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A Bayesian Approach to Modeling Operational Risk When Data is Scarce

Svensson, Petter (2015) FMS820 20151
Mathematical Statistics
Abstract (Swedish)
The goal of this thesis is to investigate whether it is possible to construct
an advanced measurement approach (AMA) model for operational
risk when the number of internal data points are very scarce.
An AMA model should combine internal data, external data, scenario
data, and business environment and internal control factors to give a
one year VaR estimate with 99.9 % confidence of operational risk. Out
of the methods of combining the different data sources suggested in the
literature, only the Bayesian inference approach is suitable due to the
small amount of data available. In order to not be restricted to suitable
conjugate-pairs, a numerical approach to evaluating the posterior
distributions is undertaken, and three... (More)
The goal of this thesis is to investigate whether it is possible to construct
an advanced measurement approach (AMA) model for operational
risk when the number of internal data points are very scarce.
An AMA model should combine internal data, external data, scenario
data, and business environment and internal control factors to give a
one year VaR estimate with 99.9 % confidence of operational risk. Out
of the methods of combining the different data sources suggested in the
literature, only the Bayesian inference approach is suitable due to the
small amount of data available. In order to not be restricted to suitable
conjugate-pairs, a numerical approach to evaluating the posterior
distributions is undertaken, and three different severity distributions
are tried out. The distributions tried are the Weibull; the generalized
Champernowne, which is suggested by the literature due to its
tail behavior; and the g-and-h, which is suggested by the literature
due to both its versatility and tail behavior. The conclusion of this
thesis is that it is possible to construct an AMA model with Poisson
loss frequencies using Bayesian inference to combine the different data
sources. However, the data material was too scarce to draw any reliable
conclusions about the severity distribution. (Less)
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author
Svensson, Petter
supervisor
organization
course
FMS820 20151
year
type
H2 - Master's Degree (Two Years)
subject
keywords
AMA, Bayesian inference, Basel II, g-and-h distribution, generalized Champernowne distribution, loss distribution approach, operational risk
language
English
id
5205925
date added to LUP
2015-03-26 11:35:28
date last changed
2015-03-26 11:35:28
@misc{5205925,
  abstract     = {The goal of this thesis is to investigate whether it is possible to construct
an advanced measurement approach (AMA) model for operational
risk when the number of internal data points are very scarce.
An AMA model should combine internal data, external data, scenario
data, and business environment and internal control factors to give a
one year VaR estimate with 99.9 % confidence of operational risk. Out
of the methods of combining the different data sources suggested in the
literature, only the Bayesian inference approach is suitable due to the
small amount of data available. In order to not be restricted to suitable
conjugate-pairs, a numerical approach to evaluating the posterior
distributions is undertaken, and three different severity distributions
are tried out. The distributions tried are the Weibull; the generalized
Champernowne, which is suggested by the literature due to its
tail behavior; and the g-and-h, which is suggested by the literature
due to both its versatility and tail behavior. The conclusion of this
thesis is that it is possible to construct an AMA model with Poisson
loss frequencies using Bayesian inference to combine the different data
sources. However, the data material was too scarce to draw any reliable
conclusions about the severity distribution.},
  author       = {Svensson, Petter},
  keyword      = {AMA,Bayesian inference,Basel II,g-and-h distribution,generalized Champernowne distribution,loss distribution approach,operational risk},
  language     = {eng},
  note         = {Student Paper},
  title        = {A Bayesian Approach to Modeling Operational Risk When Data is Scarce},
  year         = {2015},
}