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| Autor | Jesús Gonzalo, José Olmo |
| Titel | Which Extreme Values Are Really Extreme? |
| Zeitschrift | Journal of Financial Econometrics |
| Ort | Oxford |
| Verlag | Oxford University Press |
| Jahr | 2004 |
| Jahrgang | 2 |
| Nummer | 3 |
| Seiten | 349-369 |
| DOI | 10.1093/jjfinec/nbh014 |
| URL | http://www.eco.uc3m.es/~jgonzalo/ExtremeValuesJoFE.pdf |
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| Fußnoten | no |
| Fragmente | 1 |
| [1.] Rh/Fragment 011 10 - Diskussion Zuletzt bearbeitet: 2012-07-30 23:19:06 Hindemith | Fragment, Gesichtet, Gonzalo Olmo 2004, Rh, SMWFragment, Schutzlevel sysop, Verschleierung |
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| Untersuchte Arbeit: Seite: 11, Zeilen: 10-19 |
Quelle: Gonzalo_Olmo_2004 Seite(n): 349, 350, Zeilen: 20-27, 1-5 |
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| The question one try to answer is: If things go wrong, how wrong can they go? The variance used as a risk measure is unable to answer this question. Alternative measures regarding possible values out of the range of available information need to be defined and calculated. Extreme value theory (EVT) provides the tools to model the asymptotic distribution of the maximum of a sequence of random variables Xi, and in this sense this theory can be very helpful in order to get a first impression about how wrong things can go. A deeper insight into EVT allows knowing not only the order of convergence of the maximum but also the limiting distribution of the largest observations of the sequence. These observations are the main ingredients of more informative risk measures that are normally utilized, like Value at Risk (VaR) or Expected Shortfall. | The question one would like to answer is: ‘‘If things go wrong, how wrong can they go?’’ The variance used as a risk measure is unable to answer this question.
Alternative measures regarding possible values out of the range of available information need to be defined and calculated. Extreme value theory (EVT) provides the tools to model the asymptotic distribution of the maximum of a sequence of random variables {Xn}, and in this sense this theory can be very helpful in order to obtain a first impression about how wrong things [page 350] can go. A deeper insight into EVT allows us to know not only the order of convergence of the maximum, but also the limiting distribution of the largest observations of the sequence. These observations are the main ingredients of more informative risk measures that have been recently introduced, like value at risk (VaR) or expected shortfall. |
Source is not given. Minimally adjusted. |
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