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Relevant bibliographies by topics / Risk (Insurance) – Mathematical models

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Academic literature on the topic 'Risk (Insurance) – Mathematical models'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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Journal articles on the topic "Risk (Insurance) – Mathematical models"

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Prokopjeva, Evgenija, Evgeny Tankov, Tatyana Shibaeva, and Elena Perekhozheva. "Behavioral models in insurance risk management." Investment Management and Financial Innovations 18, no. 4 (October 21, 2021): 80–94. http://dx.doi.org/10.21511/imfi.18(4).2021.08.

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Behavioral characteristics attributed to consumers of insurance services are a relevant factor for analyzing the current situation in the insurance market and developing effective strategies for insurers’ actions. In turn, considering these characteristics allows the insurer to be more successful in the highly competitive field, achieving mutual satisfaction in interacting with the customer. This study is aimed to develop cognitive models of the situation (frame) “Insurance”, taking into account the specifics of the Russian insurance market and systemic factors affecting participants’ behavior

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Drissi, Ramzi. "Mathematical Risk Modeling: an Application in Three Cases of Insurance Contracts." International Journal of Advances in Management and Economics 8, no. 6 (October 30, 2019): 01–10. http://dx.doi.org/10.31270/ijame/v08/i06/2019/1.

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Risk is often defined as the degree of uncertainty regarding the future. This general definition of risk can be extended to define different types of risks according to the source of the underlying uncertainty. In this context, the objective of this paper is to mathematically model risks in insurance. The choice of methods and techniques that allow the construction of the model significantly influence the responses obtained. We approach these different issues by modeling risks in three base cases: basic insurance of goods, life insurance, and financial risk insurance. Our findings show that ri

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Zhuk, Tetyana. "Mathematical Models of Reinsurance." Mohyla Mathematical Journal 3 (January 29, 2021): 31–37. http://dx.doi.org/10.18523/2617-70803202031-37.

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Insurance provides financial security and protection of the independence of the insured person. Its principles are quite simple: insurance protects investments, life and property. You regularly pay a certain amount of money in exchange for a guarantee that in case of unforeseen circumstances (accident, illness, death, property damage) the insurance company will protect you in the form of financial compensation.Reinsurance, in turn, has a significant impact on ensuring the financial stability of the insurer. Because for each type of insurance there is a possibility of large and very large risks

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Chen, Liansheng, and Jinhua Tao. "Mixed Insurance Risk Models." Missouri Journal of Mathematical Sciences 8, no. 1 (February 1996): 3–10. http://dx.doi.org/10.35834/1996/0801003.

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Korstanje, Maximiliano Emanuel, and Babu P. George. "What does insurance purchase behaviour say about risks?" International Journal of Disaster Resilience in the Built Environment 6, no. 3 (September 14, 2015): 289–99. http://dx.doi.org/10.1108/ijdrbe-09-2012-0030.

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Purpose – This paper aims to explore the world of insurances as rites of adaptancy and resiliency before risk and disasters. The research on risks, both perceived and real, has become a frequent theme of academic research in the recent past. Design/methodology/approach – The information given by the superintendencia de Seguros de Buenos Aires involves 100 per cent of the insurances companies of Argentina. The reading of insurance demands corresponds with a new method in the studies of risks. Findings – Using advanced probability theory and quantitative techniques, risk management researchers h

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Lefèvre, Claude, and Philippe Picard. "RISK MODELS IN INSURANCE AND EPIDEMICS: A BRIDGE THROUGH RANDOMIZED POLYNOMIALS." Probability in the Engineering and Informational Sciences 29, no. 3 (March 23, 2015): 399–420. http://dx.doi.org/10.1017/s0269964815000066.

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The purpose of this work is to construct a bridge between two classical topics in applied probability: the finite-time ruin probability in insurance and the final outcome distribution in epidemics. The two risk problems are reformulated in terms of the joint right-tail and left-tail distributions of order statistics for a sample of uniforms. This allows us to show that the hidden algebraic structures are of polynomial type, namely Appell in insurance and Abel–Gontcharoff in epidemics. These polynomials are defined with random parameters, which makes their mathematical study interesting in itse

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Shkolnyk, Inna, Eugenia Bondarenko, and Valery Balev. "Estimation of the capacity of the Ukrainian stock market’s risk insurance sector." Insurance Markets and Companies 8, no. 1 (November 24, 2017): 34–47. http://dx.doi.org/10.21511/ins.08(1).2017.04.

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The purpose of the article is to determine the degree of financial interaction between the stock and insurance market, or, in other words, to determine the potential capacity of the stock market’s risk insurance sector for the Ukrainian insurance market. The authors examine the insurance not of all possible risks on the stock market, but only the most potentially important for the development of the stock market at this stage of economic development: insurance of professional risks of depositories and insurance of individual investments of individuals – participants of the stock market. In ord

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Nkeki, C. I., and G. O. S. Ekhaguere. "Some actuarial mathematical models for insuring the susceptibles of a communicable disease." International Journal of Financial Engineering 07, no. 02 (May 18, 2020): 2050014. http://dx.doi.org/10.1142/s2424786320500140.

