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Stochastic comparisons of the largest claim amounts from two sets of interdependent heterogeneous portfolios

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 Added by Hamzeh Torabi
 Publication date 2018
  fields Financial
and research's language is English




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Let $ X_{lambda_1},ldots,X_{lambda_n}$ be dependent non-negative random variables and $Y_i=I_{p_i} X_{lambda_i}$, $i=1,ldots,n$, where $I_{p_1},ldots,I_{p_n}$ are independent Bernoulli random variables independent of $X_{lambda_i}$s, with ${rm E}[I_{p_i}]=p_i$, $i=1,ldots,n$. In actuarial sciences, $Y_i$ corresponds to the claim amount in a portfolio of risks. In this paper, we compare the largest claim amounts of two sets of interdependent portfolios, in the sense of usual stochastic order, when the variables in one set have the parameters $lambda_1,ldots,lambda_n$ and $p_1,ldots,p_n$ and the variables in the other set have the parameters $lambda^{*}_1,ldots,lambda^{*}_n$ and $p^*_1,ldots,p^*_n$. For illustration, we apply the results to some important models in actuary.



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Let $ X_{lambda_1},ldots,X_{lambda_n}$ be a set of dependent and non-negative random variables share a survival copula and let $Y_i= I_{p_i}X_{lambda_i}$, $i=1,ldots,n$, where $I_{p_1},ldots,I_{p_n}$ be independent Bernoulli random variables independent of $X_{lambda_i}$s, with ${rm E}[I_{p_i}]=p_i$, $i=1,ldots,n$. In actuarial sciences, $Y_i$ corresponds to the claim amount in a portfolio of risks. This paper considers comparing the smallest claim amounts from two sets of interdependent portfolios, in the sense of usual and likelihood ratio orders, when the variables in one set have the parameters $lambda_1,ldots,lambda_n$ and $p_1,ldots,p_n$ and the variables in the other set have the parameters $lambda^{*}_1,ldots,lambda^{*}_n$ and $p^*_1,ldots,p^*_n$. Also, we present some bounds for survival function of the smallest claim amount in a portfolio. To illustrate validity of the results, we serve some applicable models.
Let $X_{lambda_1}, ldots , X_{lambda_n}$ be independent non-negative random variables belong to the transmuted-G model and let $Y_i=I_{p_i} X_{lambda_i}$, $i=1,ldots,n$, where $I_{p_1}, ldots, I_{p_n}$ are independent Bernoulli random variables independent of $X_{lambda_i}$s, with ${rm E}[I_{p_i}]=p_i$, $i=1,ldots,n$. In actuarial sciences, $Y_i$ corresponds to the claim amount in a portfolio of risks. In this paper we compare the smallest and the largest claim amounts of two sets of independent portfolios belonging to the transmuted-G model, in the sense of usual stochastic order, hazard rate order and dispersive order, when the variables in one set have the parameters $lambda_1,ldots,lambda_n$ and the variables in the other set have the parameters $lambda^{*}_1,ldots,lambda^{*}_n$. For illustration we apply the results to the transmuted-G exponential and the transmuted-G Weibull models.
Financial markets are exposed to systemic risk, the risk that a substantial fraction of the system ceases to function and collapses. Systemic risk can propagate through different mechanisms and channels of contagion. One important form of financial contagion arises from indirect interconnections between financial institutions mediated by financial markets. This indirect interconnection occurs when financial institutions invest in common assets and is referred to as overlapping portfolios. In this work we quantify systemic risk from indirect interconnections between financial institutions. Having complete information of security holdings of major Mexican financial intermediaries and the ability to uniquely identify securities in their portfolios, allows us to represent the Mexican financial system as a bipartite network of securities and financial institutions. This makes it possible to quantify systemic risk arising from overlapping portfolios. We show that focusing only on direct exposures underestimates total systemic risk levels by up to 50%. By representing the financial system as a multi-layer network of direct exposures (default contagion) and indirect exposures (overlapping portfolios) we estimate the mutual influence of different channels of contagion. The method presented here is the first objective data-driven quantification of systemic risk on national scales that includes overlapping portfolios.
Discussion of ``2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization by V. Koltchinskii [arXiv:0708.0083]
We consider expected performances based on max-stable random fields and we are interested in their derivatives with respect to the spatial dependence parameters of those fields. Max-stable fields, such as the Brown--Resnick and Smith fields, are very popular in spatial extremes. We focus on the two most popular unbiased stochastic derivative estimation approaches: the likelihood ratio method (LRM) and the infinitesimal perturbation analysis (IPA). LRM requires the multivariate density of the max-stable field to be explicit, and IPA necessitates the computation of the derivative with respect to the parameters for each simulated value. We propose convenient and tractable conditions ensuring the validity of LRM and IPA in the cases of the Brown--Resnick and Smith field, respectively. Obtaining such conditions is intricate owing to the very structure of max-stable fields. Then we focus on risk and dependence measures, which constitute one of the several frameworks where our theoretical results can be useful. We perform a simulation study which shows that both LRM and IPA perform well in various configurations, and provide a real case study that is valuable for the insurance industry.
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