Abstract
In this chapter we consider approaches to selecting a parametric family of distributions for a random variable and approaches to estimating the parameters. We also present techniques for analyzing the tails of the chosen probability distribution and the effect of the tails on the estimation of risk measures. Finally, we consider a semiparametric approach to the estimation of tail probabilities. It provides an alternative to relying on a full parametric model in order to produce estimates of tail probabilities beyond the range of the sample data.
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References
Casella, G., Berger, R.L.: Statistical Inference, 2nd edn. Duxbury Press, Belmont (2002)
Embrechts, P., Klüppelberg, C., Mikosch, T.: Modelling Extremal Events for Insurance and Finance. Springer, New York (1997)
de Haan, L., Ferreira, A.: Extreme Value Theory: An Introduction. Springer, New York (2006)
Meucci, A.: Risk and Asset Allocation. Springer, New York (2005)
Resnick, S.I.: Heavy-Tail Phenomena: Probabilistic and Statistical Modeling. Springer, New York (2007)
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© 2012 Springer Science+Business Media New York
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Hult, H., Lindskog, F., Hammarlid, O., Rehn, C.J. (2012). Parametric Models and Their Tails. In: Risk and Portfolio Analysis. Springer Series in Operations Research and Financial Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4103-8_8
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DOI: https://doi.org/10.1007/978-1-4614-4103-8_8
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Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-4102-1
Online ISBN: 978-1-4614-4103-8
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