Schriftenreihe des Kaerntner Instituts fuer Hoehere Studien Loan Loss Provisions in Alternative Banking Landscapes

Name: Schriftenreihe des Kaerntner Instituts fuer Hoehere Studien Loan Loss Provisions in Alternative Banking Landscapes
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Afbeelding van Schriftenreihe des Kaerntner Instituts fuer Hoehere Studien Loan Loss Provisions in Alternative Banking Landscapes

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Bol This book comprises two empirical studies that use econometric techniques to examine loan loss provisions in Southeast Europe (SEE). The first study applies the Generalized Method of Moments estimator to a dynamic panel dataset, shedding light on specific loan loss provisioning practices. This book comprises two empirical studies that use econometric techniques to examine loan loss provisions in Southeast Europe (SEE). The first study applies the Generalized Method of Moments estimator to a dynamic panel dataset, shedding light on specific loan loss provisioning practices. This study makes a unique contribution to the field, extending empirical evidence on discretionary and non-discretionary components of loan loss provisions in SEE. Additionally, the analysis of outlying observations offers interesting insights into potential motives influencing decisions on loan loss provisions. Filling a gap where research in SEE is infrequent, the second study uses individual structural vector auto-regression (SVAR) models for individual economies and panel SVAR models for SEE EU states and the Western Balkans, forecasting response functions of loan loss provisions to adverse GDP and employment shocks.

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This book comprises two empirical studies that use econometric techniques to examine loan loss provisions in Southeast Europe (SEE). The first study applies the Generalized Method of Moments estimator to a dynamic panel dataset, shedding light on specific loan loss provisioning practices. This book comprises two empirical studies that use econometric techniques to examine loan loss provisions in Southeast Europe (SEE). The first study applies the Generalized Method of Moments estimator to a dynamic panel dataset, shedding light on specific loan loss provisioning practices. This study makes a unique contribution to the field, extending empirical evidence on discretionary and non-discretionary components of loan loss provisions in SEE. Additionally, the analysis of outlying observations offers interesting insights into potential motives influencing decisions on loan loss provisions. Filling a gap where research in SEE is infrequent, the second study uses individual structural vector auto-regression (SVAR) models for individual economies and panel SVAR models for SEE EU states and the Western Balkans, forecasting response functions of loan loss provisions to adverse GDP and employment shocks.

Lees meer