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Using epidemiological and actuarial analysis, this paper formulates some new actuarial mathematical models, called S-I-DR-S models, for insuring the susceptibles of a population exposed to a communicable disease. Epidemiologically, the population is structured into four demographic groups, namely: susceptibles [Formula: see text], infectives [Formula: see text], diseased [Formula: see text] and recovered [Formula: see text], with the latter automatically re-entering the group of susceptibles [Formula: see text]. The insurance policies are targeted at the members of the susceptible group who fa

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Singh, Amrik, and K. R. Ramkumar. "Risk assessment for health insurance using equation modeling and machine learning." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 2 (July 26, 2021): 201–25. http://dx.doi.org/10.3233/kes-210065.

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Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as ‘inverse problem’ the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stabl

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Khanlarzadeh, Sarvinaz. "Mathematical Modeling of the Risk Reinsurance Process." WSEAS TRANSACTIONS ON MATHEMATICS 21 (June 20, 2022): 447–60. http://dx.doi.org/10.37394/23206.2022.21.52.

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This paper presents a method for assessing financial risks and managing them to optimize the decision-making process. It is shown that the type of economic entity at risk and its activities in the financial market affect the specifics of financial risk management, which can be classified into three main groups: hedging, diversification, and insurance. The main instruments used for this purpose are also identified. Special attention is given to the dynamic properties of financial flows arising from the simulation of artificial financial instruments, as well as to their influence on the results

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Dissertations / Theses on the topic "Risk (Insurance) – Mathematical models"

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蕭德權 and Tak-kuen Siu. "Risk measures in finance and insurance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31242297.

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Gong, Qi, and 龔綺. "Gerber-Shiu function in threshold insurance risk models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40987966.

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Wan, Lai-mei. "Ruin analysis of correlated aggregate claims models." Thesis, Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B30705708.

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Chau, Ki-wai, and 周麒偉. "Fourier-cosine method for insurance risk theory." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/208586.

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In this thesis, a systematic study is carried out for effectively approximating Gerber-Shiu functions under L´evy subordinator models. It is a hardly touched topic in the recent literature and our approach is via the popular Fourier-cosine method. In theory, classical Gerber-Shiu functions can be expressed in terms of an infinite sum of convolutions, but its inherent complexity makes efficient computation almost impossible. In contrast, Fourier transforms of convolutions could be evaluated in a far simpler manner. Therefore, an efficient numerical method based on Fourier transform is pursue

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Kwan, Kwok-man, and 關國文. "Ruin theory under a threshold insurance risk model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38320034.

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Liu, Luyin, and 劉綠茵. "Analysis of some risk processes in ruin theory." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/195992.

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In the literature of ruin theory, there have been extensive studies trying to generalize the classical insurance risk model. In this thesis, we look into two particular risk processes considering multi-dimensional risk and dependent structures respectively. The first one is a bivariate risk process with a dividend barrier, which concerns a two-dimensional risk model under a barrier strategy. Copula is used to represent the dependence between two business lines when a common shock strikes. By defining the time of ruin to be the first time that either of the two lines has its surplus level be

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Chen, Yiqing, and 陳宜清. "Study on insurance risk models with subexponential tails and dependence structures." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841768.

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Lin, Erlu, and 林尔路. "Analysis of dividend payments for insurance risk models with correlated aggregate claims." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203992.

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Wong, Tsun-yu Jeff, and 黃峻儒. "On some Parisian problems in ruin theory." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206448.

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Traditionally, in the context of ruin theory, most judgements are made on an immediate sense. An example would be the determination of ruin, in which a business is declared broke right away when it attains a negative surplus. Another example would be the decision on dividend payment, in which a business pays dividends whenever the surplus level overshoots certain threshold. Such scheme of decision making is generally being criticized as unrealistic from a practical point of view. The Parisian concept is therefore invoked to handle this issue. This idea is deemed more realistic since it allows

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Zhu, Jinxia, and 朱金霞. "Ruin theory under Markovian regime-switching risk models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203980.

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Books on the topic "Risk (Insurance) – Mathematical models"

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1957-, Willmot G. E., ed. Insurance risk models. Schaumburg, Ill: Society of Acturaries, 1992.

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Insurance risk and ruin. Cambridge, UK: Cambridge University Press, 2005.

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Heilmann, Wolf-Rüdiger. Fundamentals of risk theory. Karlsruhe: VVW, 1988.

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Schmidli, Hanspeter. Characteristics of ruin probabilities in classical risk models with and without investment, Cox risk models and perturbed risk models. Århus, Denmark: University of Aarhus, Dept. of Theoretical Statistics, Institute of Mathematical Sciences, 2000.

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author, Frey Rüdiger, and Embrechts Paul 1953 author, eds. Quantitative risk management: Concepts, techniques and tools. Princeton, NJ: Princeton University Press, 2015.

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Rüdiger, Frey, and Embrechts Paul 1953-, eds. Quantitative risk management: Concepts, techniques, and tools. Princeton, N.J: Princeton University Press, 2005.

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Aspects of risk theory. New York: Springer-Verlag, 1991.

