GENERALIZED GAMMA DISTRIBUTION: MODEL EXPLORATION AND RISK MEASURE
Generalized Gamma Model is generalization of Gamma model which has three non-negative parameters, i.e a; d; and p. Parameter a is scale parameter, while parameter d and p are shape parameters. Generalized Gamma Model is a good model candidate which used in risk measurement. This model can constr...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/49716 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Generalized Gamma Model is generalization of Gamma model which has three
non-negative parameters, i.e a; d; and p. Parameter a is scale parameter,
while parameter d and p are shape parameters. Generalized Gamma Model
is a good model candidate which used in risk measurement. This model can
construct some models which accomodate skewness and it can be used in risk
data. In insurance and finance, risk often comes from several individual risks
which are referred as aggregate risk. Therefore, it is needed to construct aggre-
gate model. In spesific, the construction of the aggregate model is constructed
from single risk distribution of Generalized Gamma. Risks in aggregate model
can be independent or dependent. The dependence will affect to probability
function and distribution function of aggregate model. Risk can be measured
through risk measure. Value-at-Risk (VaR) is the most popular risk measure
to predict losses. In calculating VaR, the exact form of the probability fun-
ction or distribution function is needed. Meanwhile, probability function and
distribution function of Generalized Gamma aggregate model are difficult to
obtain directly, thus Translated Gamma Approximation is used. Hence, the
calculation of VaR for Generalized Gamma aggregate model will be calculated
numerically and also using concept of lower bound for aggregate. |
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