Special Issue "Young Researchers in Insurance and Risk Management"

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 15 December 2019

Special Issue Editors

Guest Editor
Dr. Thorsten Moenig

Fox School of Business, Temple University, Philadelphia, PA 19122, USA
Website | E-Mail
Interests: actuarial science; variable annuities; insurance economics
Guest Editor
Dr. Albert Cohen

Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
E-Mail
Interests: Financial mathematics; financial markets; actuarial science; insurance; financial risk management

Special Issue Information

Dear Colleagues,

Young minds are a wonderful source of fresh, disruptive ideas in the definition, pricing, and mitigation of risk. Unencumbered by traditional approaches to risk management and insurance, this next generation is primed to think about risk in emerging areas such as cyber-insurance and autonomous cars, and offer new insights on traditional actuarial topics. This special issue seeks to put a spotlight on the next generation of actuarial scientists, risk managers, and quants who may not have had the chance to see their work disseminated in a leading journal such as Risks.

Our goal with this Special Issue is to encourage postdoctoral fellows, graduate, and undergraduate students (with or without PhD/Fellow/Associate co-authors) to submit their work to us in the confidence that they will be reviewed with care by leading academics and practitioners in the field. We hope this experience will encourage our next generation of actuaries and risk managers to keep transforming our field for the betterment of society.

Sincerely

Dr. Albert Cohen
Dr. Thorsten Moenig
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Young Researchers
  • Actuarial Science
  • Risk Management
  • Insurance Economics

Published Papers (3 papers)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

Open AccessArticle Efficient Retirement Portfolios: Using Life Insurance to Meet Income and Bequest Goals in Retirement
Received: 26 October 2018 / Revised: 21 December 2018 / Accepted: 4 January 2019 / Published: 18 January 2019
PDF Full-text (2124 KB) | HTML Full-text | XML Full-text
Abstract
Life Insurance Retirement Plans (LIRPs) offer tax-deferred cash value accumulation, tax-free withdrawals (if properly structured), and a tax-free death benefit to beneficiaries. Thus, LIRPs share many of the tax advantages of other retirement savings vehicles but with less restrictive limitations on income and [...] Read more.
Life Insurance Retirement Plans (LIRPs) offer tax-deferred cash value accumulation, tax-free withdrawals (if properly structured), and a tax-free death benefit to beneficiaries. Thus, LIRPs share many of the tax advantages of other retirement savings vehicles but with less restrictive limitations on income and contributions. Opinions are mixed about the effectiveness of LIRPs; some financial advisers recommend them enthusiastically, while others are more skeptical. In this paper, we examine the potential of LIRPs to meet both income and bequest needs in retirement. We contrast retirement portfolios that include a LIRP with those that include only investment products with no life insurance. We consider different issue ages, face amounts, and withdrawal patterns. We simulate market scenarios and we demonstrate that portfolios that include LIRPs yield higher legacy potential and smaller income risk than those that exclude it. Thus, we conclude that the inclusion of a LIRP can improve financial outcomes in retirement. Full article
(This article belongs to the Special Issue Young Researchers in Insurance and Risk Management)
Figures

Figure 1

Open AccessArticle Using Neural Networks to Price and Hedge Variable Annuity Guarantees
Received: 1 November 2018 / Revised: 28 November 2018 / Accepted: 20 December 2018 / Published: 23 December 2018
PDF Full-text (1647 KB) | HTML Full-text | XML Full-text
Abstract
This paper explores the use of neural networks to reduce the computational cost of pricing and hedging variable annuity guarantees. Pricing these guarantees can take a considerable amount of time because of the large number of Monte Carlo simulations that are required for [...] Read more.
This paper explores the use of neural networks to reduce the computational cost of pricing and hedging variable annuity guarantees. Pricing these guarantees can take a considerable amount of time because of the large number of Monte Carlo simulations that are required for the fair value of these liabilities to converge. This computational requirement worsens when Greeks must be calculated to hedge the liabilities of these guarantees. A feedforward neural network is a universal function approximator that is proposed as a useful machine learning technique to interpolate between previously calculated values and avoid running a full simulation to obtain a value for the liabilities. We propose methodologies utilizing neural networks for both the tasks of pricing as well as hedging four different varieties of variable annuity guarantees. We demonstrated a significant efficiency gain using neural networks in this manner. We also experimented with different error functions in the training of the neural networks and examined the resulting changes in network performance. Full article
(This article belongs to the Special Issue Young Researchers in Insurance and Risk Management)
Figures

Figure 1

Open AccessArticle Credibility Methods for Individual Life Insurance
Received: 17 September 2018 / Revised: 5 November 2018 / Accepted: 15 November 2018 / Published: 11 December 2018
PDF Full-text (446 KB) | HTML Full-text | XML Full-text
Abstract
Credibility theory is used widely in group health and casualty insurance. However, it is generally not used in individual life and annuity business. With the introduction of principle-based reserving (PBR), which relies more heavily on company-specific experience, credibility theory is becoming increasingly important [...] Read more.
Credibility theory is used widely in group health and casualty insurance. However, it is generally not used in individual life and annuity business. With the introduction of principle-based reserving (PBR), which relies more heavily on company-specific experience, credibility theory is becoming increasingly important for life actuaries. In this paper, we review the two most commonly used credibility methods: limited fluctuation and greatest accuracy (Bühlmann) credibility. We apply the limited fluctuation method to M Financial Group’s experience data and describe some general qualitative observations. In addition, we use simulation to generate a universe of data and compute Limited Fluctuation and greatest accuracy credibility factors for actual-to-expected (A/E) mortality ratios. We also compare the two credibility factors to an intuitive benchmark credibility measure. We see that for our simulated data set, the limited fluctuation factors are significantly lower than the greatest accuracy factors, particularly for low numbers of claims. Thus, the limited fluctuation method may understate the credibility for companies with favorable mortality experience. The greatest accuracy method has a stronger mathematical foundation, but it generally cannot be applied in practice because of data constraints. The National Association of Insurance Commissioners (NAIC) recognizes and is addressing the need for life insurance experience data in support of PBR—this is an area of current work. Full article
(This article belongs to the Special Issue Young Researchers in Insurance and Risk Management)
Figures

Figure 1

Risks EISSN 2227-9091 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top