SSRN Author: Enkelejd HashorvaEnkelejd Hashorva SSRN Content
http://www.ssrn.com/author=1526516
http://www.ssrn.com/rss/en-usFri, 09 Feb 2018 02:13:35 GMTeditor@ssrn.com (Editor)Fri, 09 Feb 2018 02:13:35 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Aggregation of Randomly Weighted Large RisksAsymptotic tail probabilities for linear combinations of randomly weighted order statistics are approximated under various assumptions. One key assumption is the asymptotic independence for all risks. Therefore, it is not surprising that the maxima represents the most influential factor when one investigates the tail behaviour of our considered risk aggregation, which for example, can be found in the reinsurance market. This extreme behaviour confirms the "one big jump" property that has been vastly discussed in the existing literature in various forms whenever the asymptotic independence is present. An illustration of our results together with a specific application are explored under the assumption that the underlying risks follow the multivariate Log-normal distribution.
http://www.ssrn.com/abstract=1993114
http://www.ssrn.com/1665662.htmlThu, 08 Feb 2018 17:10:31 GMTREVISION: Some Mathematical Aspects of Price OptimisationCalculation of an optimal tariff is a principal challenge for pricing actuaries. In this contribution we are concerned with the renewal insurance business discussing various mathematical aspects of calculation of an optimal renewal tariff. Our motivation comes from two important actuarial tasks, namely a) construction of an optimal renewal tariff subject to business and technical constraints, and b) determination of an optimal allocation of certain premium loadings. We consider both continuous and discrete optimisation and then present several algorithmic sub-optimal solutions. Additionally, we explore some simulation techniques. Several illustrative examples show both the complexity and the importance of the optimisation approach.
http://www.ssrn.com/abstract=2779334
http://www.ssrn.com/1610862.htmlSat, 22 Jul 2017 11:33:27 GMTREVISION: Insurance Applications of Some New Dependence Models Derived from Multivariate Collective ModelsConsider two different portfolios which have claims triggered by the same events. Their corresponding collective model over a fixed time period is given in terms of individual claim sizes $(X_i,Y_i), i\ge 1$ and a claim counting random variable $N$. In this paper we are concerned with the joint distribution function $F$ of the largest claim sizes $(X_{N:N}, Y_{N:N})$. By allowing $N$ to depend on some parameter, say $\theta$, then $F=F(\theta)$ is for various choices of $N$ a tractable parametric family of bivariate distribution functions. We present three applications of the implied parametric models to some data from the literature and a new data set from a Swiss insurance company. Furthermore, we investigate both distributional and asymptotic properties of $(X_{N:N,Y_{N:N})$.
http://www.ssrn.com/abstract=2742175
http://www.ssrn.com/1602740.htmlFri, 23 Jun 2017 16:58:06 GMT