Causality between banking and currency fragilities: A dynamic panel model

Shen, Chung‐Hua; Chen, Chuan-Mu

doi:10.1016/j.gfj.2007.11.003

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…When the panel threshold dynamic model is used, the results overwhelmingly suggest that bilateral causality exists (Shen and Chen, 2008). When the panel threshold dynamic model is used, the results overwhelmingly suggest that bilateral causality exists (Shen and Chen, 2008).…”

Section: Literature Reviewmentioning

confidence: 97%

A DEA approach for ranking and optimisation of technical and management efficiency of a large bank based on financial indicators

Azadeh

Ghaderi

Mirjalili

et al. 2010

IJOR

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This study presents a data envelopment analysis (DEA) approach for ranking and optimisation of branches of a large bank based on financial indicators. The effective financial indicators are evaluated by standard organisational and managerial assessment. Then, robust DEA models are applied to rank and optimise the organisation using output-oriented models to evaluate management and technical efficiency. The models are output-oriented because they are the primary decision variables in banking institutions. Principal components analysis (PCA) and numerical taxonomy (NT) are used and applied to verify and validate DEA findings. The superiority and applicability of the algorithm are shown for various branches of a large private bank in Iran. In summary, the unique features of this study are: (1) utilisation of DEA models for ranking and optimisation of technical and management efficiency in a large private bank. (2) utilisation of a robust PCA-NT approach for verification and validation of DEA approach. The proposed framework can be used to study the behaviour of financial operations in large banks.A DEA approach for ranking and optimisation 161

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Section: Literature Reviewmentioning

confidence: 97%

A DEA approach for ranking and optimisation of technical and management efficiency of a large bank based on financial indicators

Azadeh

Ghaderi

Mirjalili

et al. 2010

IJOR

View full text Add to dashboard Cite

show abstract

“…The high lag length is disregarded because it will substantially consume our degree of freedom . Shen and Chen (), Fiordelisi, Marques‐Ibanez, and Molyneux (), and Shen, Bu, et al () used the same maximum lag lengths when they considered the panel dynamic causality test.…”

Section: Lead‐and‐lag Between Two Cycles In the Pdl Modelmentioning

confidence: 99%

Is finance a veil? Lead‐and‐lag relationship between financial and business cycles: The case of China

Shen

Shi

Wu³

2018

Euro Fin Management

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This study examines the lead‐and‐lag relationship between financial cycles (FCs) and business cycles (BCs) by using Chinese provincial data. We construct FCs of the financial sector on the basis of three financial variables: credit‐to‐GDP (gross domestic product) ratios, house prices, and equity prices. We use the panel dynamic logit model to investigate the lead‐and‐lag effect between two sectors. Results show that each province has its own unique FCs and BCs. Hence, financial policies should be different in dissimilar provinces. Next, we find that FCs lead BCs and not vice versa. Furthermore, the leading effect is stronger in rich provinces than in poor areas.

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“…Thus, there is no separation of ownership and control (Ncube, 2012 Rinaldi and Sanchis (2006); and Ranjan and Dhal (2003) incorporated macroeconomic determinants as explanatory variables of NPLs. Shen and Chen (2008) also used GDP growth in his paper and found that growth has a significant negative effect on NPLs.…”

Section: Related Literaturementioning

confidence: 99%

“…The lower the value of the ratio, the better the asset quality or vice versa. In related work, Iftikhar (2015), Shehzad et al (2010) and Shen and Chen (2008) also employed NPLs as a proxy of banking fragility to measure the riskiness of banks.…”

Section: Financial Fragilitymentioning

confidence: 99%

Bank fragility in Africa: GMM dynamic panel data evidence

Kedir

Iftikhar

Murinde

et al. 2018

Transnational Corporations Review

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This study investigates the impact of bank-level and macroeconomic variables on bank fragility using a dynamic two-step GMM panel estimator on 433 banks in 46 African countries over the period 1997-2012. The study finds that both bank characteristics and macroeconomic variables are key drivers of bank fragility. The past experience of higher levels of non-performing loans (NPLs) significantly and positively determines current levels of NPLs. The growth of gross loan is negative and significant but economic growth leads to higher NPLs. The equity to assets ratio and the log of assets of banks are negatively associated with NPLs suggesting their potential to provide buffers to banks. Equally, total assets reduce bank fragility. These findings have important policy implications. The study shows that credit risk management initiatives, bank operation oversight and regulations should not be restricted in the times of financial crises, even during positive economic growth episodes in the business cycle.

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Causality between banking and currency fragilities: A dynamic panel model

Cited by 16 publications

References 15 publications

A DEA approach for ranking and optimisation of technical and management efficiency of a large bank based on financial indicators

A DEA approach for ranking and optimisation of technical and management efficiency of a large bank based on financial indicators

Is finance a veil? Lead‐and‐lag relationship between financial and business cycles: The case of China

Bank fragility in Africa: GMM dynamic panel data evidence

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