Banking soundness and financial crises' predictability: A case study of Turkey

Khallouli, Wajih; Nabi, Mahmoud Sami

doi:10.1016/j.inteco.2013.10.002

Cited by 8 publications

(8 citation statements)

References 30 publications

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“…EWSs sometimes extend set of explanatory variables towards individual bank performance data, specifically some proxies for credit risk (contamination of asset portfolio), interest rate risk, or exchange rate risk exposure. These data proved its importance in single country studies (Khallouli & Nabi, 2013), as well as in models that predict individual bank failures (Demyanyk & Hasan, 2010; Kimmel, Thornton, & Bennett, 2016; Oet, Bianco, Gramlich, & Ong, 2013; Tanaka, Kinkyo, & Hamori, 2016). Unfortunately, our choice of methodology leaves no space for any of those extensions.…”

Section: Research Methodology: a Logistic Panel Regression Modelmentioning

confidence: 86%

“…Recently, Dabrowski, Beyers, and de Villiers (2016) found those models outperforming standard logit panel regression or signal extraction models. Khallouli and Nabi (2013) applied Markov switching regression model in the case of Turkey and managed to identify two twin crises that happened in Turkey in the sample period.…”

Section: Research Methodology: a Logistic Panel Regression Modelmentioning

confidence: 99%

“…It is also found in a number of historical crises (Reinhart & Rogoff, 2014) and in some more contemporary banking crises. For example, Khallouli and Nabi (2013) found banks' exposure to public sector amongst the most significant explanatory variables in case of two banking and currency crises of modern Turkey. This way, whenever the problem arises, it will soon spread throughout the whole economic system.…”

Section: Interrelation Between Different Categories Of Financial Crisesmentioning

confidence: 99%

“…Some other authors experimented with a definition of banking crisis, which is based on twin crisis literature. Specifically, Khallouli and Nabi (2013) used a composite index of twin crisis intensity‐based on four indicators: monthly variations of exchange rate, international reserves, interbank interest rate, and central bank loans in total bank liabilities. Those indicators are good enough for currency disturbances joined with bank liquidity crisis, the type of banking crisis formally modelled in Bryant (1980), or Diamond and Dybvig (1983), but it will probably miss more silent solvency type of banking disturbances, also known as bank distress.…”

Section: Operational Definitions Of Banking Crisismentioning

confidence: 99%

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Determinants of financial crises—An early warning system based on panel logit regression

Jemović

Marinković

2019

Int. J Fin Econ

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Despite the fact that different types of financial crises are rooted in similar weaknesses of economy or may have common determinants, the very transmission mechanism may determine one category as leading or lagging behind others. We are focused on financial crises that necessarily have the features of systemic banking crises and assess econometric early warning system of 64 systemic banking crises that occurred in the period from 1977 to 2013. The paper employs two different procedures, based on panel logit regression. The dynamic discrete‐choice (binary) early warning model clearly outperformed the static model. The set of significant explanatory variables changed relative to the findings of the static model. The most significant predictor of the crises in the better performing model is deposit insurance system, followed by international reserves, M2‐to‐international reserves ratio, M2 multiplier, bank deposits, and bank reserves ratio. The statistical significance of the lagged variable confirmed the necessity to take the effect of crisis persistence into account.

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Section: Research Methodology: a Logistic Panel Regression Modelmentioning

confidence: 86%

Section: Research Methodology: a Logistic Panel Regression Modelmentioning

confidence: 99%

Section: Interrelation Between Different Categories Of Financial Crisesmentioning

confidence: 99%

Section: Operational Definitions Of Banking Crisismentioning

confidence: 99%

See 2 more Smart Citations

Determinants of financial crises—An early warning system based on panel logit regression

Jemović

Marinković

2019

Int. J Fin Econ

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“…In another study, Li, Chen, and French (2015) considered the data on future contracts and options to develop the logit model. There are other similar studies on the development of early warning systems based on econometric models (e.g., see Giovanni, Calice, & Leonida, 2014; Caggiano, Calice, Leonida, & Kapetanios, 2016; Khallouli & SamiNabi, 2013; Gresnigt, Kole, & Franses, 2015).…”

Section: Introductionmentioning

confidence: 88%

Network‐based early warning system to predict financial crisis

Dastkhan

2019

Int. J Fin Econ

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In recent years, early warning systems have been developed as a significant tool to prevent financial and economic crises. In this paper, we applied the forward‐looking conditional value at risk (COVaR) as a market‐based systemic risk measure to analyse the systemic risk in an emerging market. A network representation of asset exposures is presented based on the values of ΔCoVaR. According to the exposure networks, two new network‐based indices are introduced. Furthermore, regarding the dependence of the new indices on the values of forward‐looking CoVaR, they are applied as the indicators for the proposed early warning system to predict downturns and crises in the level of firms and the whole market. The results of the early warning system for the market return show that the indicators based on both proposed models have a good ability to predict the crises in the market. In other words, the indicators issued a warning signal up to seven periods before the real crisis in the market. Similarly, the results of the early warning system to predict downturns at the level of firms also indicate the ability of indicators to issue the true warning signals. The results of the proposed system can help policymakers and authorities to determine the appropriate microprudential and macroprudential policies to avoid systemic risks.

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