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Forecasting Loss Given Default models : impact of account characteristics and macroeconomic state

Tobback, Ellen; Martens, David; Van Gestel, Tony; … - In: Journal of the Operational Research Society : OR 65 (2014) 3, pp. 376-392

Persistent link: https://www.econbiz.de/10010251701

What does your Facebook profile reveal about your creditworthiness? : using alternative data for microfinance

De Cnudde, Sofie; Moeyersoms, Julie; Stankova, Marija; … - In: Journal of the Operational Research Society 70 (2019) 3, pp. 353-363

Persistent link: https://www.econbiz.de/10012212384

A novel credit rating migration modeling approach using macroeconomic indicators

Berteloot, Koen; Verbeke, Wouter; Castermans, Gerd; Van … - In: Journal of forecasting 32 (2013) 7, pp. 654-672

Persistent link: https://www.econbiz.de/10010202164

Benchmarking regression algorithms for loss given default modeling

Loterman, Gert; Brown, Iain; Martens, David; Mues, … - In: International journal of forecasting 28 (2012) 1, pp. 161-170

Persistent link: https://www.econbiz.de/10009581995

Benchmarking regression algorithms for loss given default modeling

Loterman, Gert; Brown, Iain; Martens, David; Mues, … - In: International Journal of Forecasting 28 (2012) 1, pp. 161-170

The introduction of the Basel II Accord has had a huge impact on financial institutions, allowing them to build credit risk models for three key risk parameters: PD (probability of default), LGD (loss given default) and EAD (exposure at default). Until recently, credit risk research has focused...

Persistent link: https://www.econbiz.de/10010796146