Example of Insurance: Mathematics and Economics format
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Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format Example of Insurance: Mathematics and Economics format
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open access Open Access

Insurance: Mathematics and Economics — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Statistics and Probability #64 of 239 down down by 8 ranks
Statistics, Probability and Uncertainty #41 of 152 down down by 10 ranks
Economics and Econometrics #211 of 661 down down by 44 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 402 Published Papers | 1103 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 01/06/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

2.7

4% from 2019

CiteRatio for Insurance: Mathematics and Economics from 2016 - 2020
Year Value
2020 2.7
2019 2.6
2018 2.5
2017 2.5
2016 2.4
graph view Graph view
table view Table view

1.139

5% from 2019

SJR for Insurance: Mathematics and Economics from 2016 - 2020
Year Value
2020 1.139
2019 1.201
2018 0.949
2017 1.083
2016 0.968
graph view Graph view
table view Table view

1.595

1% from 2019

SNIP for Insurance: Mathematics and Economics from 2016 - 2020
Year Value
2020 1.595
2019 1.606
2018 1.256
2017 1.322
2016 1.489
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 4% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 5% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 1% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Insurance: Mathematics and Economics

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Elsevier

Insurance: Mathematics and Economics

Insurance: Mathematics and Economics is an international journal that intends to strengthen communication between individuals and groups who produce and apply research results in insurance and finance, aiming to integrate the currently fragmented research in both fields. The j...... Read More

Statistics, Probability and Uncertainty

Statistics and Probability

Economics and Econometrics

Decision Sciences

i
Last updated on
01 Jun 2020
i
ISSN
0167-6687
i
Impact Factor
High - 1.453
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
elsarticle-num
i
Citation Type
Numbered
[25]
i
Bibliography Example
G. E. Blonder, M. Tinkham, T. M. Klapwijk, Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion, Phys. Rev. B 25 (7) (1982) 4515–4532. URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1016/J.INSMATHECO.2007.02.001
Pair-copula constructions of multiple dependence
Kjersti Aas, Claudia Czado1, Arnoldo Frigessi2, Henrik Bakken3

Abstract:

Building on the work of Bedford, Cooke and Joe, we show how multivariate data, which exhibit complex patterns of dependence in the tails, can be modelled using a cascade of pair-copulae, acting on two variables at a time. We use the pair-copula decomposition of a general multivariate distribution and propose a method for perf... Building on the work of Bedford, Cooke and Joe, we show how multivariate data, which exhibit complex patterns of dependence in the tails, can be modelled using a cascade of pair-copulae, acting on two variables at a time. We use the pair-copula decomposition of a general multivariate distribution and propose a method for performing inference. The model construction is hierarchical in nature, the various levels corresponding to the incorporation of more variables in the conditioning sets, using pair-copulae as simple building blocks. Pair-copula decomposed models also represent a very flexible way to construct higher-dimensional copulae. We apply the methodology to a financial data set. Our approach represents the first step towards the development of an unsupervised algorithm that explores the space of possible pair-copula models, that also can be applied to huge data sets automatically. read more read less

Topics:

Multivariate normal distribution (51%)51% related to the paper, Vine copula (51%)51% related to the paper, Copula (linguistics) (51%)51% related to the paper, Conditional probability distribution (50%)50% related to the paper
View PDF
1,744 Citations
Journal Article DOI: 10.1016/J.INSMATHECO.2007.10.005
Goodness-of-fit tests for copulas: A review and a power study
Christian Genest1, Bruno Rémillard2, David Beaudoin1

Abstract:

Many proposals have been made recently for goodness-of-fit testing of copula models. After reviewing them briefly, the authors concentrate on “blanket tests”, i.e., those whose implementation requires neither an arbitrary categorization of the data nor any strategic choice of smoothing parameter, weight function, kernel, wind... Many proposals have been made recently for goodness-of-fit testing of copula models. After reviewing them briefly, the authors concentrate on “blanket tests”, i.e., those whose implementation requires neither an arbitrary categorization of the data nor any strategic choice of smoothing parameter, weight function, kernel, window, etc. The authors present a critical review of these procedures and suggest new ones. They describe and interpret the results of a large Monte Carlo experiment designed to assess the effect of the sample size and the strength of dependence on the level and power of the blanket tests for various combinations of copula models under the null hypothesis and the alternative. To circumvent problems in the determination of the limiting distribution of the test statistics under composite null hypotheses, they recommend the use of a double parametric bootstrap procedure, whose implementation is detailed. They conclude with a number of practical recommendations. read more read less

Topics:

Statistical hypothesis testing (55%)55% related to the paper, Goodness of fit (54%)54% related to the paper, Copula (linguistics) (52%)52% related to the paper, Parametric statistics (52%)52% related to the paper, Anderson–Darling test (51%)51% related to the paper
995 Citations
open accessOpen access Journal Article DOI: 10.1016/S0167-6687(02)00185-3
A Poisson log-bilinear regression approach to the construction of projected lifetables
Natacha Brouhns1, Michel Denuit1, Jeroen K. Vermunt2

