*Tid:* **5 juni 2017 kl 15.00-16.00.**
**Seminarierummet 3721**, Institutionen för
matematik, KTH, Lindstedtsvägen 25, plan 7.
Karta!
*Föredragshållare:*
**
Ludvig Pucek och Viktor Sonebäck
**
**Titel:**
Hierarchical clustering of market risk models
**Abstract**
This thesis aims to discern what factors and assumptions are the most important in market
risk modelling through examining a broad range of models, for different risk measures
(VaR0:01, ES0:01 and ES0:025) and using hierarchical clustering to identify similarities and dissimilarities
between the models. The data used is daily log returns for OMXS30 stock index
and Bloomberg Barclays US aggregate bond index (AGG) from which daily risk estimates
are simulated.
In total, 33 market risk models are included in the study. These models consist of unconditional
variance models (Student's t distribution, Normal distribution, Historical simulation
and Extreme Value Theory (EVT) with Generalized Pareto Tails (GPD)) and conditional
variance models (ARCH, GARCH, GJR-GARCH and EGARCH). The conditional models
are used in filtered and unfiltered market risk models.
The hierarchical clustering is done for all risk measures and for both time series, and a
comparison is made between VaR0:01 and ES0:025.
The thesis shows that the most important assumption is whether the models have conditional
or unconditional variance. The hierarchy for assumptions then dier depending on time series
and risk measure. For OMXS30, the clusters for VaR0:01 and ES0:025 are the same and the
largest dividing factors for the conditional models are (in descending order):
Leverage component (EGARCH or GJR-GARCH models) or no leverage component
(GARCH or ARCH)
Filtered or unfiltered models
Type of variance model (EGARCH/GJR-GARCH and GARCH/ARCH)
The ES_0.01 cluster shows that ES_0.01 puts a higher emphasis on normality or non-normality
assumptions in the models.
The similarities in the different clusters are more prominent for OMXS30 than for AGG.
The hierarchical clustering for AGG is also more sensitive to the choice of risk measure. For
AGG the variance models are generally less important and more focus lies in the assumed
distributions in the variance models (normal innovations or student's t innovations) and the
assumed nal log return distribution (Normal, Student's t, HS or EVT-tails).
In the lowest level clusters, the transition from VaR_0.01 to ES_0.025 result in a smaller model
disagreement.
The full report (pdf)
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