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Risks
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Stochastic Control in Insurance and Finance: Modelling and Numerical Analysis
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Stochastic Control in Insurance and Finance: Modelling and Numerical Analysis

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 7370

Special Issue Editor

Dr. Zhuo Jin

E-Mail Website
Guest Editor
Centre for Actuarial Studies, Department of Economics, The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: stochastic control; actuarial science; financial mathematics; numerical methods in stochastic systems

Special Issue Information

Dear Colleagues,

Stochastic control theory is a powerful tool to investigate the decision-making problems arising from insurance and finance. Different assumptions regarding the control variables lead to various optimization formulations. Depending on the complexity of the stochastic systems, analytical derivation and numerical methods—especially the recent breakthrough machine learning techniques—represent alternative approaches to tackling problems.

This Special Issue aims to collect high-quality research papers on theoretical and numerical methods for solving stochastic optimization problems in insurance and finance. We encourage submissions that are related, but not limited, to the following topics:

  • Dividend
  • Reinsurance
  • Optimal investment/consumption
  • Retirement planning
  • Optimal stopping
  • Optimal contracting
  • Risk sharing
  • Stochastic games
  • Machine learning methods
  • Computational methods in stochastic systems

Dr. Zhuo Jin
Guest Editor

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 submissions that pass pre-check are 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 monthly 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 1800 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

  • dividend
  • reinsurance
  • optimal investment/consumption
  • retirement planning
  • optimal stopping
  • optimal contracting
  • risk sharing
  • stochastic games
  • machine learning methods
  • computational methods in stochastic systems

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Published Papers (2 papers)

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Research

14 pages, 1350 KiB  
Article
Examination of Interest-Growth Differentials and the Risk of Sovereign Insolvency
by Jussi Lindgren
Risks 2021, 9(4), 75; https://doi.org/10.3390/risks9040075 - 14 Apr 2021
Cited by 2 | Viewed by 4312
Abstract
The objective of this research was to demonstrate the (nonlinear) risks of sovereign insolvency and explore the applicability of stochastic modeling in public debt management, given a structural economic model of stochastic government debt dynamics. A stochastic optimal control model was developed to [...] Read more.
The objective of this research was to demonstrate the (nonlinear) risks of sovereign insolvency and explore the applicability of stochastic modeling in public debt management, given a structural economic model of stochastic government debt dynamics. A stochastic optimal control model was developed to model public debt dynamics based on the debt accounting identity, where the interest-growth differential obeys a continuous random process. This stochasticity represents both the interest rate risk of public debt and the variability of the growth rate of the nominal Gross Domestic Product combined. The optimal fiscal policy was analyzed in terms of the model parameters. The model was simulated, and results were visualized. The insolvency risk was demonstrated by examining the variance of the optimal process. The model was amended with hidden credit risk premia and fiscal multipliers, which forces the debt dynamics to be nonlinear in the debt ratio. The results, on the other hand, confirm that the volatility of the interest-growth differential is crucial in terms of sovereign solvency and in addition, it demonstrates the large risks stemming from the multiplier effect, which underlines the need for prudent debt management and fiscal policy. Full article
(This article belongs to the Special Issue Stochastic Control in Insurance and Finance: Modelling and Numerical Analysis)
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10 pages, 348 KiB  
Article
Quantile Credibility Models with Common Effects
by Wei Wang, Limin Wen, Zhixin Yang and Quan Yuan
Risks 2020, 8(4), 100; https://doi.org/10.3390/risks8040100 - 25 Sep 2020
Cited by 2 | Viewed by 2292
Abstract
Different from classical Bühlmann and Bühlmann Straub credibility models in which independence between different risks are assumed, this paper takes dependence between risks into consideration and extends the classical Bühlmann model by introducing a common stochastic shock element. What is more, instead of [...] Read more.
Different from classical Bühlmann and Bühlmann Straub credibility models in which independence between different risks are assumed, this paper takes dependence between risks into consideration and extends the classical Bühlmann model by introducing a common stochastic shock element. What is more, instead of relying on complete information of historical data, we aim to derive the premium using quantile of the available data. By the method of linear regression, we manage to obtain the quantile credibility premium with common effects. Our result is the generalization of existing results in credibility theory. Both quantile credibility model proposed by Pitselis (2013) and credibility premium for models with dependence induced by common effects obtained by Wen et al. (2009) are special cases of our model. Numerical simulations are also presented to illustrate the impact of quantile credibility with common effect. Full article
(This article belongs to the Special Issue Stochastic Control in Insurance and Finance: Modelling and Numerical Analysis)
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