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Report on Analysis of the 260-Day Value at Risk (VAR) of Portfolio of Shares (Paperback)
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Report on Analysis of the 260-Day Value at Risk (VAR) of Portfolio of Shares (Paperback)
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Scholarly Essay from the year 2012 in the subject Business
economics - Investment and Finance, grade: B, Kings College London,
language: English, abstract: For quite a long time now the main
concern for investors as well as regulators of financial markets
has been the risk of catastrophic market and the sufficiency of
capital needed to counter such kind of risk when it occurs. Many
institutions have undergone loses despite their gigantic nature and
good forecasting and this has been associated with inappropriate
forms of pricing and poor management together with the fraudulent
cases, factors that have always brought the issue of managing risk
and regulating these financial markets to the level of public
policy as well as discussion. A basic tool that has been identified
as being effective in the assessment of financial risk is the Value
at Risk (VaR) process (Artzner, et al., 1997). The VaR has been
figured out as being an amount that is lost on a given form of
portfolio including a small probability in a certain fixed period
of time counted in terms of days. VaR however poses a major
challenge during its implementation and this has more to do with
the specification of the kind of probability distribution having
extreme returns that is made use of during the calculation of the
estimates used in the VaR analysis (Mahoney, 1996; McNeil &
Frey, 2000; Dowd, 2001). As has been noted, the nature of VaR
estimation majorly does depend on the accurate predictions of some
uncommon events or risks that are catastrophic. This is attributed
to the fact that VaR is a calculation made from the lowest
portfolio returns. For this reason, any form of calculation that is
employed in the estimation of VaR must be able to encompass the
tail events' prediction and make this its primary goal (Chiang, et
al., 2007; Engle, 2002; Engle & Kroner, 1995; Engle &
Rothschild, 1990; Francis, et al., 2001). There have been
statistical techniques as well as thumb rules that many researchers
argue a
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