Поиск по каталогу |
(строгое соответствие)
|
- Профессиональная
- Научно-популярная
- Художественная
- Публицистика
- Детская
- Искусство
- Хобби, семья, дом
- Спорт
- Путеводители
- Блокноты, тетради, открытки
What Drives the Aggregate Credit Risk. The Case of the Czech Republic
В наличии
Местонахождение: Алматы | Состояние экземпляра: новый |
Бумажная
версия
версия
Автор: Jan M?lek
ISBN: 9783659747656
Год издания: 2015
Формат книги: 60×90/16 (145×215 мм)
Количество страниц: 76
Издательство: LAP LAMBERT Academic Publishing
Цена: 23777 тг
Положить в корзину
Способы доставки в город Алматы * комплектация (срок до отгрузки) не более 2 рабочих дней |
Самовывоз из города Алматы (пункты самовывоза партнёра CDEK) |
Курьерская доставка CDEK из города Москва |
Доставка Почтой России из города Москва |
Аннотация: There has been a long discussion about macroeconomic variables influencing the level of aggregate credit risk in the economy. While literature provides both evidence and theoretical explanation of the influence of the business cycle on credit risk, the effect of other variables has not been explored sufficiently. In addition, recent literature suggests the existence of a latent factor behind aggregate credit risk. This work provides in its first part a discussion of potential aggregate credit risk drivers, which have been previously suggested in literature. We verify using a regression model whether the effect of these variables is also apparent in the Czech Republic. The second part of this work explicitly models the latent factor by adding an unobserved component to the model constructed earlier in this thesis. The contribution of this work is due to our belief twofold. First, we add a latent component to the linear regression model. Secondly, we analyze if and under which circumstances the latent component extension improves the fit of the regression model and discuss whether the explicit estimate of the unobserved component has a feasible interpretation as the default cycle.
Ключевые слова: Business Cycle, Credit risk, GLS, Kalman Filter, credit cycle, segmented regression