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Der Vortrag „Parametric Approaches“ von Edu Pristine ist Bestandteil des Kurses „Archiv - Market Risks“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
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... the Great Depression, 1987 crash, Tech Bubble crash, failures of major institutions like LTCM, Enron, Worldcom, Barrings Bank, and the very recent financial crises. In all types of risk, market, credit, operational or insurance risk, one of the greatest challenges is to ...
... absence of useful historical data, EVT provides guidance on the kind of distribution we should select because our quantile estimates, our VaRs, and the estimated probabilities associated ...
... large samples of independent & identically distributed observations e.g. daily or hourly P/L from trading. Observations are then segregated into equal-sized, mutually exclusive and exhaustive groups or blocks. ...
... the distribution is known as Frechet distribution – most important and most widely used in financial models. It represents the fat-tailed distribution commonly found in asset returns. ...
... It makes the most efficient use of the limited data on extreme values Very widely used. POT approach involves fewer parameters than the GEV approach, and is both easier to ...
... The first equation is used in financial risk management because the GPD is heavy-tailed only when ? > 0. Threshold should be optimal as a threshold too high would generate too few excesses; leading to high variance, and one too low ...
... of excess losses converge to the GPD. It is the natural model for excess loss. We select u, which determines the number of observations Nu, in ...
... The expression for Va R using POT parameters is given as u: threshold ( in % term), n: number of observations, Nu: number of observations that exceed threshold The expected shortfall can then be defined as 8 ...
... they both have a tail parameter denoted by ?. There is a subtle difference as the GEV theory focuses on the distribution of extremes, whereas POT focuses on the distribution of values that exceed a certain ...
... observations. Another important reason for using multivariate EVT is that often when modeling financial risk, we investigate the development of a portfolio's log-returns. This prevents the possibility of tracking ...