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Schlesinger, Harris. Extending Arrow-Pratt risk premiums. Berlin: IIM/Industrial Policy, Wissenschaftszentrum Berlin, 1985.

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Individuelle Zahlungsbereitschaft für Versicherungsschutz und Messung der Risikoeinstellung bei der Versicherungsentscheidung: Eine entscheidungstheoretische Analyse. Frankfurt am Main: P. Lang, 1993.

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10

Burney, S. M. Aqil. Risk theory and insurance: A stochastic approach. Karachi: Bureau of Composition, Compilation & Translation, University of Karachi, 2002.

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Book chapters on the topic "Risk (Insurance) – Mathematical models"

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Bernhard, Pierre, Jacob C. Engwerda, Berend Roorda, J. M. Schumacher, Vassili Kolokoltsov, Patrick Saint-Pierre, and Jean-Pierre Aubin. "Asset and Liability Insurance Management (ALIM) for Risk Eradication." In The Interval Market Model in Mathematical Finance, 319–35. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-0-8176-8388-7_18.

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Swishchuk, Anatoly. "Stochastic Stability and Optimal Control of Semi-Markov Risk Processes in Insurance Mathematics." In Semi-Markov Models and Applications, 313–23. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4613-3288-6_19.

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Shimizu, Yasutaka. "Lévy Insurance Risk Models." In Asymptotic Statistics in Insurance Risk Theory, 25–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-9284-0_2.

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Asmussen, Søren, and Mogens Steffensen. "Chapter V: Markov Models in Life Insurance." In Risk and Insurance, 113–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35176-2_5.

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Gomes, M. Ivette, and Dinis D. Pestana. "Large Claims — Extreme Value Models." In Insurance and Risk Theory, 301–23. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4620-0_20.

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Moriconi, Franco. "Analyzing Default-Free Bond Markets by Diffusion Models." In Financial Risk in Insurance, 25–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57846-5_2.

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Centeno, Lourdes. "Some Mathematical Aspects of Combining Proportional and Non-Proportional Reinsurance." In Insurance and Risk Theory, 247–66. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4620-0_16.

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Koller, Michael. "Cash Flows and the Mathematical Reserve." In Stochastic Models in Life Insurance, 29–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28439-7_4.

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Brannigan, Vincent, and Carol Smidts. "Risk Based Regulation Using Mathematical Risk Models." In Probabilistic Safety Assessment and Management ’96, 721–25. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3409-1_115.

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Fleming, Wendell H. "Optimal Investment Models and Risk Sensitive Stochastic Control." In Mathematical Finance, 75–88. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4757-2435-6_6.

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Conference papers on the topic "Risk (Insurance) – Mathematical models"

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Margaretha, Helena, Melissa Susanto, Earlitha Olivia Lionel, and Ferry V. Ferdinand. "An actuarial model of stroke long term care insurance with obesity as a risk factor." In PROCEEDINGS OF THE 8TH SEAMS-UGM INTERNATIONAL CONFERENCE ON MATHEMATICS AND ITS APPLICATIONS 2019: Deepening Mathematical Concepts for Wider Application through Multidisciplinary Research and Industries Collaborations. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5139124.

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Yang, Hailiang. "Risk: From Insurance to Finance." In Proceedings of the International Conference on Mathematical Finance. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799579_0019.

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Piromsopa, Krerk, Tomas Klima, and Lukas Pavlik. "Designing Model for Calculating the Amount of Cyber Risk Insurance." In 2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI). IEEE, 2017. http://dx.doi.org/10.1109/mcsi.2017.41.

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Chapados, Nicolas, Charles Dugas, Pascal Vincent, and Réjean Ducharme. "Scoring Models for Insurance Risk Sharing Pool Opimization." In 2008 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2008. http://dx.doi.org/10.1109/icdmw.2008.132.

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Ma, Jin, and Xiaodong Sun. "Sharp Estimates of Ruin Probabilities for Insurance Models Involving Investments." In Proceedings of the International Conference on Mathematical Finance. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799579_0007.

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Alwie, Ferren, Mila Novita, and Suci Fratama Sari. "Risk measurement for insurance sector with credible tail value-at-risk." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES AND TECHNOLOGY 2018 (MATHTECH2018): Innovative Technologies for Mathematics & Mathematics for Technological Innovation. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136427.

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Brigo, Damiano, and Clément Piat. "Static Versus Adapted Optimal Execution Strategies in Two Benchmark Trading Models." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0010.

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Brigo, Damiano, Thomas Hvolby, and Frédéric Vrins. "Wrong-Way Risk Adjusted Exposure: Analytical Approximations for Options in Default Intensity Models." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0002.

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Franke, Ulrik, and Joachim Draeger. "Two simple models of business interruption accumulation risk in cyber insurance." In 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). IEEE, 2019. http://dx.doi.org/10.1109/cybersa.2019.8899678.

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Jensen, Emily, Maya Luster, Hansol Yoon, Brandon Pitts, and Sriram Sankaranarayanan. "Mathematical Models of Human Drivers Using Artificial Risk Fields." In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022. http://dx.doi.org/10.1109/itsc55140.2022.9922389.

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