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Letteratura scientifica selezionata sul tema "Risk (Insurance) – Mathematical models"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 29 gennaio 2023

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Articoli di riviste sul tema "Risk (Insurance) – Mathematical models"

1

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 in the market. In this regard, the study involves systemizing risks at various levels of the economic system, generalizing factors for the motivation of insurance consumers, developing descriptive and economic-mathematical models for the behavior of economic entities in risky situations.The results obtained represent a behavioral model of interactions among insurance market entities, which determines opportunities for efficient and mutually beneficial coordination of their activities. The developed model includes the following elements: structured individual and institutional frames “Insurance”; a professional index of interest in insurance presented in the form of a mathematical model; methodology for governing the relationships among insurance participants in the digital environment.The recommendations enable predictions of the situation in the insurance market and allow most accurately defining the consumer needs in the conditions of market changes.

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2

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 risk modeling allowed us to better measure certain events, but did not allow us to predict them accurately due to a lack of information. Therefore, good modeling of the risk determinants makes it possible to modify the probability associated with the occurrence of a risk. While it cannot predict exactly when a risk will occur, it can help make decisions that will reduce its effects. Keywords: Basic insurance, Life insurance, Mathematical models, Financial risk, Biometric function.

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3

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 that one insurance company can not fully assume. In the case of a portfolio with very high risks, the company may limit their acceptance, or give part of the reinsurance. The choice of path depends entirely on the company’s policy and type of insurance.This paper considers the main types of reinsurance and their mathematical models. An analysis of the probability of bankruptcy and the optimal use of a particular type of reinsurance are provided.There are also some examples and main results of research on this topic. After all, today the insurance industry is actively gaining popularity both in Ukraine and around the world. Accordingly, with a lot of competition, every insurer wants to get the maximum profit with minimal e↵ort.

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4

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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5

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 have been able to construct sophisticated mathematical-statistical models of risk. Research limitations/implications – However, the relation between anticipated risks and insurance purchase behaviour has not received sufficient attention. In the present study, starting from the premise that societies may be studied by examining their fears, the authors posit that these fears are represented in the insurance premiums people buy for being protected. Originality/value – Insurance purchase behaviour at any particular point in time is a measure of what a society considers to be risky at that time and is a key source of information for tourism managers.

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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 itself.

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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 order to calculate the capacity of the stock market’s risk insurance sector in the context of the two above mentioned types, the authors apply the models that are widely used in the economic-mathematical analysis. For mathematical calculations we used 31 absolute indicators of the characteristics of the state of the stock and insurance markets, as well as some macroeconomic indicators. When forming an array of input data for mathematical calculations we used annual values of absolute indicators for the period 2005–2015 were used. For the adequacy of the received calculations the normalization of the selected indicators was carried out. All indicators were divided into two groups: stimulators and de-stimulators. The normalization of stimulator indicators was carried out by the method of natural normalization, and of de-stimulator indicators – according to the Savage formula. The capacity of the segment of the new type of insurance was determined by the authors as the maximum possible amount of insurance premiums that insurers can get in the process of implementing a new insurance product based on the current state of development of the insurance market. The capacity of the sector of the new type of insurance was presented as a function of the main component (an indicator that directly characterizes the created segment) and the corrective component (a set of indicators characterizing the segments created indirectly). The weight coefficients of the corrective component were determined by using the Fischer’s formula. As a result of the calculations, the authors obtained the data on the prospects of simultaneous introduction for the stock and insurance markets of such types of insurance as a professional liability insurance of depositories and an insurance of individual investors on the stock market.

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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 face the risk of infection and death due to the disease. Using actuarial techniques and principles, we determine some interesting features of the model, namely, (a) financial obligations of the parties, (b) present value of premiums, (c) quantum of claims by infected policy holders (PHs), (d) quantum of claims on behalf of deceased PHs, (e) cumulative insurance reserve for annuity and (f) lump sum plan. To check the risk of insolvency, premium adjustment for the PHs is also considered.

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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 stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.

