پیش بینی اتلاف خودکار با توجه به وام های بانکی با استفاده از مدل چند مرحله Forecasting loss given default of bank loans with multi-stage model
- نوع فایل : کتاب
- زبان : انگلیسی
- ناشر : Elsevier
- چاپ و سال / کشور: 2017
توضیحات
رشته های مرتبط مدیریت
گرایش های مرتبط بانکداری
مجله بین المللی پیش بینی – International Journal of Forecasting
دانشگاه تحصیلات تکمیلی برای مطالعات پیشرفته، چیکاوا، توکیو، ژاپن
نشریه نشریه الزویر
گرایش های مرتبط بانکداری
مجله بین المللی پیش بینی – International Journal of Forecasting
دانشگاه تحصیلات تکمیلی برای مطالعات پیشرفته، چیکاوا، توکیو، ژاپن
نشریه نشریه الزویر
Description
1. Introduction The Basel II/III Accord allows banks to estimate their credit risk capital requirements using an internal ratingsbased (IRB) approach. The probability of default (PD) and loss given default (LGD) are the most important credit risk parameters in an IRB approach. If banks select the foundations internal ratings-based (FIRB) approach, no proprietary LGD predictive model is required, but if they select the advanced internal ratings-based (AIRB) approach, they will have to build a proprietary predictive model for LGD. We analyze LGD using data provided by three Japanese banks. The aims of this study are as follows. First, we analyze the factors that influence the Japanese LGD. Second, we develop an expected loss (EL) predictive model, consisting of the PD predictive and LGD predictive models. Third, we compare the performances of LGD models and othermodels. This paper builds on the work of Kawada and Yamashita (2013). We then investigate the factors that influence LGD and improve the LGD predictive model proposed by Kawada and Yamashita (2013). The remainder of this paper is structured as follows. Section 2 presents a literature review of the influencing factors and LGD modeling methods. Section 3 defines the terms default and LGD, as used in this study. Section 4 describes the dataset of bank loans used in this study. Section 5 discusses the factors that influence LGD. Section 6 proposes the EL predictive model, consisting of the PD and LGD predictive models. Section 7 evaluates the predictive accuracy of the LGD predictive model. Section 8 presents our conclusions
