Joint effect of financial fragility and macroeconomic shocks on bank loan losses: Evidence from Europe

Pesola, Jarmo

doi:10.1016/j.jbankfin.2011.04.013

Cited by 25 publications

(10 citation statements)

References 23 publications

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“…Iacoviello (2005) and Calza et al (2007) use a similar general equilibrium model to describe the link between loans, real estate prices and economic performance. Marcucci and Quagliariello (2008) found that house prices are positively related to NPL in the short term, which supports the view that wealth could be used as a buffer against unexpected shocks or as collateral to facilitate access to credit (see also Borio and Lowe 2002;Pesola 2011).…”

Section: House Prices and Credit Riskmentioning

confidence: 76%

“…In the literature, there is a general consensus that factors such as household indebtedness and credit availability, in addition to macroeconomic factors, play an important role in determining credit risk (see for example (Pesola 2011), among others). However, most related literature focuses on one issue or the other, leaving little insight into how these risk factors interact.…”

Section: Introductionmentioning

confidence: 99%

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Housing, Housing Finance and Credit Risk

Canepa

Khaled

2018

IJFS

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This paper investigates the determinants of credit risk from a broad perspective. Particular attention is given to the role of housing affordability and household indebtedness. However, the impact of credit market developments and regulations is also closely examined. Using a large panel of countries it is found that housing affordability and household fragility significantly affect the risk of banks' loan portfolios. In addition, an analysis of the conditional quantiles of non-performing loan ratios reveals that financial institutions in countries with greater levels of financial liberalization and less regulated markets also experience greater credit risk.

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Section: House Prices and Credit Riskmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Housing, Housing Finance and Credit Risk

Canepa

Khaled

2018

IJFS

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“…e empirical research by Mare [24] also shows the necessity of considering macroeconomics when studying the risk of the banking system. Furthermore, Nickell et al [25] and Pesola [26] have found that macroeconomic fluctuations are closely related to the credit risk of the banking system. e empirical studies of the Brazilian banking system by Mendonça and Silva [27] show that the main banking variables affecting the systemic risk are leverage and return on assets.…”

Section: The Modelmentioning

confidence: 99%

Effect of the Dynamic Macroeconomic Fluctuation on the Stability of a Banking Network System with Scale-Free Structure

Gao

Fan

2020

Mathematical Problems in Engineering

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e stability of banking system has caused wide concerns since the global financial crisis. e present paper focuses on studying the stability of a banking network system in the case that the banking network system suffers the dynamic macroeconomic fluctuation shocks. We firstly construct a banking network system with a scale-free structure, then let the banking network system suffer the dynamic macroeconomic fluctuation (here, we consider three kinds of situations; the macro economy fluctuates in downward, upward, and random trends, respectively). en, we study the stability of the banking network system under each situation. Firstly, the results show that the scale-free topology has an important effect on the stability of the banking network system at each macroeconomic fluctuation situation. Secondly, the investment payback period and the ratio of investment to deposit have a large effect on the stability of the banking network system in the cases of dynamic macroeconomic fluctuation. In addition, our further studies find that there is an optimal ratio of the investment to deposit under various macroeconomic fluctuation scenarios, which divides the banking system into stable and unstable regions. Finally, we discuss the regulation strategies for banks, which may provide decision supports for the relevant regulatory authority. connectivity helped to resist risks at lower shocks, but it had the opposite effect at higher shocks. Lenzu and Tedeschi [13] constructed an interbank network model with different network structures to study the stability of the heterogeneous banking system in the face of random shocks and found that the banking system with the random network was more stable than that with the scale-free network. Caccioli et al. [14] proposed a network approach from the perspective of risk contagion to calculate the stability of the financial network due to overlapping portfolios. ey used the "branch process" to understand the financial contagion and found that there was a critical point of leverage that the financial network was stable when the leverage was lower than the critical point, while it was unstable as the leverage increased. Karimi and Raddant [15] investigated cascades of defaults in an interbank loan market and found that the ability of a defaulted institution to start a cascade depends on an interplay of shock size and connectivity. In addition, Degryse and Nguyen [16] had also shown that the size of interbank assets, the distribution of banks' claims and liabilities, and the level of thebanks' capital adequacy ratio were the main causes to the interbank risk contagion. e empirical studies of Mistrulli [17] and Kanno [18] showed that the contagion risk in the banking system was affected by the size of default banks. e abovementioned studies explored the stability of the banking network system, but all did not consider the effect of the dynamic macroeconomic fluctuation on the banking system. While, considering the slowdown of macroeconomic growth in China since 2008, the growth rate of banks' ...

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“…Macroeconomic shocks have an important impact on bank risk and other bank level variables (Claudia M. Buch et al, 2014). Strong adverse aggregate shocks contribute heavily to loan losses when banks are highly exposed to such shocks (Pesola, 2011). Adverse macroeconomic shocks may make it difficult for bank borrowers to pay their debts in full and on time, thus threatening solvency of banks (Gavin & Hausmann, 1996).…”

Section: Transmission Of Macroeconomic Shocks To Bank Riskmentioning

confidence: 99%

Understanding the Response of Indian Banks to Macro-Economic Shocks: A Strategy Perspective

Gupta

2015

SSRN Journal

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The vulnerability of banks to macroeconomic and financial shocks is an area of growing interest to policymakers, especially in emerging markets. Strong adverse aggregate shocks contribute heavily to loan losses when banks are highly exposed to such shocks. I intend to understand the heterogeneity in Indian bank risk responses to macro shocks. Based on the work of Buch et al. (2010), I intend to discuss how macro factors effect bank risk. Using Factor augmented VAR model, I will allow macroeconomic factors to affect bank risk, when macroeconomic factors themselves are modelled as a function of banking variables. We examine if discriminating strategies impact risk responsiveness of Indian commercial banks to macroeconomic shocks using Factor augmented VaR approach for quarterly periods during 2002-13.

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Joint effect of financial fragility and macroeconomic shocks on bank loan losses: Evidence from Europe

Cited by 25 publications

References 23 publications

Housing, Housing Finance and Credit Risk

Housing, Housing Finance and Credit Risk

Effect of the Dynamic Macroeconomic Fluctuation on the Stability of a Banking Network System with Scale-Free Structure

Understanding the Response of Indian Banks to Macro-Economic Shocks: A Strategy Perspective

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