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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 of financial risk management when taking into account errors in estimating parameters of mathematical models. The purpose of our study is to create a mathematical model that can be used to assess the risk reinsurance process. We will create a mathematical model of the risk reinsurance process using the following steps: 1. Identify all of the relevant variables in our analysis. 2. Determine how these variables interact with each other and come to a conclusion about how they influence each other's values. 3. Find equations that represent these relationships between the variables and solve for their values with those equations. 4. Test these models against real data from known cases in order to ensure that they work as expected, then use them for future studies or applications requiring this type of modeling technique.

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Tesi sul tema "Risk (Insurance) – Mathematical models"

1

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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2

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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4

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 pursued in this thesis for evaluating Gerber-Shiu functions. Fourier-cosine method is a numerical method based on Fourier transform and has been very popular in option pricing since its introduction. It then evolves into a number of extensions, and we here adopt its spirit to insurance risk theory. In this thesis, the proposed approximant of Gerber-Shiu functions under an L´evy subordinator model has O(n) computational complexity in comparison with that of O(n log n) via the usual numerical Fourier inversion. Also, for Gerber-Shiu functions within the proposed refined Sobolev space, an explicit error bound is given and error bound of this type is seemingly absent in the literature. Furthermore, the error bound for our estimation can be further enhanced under extra assumptions, which are not immediate from Fang and Oosterlee’s works. We also suggest a robust method on the estimation of ruin probabilities (one special class of Gerber-Shiu functions) based on the moments of both claim size and claim arrival distributions. Rearrangement inequality will also be adopted to amplify the use of our Fourier-cosine method in ruin probability, resulting in an effective global estimation. Finally, the effectiveness of our result will be further illustrated in a number of numerical studies and our enhanced error bound is apparently optimal in our demonstration; more precisely, empirical evidence exhibiting the biggest possible error convergence rate agrees with our theoretical conclusion.<br>published_or_final_version<br>Mathematics<br>Master<br>Master of Philosophy

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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 below zero, we derive a discrete approximation procedure to calculate the expected discounted dividends until ruin under such a model. A thorough discussion of application in proportional reinsurance with numerical examples is provided as well as an examination of the joint optimal dividend barrier for the bivariate process. The second risk process is a semi-Markovian dual risk process. Assuming that the dependence among innovations and waiting times is driven by a Markov chain, we analyze a quantity resembling the Gerber-Shiu expected discounted penalty function that incorporates random variables defined before and after the time of ruin, such as the minimum surplus level before ruin and the time of the first gain after ruin. General properties of the function are studied, and some exact results are derived upon distributional assumptions on either the inter-arrival times or the gain amounts. Applications in a perpetual insurance and the last inter-arrival time before ruin are given along with some numerical examples.<br>published_or_final_version<br>Statistics and Actuarial Science<br>Master<br>Master of Philosophy

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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 certain delay in the execution of decisions. In this thesis, such Parisian concept is utilized on two different aspects. The first one is to incorporate this concept on defining ruin, leading to the introduction of Parisian ruin time. Under such a setting, a business is considered ruined only when the surplus level stays negative continuously for a prescribed length of time. The case for a fixed delay is considered. Both the renewal risk model and the dual renewal risk model are studied. Under a mild distributional assumption that either the inter arrival time or the claim size is exponentially distributed (while keeping the other arbitrary), the Laplace transform to the Parisian ruin time is derived. Numerical example is performed to confirm the reasonableness of the results. The methodology in obtaining the Laplace transform to the Parisian ruin time is also demonstrated to be useful in deriving the joint distribution to the number of negative surplus causing or without causing Parisian ruin. The second contribution is to incorporate this concept on the decision for dividend payment. Specifically, a business only pays lump-sum dividends when the surplus level stays above certain threshold continuously for a prescribed length of time. The case for a fixed and an Erlang(n) delay are considered. The dual compound Poisson risk model is studied. Laplace transform to the ordinary ruin time is derived. Numerical examples are performed to illustrate the results.<br>published_or_final_version<br>Statistics and Actuarial Science<br>Master<br>Master of Philosophy

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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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Più fonti

Libri sul tema "Risk (Insurance) – Mathematical models"

1

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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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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Capitoli di libri sul tema "Risk (Insurance) – Mathematical models"

1

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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Atti di convegni sul tema "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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