SSRN Author: Jan BeirlantJan Beirlant SSRN Content
http://www.ssrn.com/author=434769
http://www.ssrn.com/rss/en-usFri, 22 Dec 2017 02:59:59 GMTeditor@ssrn.com (Editor)Fri, 22 Dec 2017 02:59:59 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Estimating the Maximum Possible Earthquake Magnitude Using Extreme Value Methodology: The Groningen CaseThe area-characteristic, maximum possible earthquake magnitude TM is required by the earthquake engineering community, disaster management agencies and the insurance industry. The Gutenberg-Richter law predicts that earthquake magnitudes M follow a truncated exponential distribution. In the geophysical literature several estimation procedures were proposed, see for instance Kijko and Singh (Acta Geophys., 2011) and the references therein. Estimation of TM is of course an extreme value problem to which the classical methods for endpoint estimation could be applied. We argue that recent methods on truncated tails at high levels (Beirlant et al., Extremes, 2016; Electron. J. Stat., 2017) constitute a more appropriate setting for this estimation problem. We present upper confidence bounds to quantify uncertainty of the point estimates. We also compare methods from the extreme value and geophysical literature through simulations. Finally, the different methods are applied to the magnitude ...
http://www.ssrn.com/abstract=3089547
http://www.ssrn.com/1653425.htmlThu, 21 Dec 2017 05:31:54 GMTREVISION: Modeling Censored Losses Using Splicing: A Global Fit Strategy with Mixed Erlang and Extreme Value DistributionsIn risk analysis, a global fit that appropriately captures the body and the tail of the distribution of losses is essential. Modeling the whole range of the losses using a standard distribution is usually very hard and often impossible due to the specific characteristics of the body and the tail of the loss distribution. A possible solution is to combine two distributions in a splicing model: a light-tailed distribution for the body which covers light and moderate losses, and a heavy-tailed distribution for the tail to capture large losses. We propose a splicing model with a mixed Erlang (ME) distribution for the body and a Pareto distribution for the tail. This combines the flexibility of the ME distribution with the ability of the Pareto distribution to model extreme values. We extend our splicing approach for censored and/or truncated data.
Relevant examples of such data can be found in financial risk analysis. We illustrate the flexibility of this splicing model using practical ...
http://www.ssrn.com/abstract=2872107
http://www.ssrn.com/1591566.htmlWed, 17 May 2017 03:44:34 GMT