1,720,957 research outputs found
Economic capital management for insurance companies using conditional value at risk and a copula approach
No full textThe loss ratio (LR) for insurance companies is defined as the ratio of incurred claims and earned premiums for a specified class of insurance (CoI). The company may estimate then its capital requirement for that particular CoI by using Value at Risk (VaR) or conditional VaR (CVaR) of the loss ratio distribution at a specified probability value. The overall objective of the company is to evaluate the aggregate capital requirement through a weighted sum of marginal capital requirements for all the classes of insurance. Nevertheless, this procedure may tend to over-estimate the aggregate capital requirement because it does not take into consideration the real dependence amongst the different classes of insurance. In other words, perfect dependence does not allow considering diversification effects. In this paper, we present a model which permits to take into consideration real correlations of the several CoIs. Thanks to copula functions, we are able to generate (by Monte Carlo simulations) correlated loss ratios with known marginal distributions. This approach is described through a numerical example that used data collected from some of the most important Italian non life insurance companies. We show then the diversification benefit thus obtained. We conclude the paper building an efficient frontier on the plane LR - CVaR; the efficient frontier may be considered a useful tool to manage the global company risk
Economic capital management for insurance companies
No full textThe loss ratio (LR) for insurance companies is defined as the ratio of incurred claims and earned premiums for a specified class of insurance (CoI). The company may estimate then its capital requirement for that particular CoI by using value at risk (VaR) or conditional VaR (CVaR) of the LR distribution at a specified probability value. The overall objective of the company is to evaluate the aggregate capital requirement through a weighted sum of marginal capital requirements for all the classes of insurance. Nevertheless, this procedure may tend to over-estimate the aggregate capital requirement because it does not take into consideration the real dependence among the different classes of insurance. In other words, perfect dependence does not allow considering diversification effects
Economic capital management for insurance companies using conditional value at risk and a copula approach
No full textThe loss ratio (LR) for insurance companies is defined as the ratio of incurred claims and earned premiums for a specified class of insurance (CoI). The company may estimate then its capital requirement for that particular CoI by using Value at Risk (VaR) or conditional VaR (CVaR) of the loss ratio distribution at a specified probability value. The overall objective of the company is to evaluate the aggregate capital requirement through a weighted sum of marginal capital requirements for all the classes of insurance. Nevertheless, this procedure may tend to over-estimate the aggregate capital requirement because it does not take into consideration the real dependence amongst the different classes of insurance. In other words, perfect dependence does not allow considering diversification effects. In this paper, we present a model which permits to take into consideration real correlations of the several CoIs. Thanks to copula functions, we are able to generate (by Monte Carlo simulations) correlated loss ratios with known marginal distributions. This approach is described through a numerical example that used data collected from some of the most important Italian non life insurance companies. We show then the diversification benefit thus obtained. We conclude the paper building an efficient frontier on the plane LR - CVaR; the efficient frontier may be considered a useful tool to manage the global company risk
Advanced operational risk modelling for banks and insurance companies
No full textThe aim of this paper is to measure operational risk in financial institutions when historical data are available starting from a fixed threshold.
To quantify the operational risk we apply the Loss Distribution Approach (LDA), a frequency/severity approach widely used in the actuarial models. Risk measures like Value at Risk (VaR) and Expected Shortfall (ES) are used for determining the risk capital necessary to cover the operational risk.
The dependence among the events in the operational risk management has been taken into account using copula functions. We employed for this purpose the Student copula, which is widely used in financial modelling.
Extreme Value Theory (EVT) has been used to model the right tail of the severity of loss distributions.
The Expectation and Maximization (EM) algorithm has been applied to estimate the parameters of the frequency and severity of loss distributions when only their left truncated distributions are available.
We conclude then with a numerical application of the proposed model which aims at evaluating the risk capital for a single financial institution. To this scope we have used, as empirical observations, the OpData® dataset supplied by OpVantage®. In order to estimate the risk capital, we calculate the Value at Risk of the simulated operational loss distribution
The solvent effect in the enatioselective hydrogenation of (E)2-methyl-2-butenoic acid with cinchonidine doped Pd/Al2O3
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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