- Tytuł:
- BOOSTING UNDER QUANTILE REGRESSION – CAN WE USE IT FOR MARKET RISK EVALUATION?
- Autorzy:
- Bień-Barkowska, Katarzyna
- Powiązania:
- https://bibliotekanauki.pl/articles/453152.pdf
- Data publikacji:
- 2014
- Wydawca:
- Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
- Tematy:
-
Boosting
quantile regression
GARCH models
value-at-risk - Opis:
- We consider boosting, i.e. one of popular statistical machine-learning meta-algorithms, as a possible tool for combining individual volatility estimates under a quantile regression (QR) framework. Short empirical exercise is carried out for the S&P500 daily return series in the period of 2004-2009. Our initial findings show that this novel approach is very promising and the in-sample goodness-of-fit of the QR model is very good. However much further research should be conducted as far as the out-of-sample quality of conditional quantile predictions is concerned.
- Źródło:
-
Metody Ilościowe w Badaniach Ekonomicznych; 2014, 15, 1; 7-17
2082-792X - Pojawia się w:
- Metody Ilościowe w Badaniach Ekonomicznych
- Dostawca treści:
- Biblioteka Nauki
Artykuł