Doprava zdarma při nákupu nad 1 499 Kč přes Zásilkovnu nebo PPL Box.

Zjistit stav objednávky

Staňte se součástí komunity milovníků knih z celého světa a získejte hromadu výhod. Založit účet zdarma

Doprava zdarma se Zásilkovnou nad 1 499 Kč

Kurýr DPD 69 Kč PPL shop 49 Kč Balíkovna 69 Kč PPL kurýr 74 Kč PPL box 39 Kč Balíkovna 49 Kč Výdejní místo DPD 49 Kč Zásilkovna 39 Kč

Kontakt

Jak nakupovat

Pomoc

Můj účet

▸ Prázdný :-(

Doprava zdarma při nákupu nad 1 499 Kč přes Zásilkovnu nebo PPL Box.

Loss Given Default Modeling: a Comparative Analysis

Name: Loss Given Default Modeling: a Comparative Analysis
Brand: LAP Lambert Academic Publishing
SKU: 18715689
Price: 590 CZK
Availability: InStock
Author: Olga Yashkir
ISBN: 9786202093750

Olga Yashkir

Yuri Yashkir

Jazyk

Angličtina

Kniha Brožovaná

Libristo kód: 18715689

Nakladatelství LAP Lambert Academic Publishing, listopad 2016

Several most popular Loss Given Default (LGD) models were investigated (LSM, Tobit, Three-Tiered Tob... Celý popis

Libristo kód: 18715689

59 b

590 Kč

Skladem u dodavatele Odesíláme za 5-8 dnů

30 dní na vrácení zboží

Zákazníci také koupili

Charite sacerdotale, ou Lecons elementaires de theologie pastorale. Tome 2 DESURMONT-A

Brožovaná

812 Kč

Les droits sexuels ou la destruction programmée de l’enfance et de la famille Sigaut

Brožovaná

641 Kč

Die Sünde aber gebiert den Tod Andrea Schacht

Brožovaná

208 Kč

Carte des Vignobles et Vins de France - Affiche 50x70cm

binding.

748 Kč

leben // gestalten Ulf Kilian

Pevná

551 Kč

Several most popular Loss Given Default (LGD) models were investigated (LSM, Tobit, Three-Tiered Tobit, Beta Regression, Inflated Beta Regression, Censored Gamma Regression) in order to compare their performance. We show that for a given input data set, the quality of the model calibration depends mainly on the proper choice of explanatory variables, but not on the fitting model. Model factors were chosen based on their correlation with historical LGDs of the calibration data set. Numerical values of non-quantitative parameters (industry, ranking, type of collateral) were introduced as their LGD average. We show that different debt instruments depend on different sets of model factors (from three factors for Revolving Credit or for Subordinated Bonds to eight factors for Senior Secured Bonds). Calibration of LGD models using distressed business cycle periods provide better fit than data from total available time span. Calibration algorithms and details of their realization using the R statistical package are presented. We demonstrate how LGD models can be used for stress testing. The results of this study can be of use to risk managers concerned with the Basel accord compliance.