Grant: Discovery Grants Program - Individual
University of Regina
$15,000.00
Topics in portfolio credit risk and operational risk: dependence/stress modeling and robust estimation
EF 2017-18
Résumé
The proposed research topics are aspects of my continuing research programs in credit risk and operational risk modeling.The recent empirical evidence shows that both asset and default correlations violently increase during stress periods. I propose to study a class of dependent default models, which can incorporate excessive correlations under stress. The implication of the level of dependence incorporated to the credit derivative prices and the risk measures will be studied. This area of research is quite important as the typical models which are built on the conditional independence and the normality assumptions, fail to explain the extreme movement in the market and, thus, introduce a severe underestimation of risk.The modeling of the joint-dynamics of underlying assets is fundamental in credit risk modeling. I intend to study a multi-asset model that incorporates stochastic volatility and stochastic correlations. Parameter estimation and diagnostic tools with a particular application to the modeling of European sovereign credit risk will be considered. In addition to credit risk, a few issues will be studied on operational risk modeling. The portfolio question includes the development of a robust regression method for external loss data scaling. Multivariate loss models will be studied to incorporate any interdependence between business units and event types.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Saskatchewan, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
418195-2013
Name nom
Bae, Taehan
Department département
Mathematics and Statistics
Organization id
17
Institution établissement
University of Regina
Province en
Saskatchewan
Province fr
Saskatchewan
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2013
Award amount
15000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
405
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Modelling and mathematical simulation of natural processes
Area of application fr
Modélisation et simulation mathématique des processus naturels
Research subject code
3000
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Statistics and probability
Research subject fr
Statistique et probabilité
Partie
5
Nb partie
6
Application title
Topics in portfolio credit risk and operational risk: dependence/stress modeling and robust estimation
Keyword
NSERC
Application summary
The proposed research topics are aspects of my continuing research programs in credit risk and operational risk modeling.The recent empirical evidence shows that both asset and default correlations violently increase during stress periods. I propose to study a class of dependent default models, which can incorporate excessive correlations under stress. The implication of the level of dependence incorporated to the credit derivative prices and the risk measures will be studied. This area of research is quite important as the typical models which are built on the conditional independence and the normality assumptions, fail to explain the extreme movement in the market and, thus, introduce a severe underestimation of risk.The modeling of the joint-dynamics of underlying assets is fundamental in credit risk modeling. I intend to study a multi-asset model that incorporates stochastic volatility and stochastic correlations. Parameter estimation and diagnostic tools with a particular application to the modeling of European sovereign credit risk will be considered. In addition to credit risk, a few issues will be studied on operational risk modeling. The portfolio question includes the development of a robust regression method for external loss data scaling. Multivariate loss models will be studied to incorporate any interdependence between business units and event types.