shows that banks with large total assets tend to affect the level of Non Performing Loans (NPL), he variable size of the bank is not widely studied for Non Performing Loans (NPL) in Indonesia [9]. Then according to [10] evaluated the effectiveness of the use of instruments in reducing systemic risk in 49 countries and some instruments (LTV and GWM) were effective in reducing procyclicality but their effectiveness was highly dependent on shocks in the financial sector.…”

Section: Empirical Studiesmentioning

confidence: 99%

Bank Capital Buffer Model and Monetary Policy Approach to Non-performing Loans in Indonesia

Husaini¹,

Sitorus²,

Maimunah³

2023

Advances in Social Science, Education and Humanities Research

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The economic and monetary crisis will greatly affect the economic growth of a country. In practice, there are many conditions and factors that can make the provision of credit detrimental to the banking sector. Therefore, the monetary and banking authorities has made various efforts to avoid systemic risks from lending. This study aims to examine the important role of bank capital buffers, RR-LDR policies, Gross Domestic Product, credit interest rates and credit growth in Indonesia on Non Performing Loan from 2012 to 2020. The model used is panel data analysis with cross section data of conventional banks listed in book four according to Bank Indonesia's classification. The results of this study indicate that the bank's capital buffer, GDP and credit interest rates have a significant positive effect on bank credit in Indonesia. On the other hand, RRLDR and credit growth have a negative effect on credit growth.

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In emerging economies, the effect of excessive credit growth and non - performing loans on banking crisis

Cited by 6 publications

References 31 publications

Early Warning Indicators for Systemic Banking Crises: Household Debt and Property Prices

Early Warning Indicators for Systemic Banking Crises: Household Debt and Property Prices

Financial Crisis Early Warning Based on Panel Data and Dynamic Dual Choice Model

Bank Capital Buffer Model and Monetary Policy Approach to Non-performing Loans in Indonesia

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