Abstract:

This paper implements Wilmoth’s [Computational methods for fitting and extrapolating the Lee–Carter model of mortality change, Technical report, Department of Demography, University of California, Berkeley] and Alho’s [North American Actuarial Journal 4 (2000) 91] recommendation for improving the Lee–Carter approach to the fo... This paper implements Wilmoth’s [Computational methods for fitting and extrapolating the Lee–Carter model of mortality change, Technical report, Department of Demography, University of California, Berkeley] and Alho’s [North American Actuarial Journal 4 (2000) 91] recommendation for improving the Lee–Carter approach to the forecasting of demographic components. Specifically, the original method is embedded in a Poisson regression model, which is perfectly suited for age–sex-specific mortality rates. This model is fitted for each sex to a set of age-specific Belgian death rates. A time-varying index of mortality is forecasted in an ARIMA framework. These forecasts are used to generate projected age-specific mortality rates, life expectancies and life annuities net single premiums. Finally, a Brass-type relational model is proposed to adapt the projections to the annuitants population, allowing for estimating the cost of adverse selection in the Belgian whole life annuity market. © 2002 Elsevier Science B.V. All rights reserved. read more read less

Topics:

Lee–Carter model (67%)67% related to the paper, Population (54%)54% related to the paper, Life insurance (53%)53% related to the paper, Life annuity (50%)50% related to the paper, Poisson regression (50%)50% related to the paper
View PDF
689 Citations
Journal Article DOI: 10.1016/J.INSMATHECO.2005.12.001
A cohort-based extension to the Lee–Carter model for mortality reduction factors
A. E. Renshaw1, Steven Haberman1

Abstract:

The Lee–Carter modelling framework is extended through the introduction of a wider class of generalised, parametric, non-linear models. This permits the modelling and extrapolation of age-specific cohort effects as well as the more familiar age-specific period effects. The choice of error distribution is generalised. The Lee–Carter modelling framework is extended through the introduction of a wider class of generalised, parametric, non-linear models. This permits the modelling and extrapolation of age-specific cohort effects as well as the more familiar age-specific period effects. The choice of error distribution is generalised. read more read less

Topics:

Lee–Carter model (60%)60% related to the paper
683 Citations
open accessOpen access Journal Article
Option pricing by Esscher transforms.

Abstract:

The Esscher transform is a time-honored tool in actuarial science. This paper shows that the Esscher transform is also an efficient technique for valuing derivative securities if the logarithms of the prices of the primitive securities are governed by certain stochastic processes with stationary and independent increments. Th... The Esscher transform is a time-honored tool in actuarial science. This paper shows that the Esscher transform is also an efficient technique for valuing derivative securities if the logarithms of the prices of the primitive securities are governed by certain stochastic processes with stationary and independent increments. This family of processes includes the Wiener process, the Poisson process, the gamma process, and the inverse Gaussian process. An Esscher transform of such a stock-price process induces an equivalent probability measure on the process. The Esscher parameter or parameter vector is determined so that the discounted price of each primitive security is a martingale under the new probability measure. The price of any derivative security is simply calculated as the expectation, with respect to the equivalent martingale measure, of the discounted payoffs. Straightforward consequences of the method of Esscher transforms include, among others, the celebrated Black-Scholes optionpricing formula, the binomial option-pricing formula, and formulas for pricing options on the maximum and minimum of multiple risky assets. Tables of numerical values for the prices of certain European call options (calculated according to four different models for stock-price movements) are also provided. read more read less

Topics:

Esscher transform (82%)82% related to the paper, Martingale (probability theory) (55%)55% related to the paper, Valuation of options (54%)54% related to the paper, Probability measure (53%)53% related to the paper, Wiener process (52%)52% related to the paper
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677 Citations
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Frequently asked questions

1. Can I write Insurance: Mathematics and Economics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Insurance: Mathematics and Economics guidelines and auto format it.

2. Do you follow the Insurance: Mathematics and Economics guidelines?

Yes, the template is compliant with the Insurance: Mathematics and Economics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Insurance: Mathematics and Economics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Insurance: Mathematics and Economics citation style.

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Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Insurance: Mathematics and Economics.

5. Can I use a manuscript in Insurance: Mathematics and Economics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Insurance: Mathematics and Economics that you can download at the end.

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Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's Insurance: Mathematics and Economics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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12. Is Insurance: Mathematics and Economics's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Insurance: Mathematics and Economics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Insurance: Mathematics and Economics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Insurance: Mathematics and Economics?

The 5 most common citation types in order of usage for Insurance: Mathematics and Economics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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16. Can I download Insurance: Mathematics and Economics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Insurance: Mathematics and Economics Endnote style according to Elsevier guidelines.

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