*Tid:***16 januari 2006 kl 1515-1600 **

*Plats :***Seminarierummet 3733**, Institutionen för
matematik, KTH, Lindstedts väg 25, plan 7. Karta!

*Föredragshållare:***
Johan Kilander
**

**Titel:** **
Dimension reduction techniques and multivariate GARCH modeling.
(Examensarbete)
**

* Sammanfattning: *
The purpose of a multivariate GARCH (MGARCH) model is to make the covariance
matrix of a set of risk factors conditional on past events. When the number
of risk factors is large, accurate and robust estimation of most MGARCH
models will not be feasible. The objective of this thesis is to construct an
MGARCH model that efficiently overcomes the estimation problems but still
provides a great deal of flexibility. To this end two existing MGARCH models
are considered: the Orthogonal (O) GARCH model and the Constant Conditional
Correlation (CCC) GARCH model. The strengths of these models are then
built upon to form a new MGARCH model referred to as the CCC-OGARCH. The
CCC-OGARCH imposes constant conditional correlations on a reduced set of
risk factors. To test the validity of this assumption a model with
state-dependent correlations is proposed. Statistical testing and
Value-at-Risk computations indicate that the risk factors could be
appropriately modeled in a CCC-OGARCH framework.