Modeling tail risks and systemic risks using copulas
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Date
2018-08Author
Kibet, Jerotich Jacqualine
Type
ThesisLanguage
enMetadata
Show full item recordAbstract
Understanding the relationship between financial variables is an essential part of managing financial risk. Such relationships in the context of risk management are of sensation interest in the modern world in which modeling risk has become a popular idea. The
2007/08 financial meltdown has accentuated the importance of this because it showed that
with a distant probability of an event happening, say 0.01%, it should not be overlooked.
The existence of dependency between the two variables is underscored in their established
joint distribution. Conventional financial models intensely depend on a relationship to
elucidation the dependence that exists between tow variables. Nevertheless, as a means of
measuring dependence, correlation entirely defines the dependence structure for normal
distributions. This is more so in the case of elliptical distributions as opposed to other
classes. Even within elliptical distributions, correlation remains delicate because it is easily
distorted by outliers.
One of the biggest shortfalls of current financial models is in their assumption and oversimplification
that leads to false parameterization. Many financial models assume that
asset returns and risk variables follow normal distribution and as such pay a_ention the
judiciously possible (central moments), undermining the value of what is remotely probable
(extreme events). Yet, the financial crisis showed that remote events are understated. Thus,
this paper focuses on copula model- that characterized the dependence structure across
the entire distribution. Also, copulas have the advantage of being able to portray more
information concerning the dependence structures between the two variables because
they are distributions themselves.
From the viewpoint systemic and tail risk, the copula is motivating. In this regard, it
enables us to disengage the dependence structure (linked with systemic risk) from the
marginal distribution (linked with tail risk) and archetypal both distinctly with a bigger
degree of precision. Fundamentally, copulas act as magnifying glasses that permit us to
examine and model financial variables with greater precision- in this case the connections
between systemic and tail risk
Citation
Master of science in Actuarial Science.Publisher
School of Mathematics, University of Nairobi
Subject
Modeling tail risksDescription
Master of science in Actuarial Science.
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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