Готові списки джерел за темами / Risk (Insurance)

Добірка наукової літератури з теми "Risk (Insurance)"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 5 березня 2023

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Статті в журналах з теми "Risk (Insurance)"

1

Mirjalil, Iskandarov Abdurasul. "AGRICULTURAL RISK INSURANCE." European International Journal of Multidisciplinary Research and Management Studies 02, no. 09 (September 1, 2022): 56–57. http://dx.doi.org/10.55640/eijmrms-02-09-12.

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Agriculture is a sector facing many risks. Agricultural risks have become the object of many scientific studies as a result of their direct impact on the food security of the state and thereby the standard of living of the population. Agricultural risks have their own characteristics. Also, the classification of agricultural risks is specific to industry risks. The article considers two main principles of risk classification and proposes a classification model based on the principle of grouping.
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2

Mualem, Elinor, and Abraham Zaks. "Risk premiums in life insurance." Insurance Markets and Companies 10, no. 1 (January 31, 2019): 1–8. http://dx.doi.org/10.21511/ins.10(1).2019.01.

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3

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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4

Chi, Yichun, and Ken Seng Tan. "OPTIMAL INCENTIVE-COMPATIBLE INSURANCE WITH BACKGROUND RISK." ASTIN Bulletin 51, no. 2 (March 29, 2021): 661–88. http://dx.doi.org/10.1017/asb.2021.7.

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ABSTRACTIn this paper, the optimal insurance design is studied from the perspective of an insured, who faces an insurable risk and a background risk. For the reduction of ex post moral hazard, alternative insurance contracts are asked to satisfy the principle of indemnity and the incentive-compatible condition. As in the literature, it is assumed that the insurer calculates the insurance premium solely on the basis of the expected indemnity. When the insured has a general mean-variance preference, an explicit form of optimal insurance is derived explicitly. It is found that the stochastic dependence between the background risk and the insurable risk plays a critical role in the insured’s risk transfer decision. In addition, the optimal insurance policy can often change significantly once the incentive-compatible constraint is removed.
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5

Zolotukhin, Aleksei. "Legal nature of business risk insurance." SHS Web of Conferences 50 (2018): 01227. http://dx.doi.org/10.1051/shsconf/20185001227.

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This paper raises the question of the legal nature of business risk insurance. A conclusion is made that the legal understanding of business risk insurance should be built upon the unity of the actual content of this type of insurance and its legal form. The presence of a special subject on the policy holder’s side in business risk insurance determines the features of the object of such type of insurance, which is represented by an entrepreneur’s insurable interests related to one’s business activity. In the legal sense, insurance is a legal relationship and is characterized by a bilateral connection between the insurer and the policyholder that manifests in a unity of their subjective rights and responsibilities. Two aspects of insurance indicate not the existence of two independent notions free from each other: insurance in the economic sense and insurance in the legal sense, but rather demonstrate two aspects of one phenomenon that exist in an inseparable unity. In the course of comparing business risk insurance and liability insurance, the author comes to a conclusion that unlike liability insurance, business risk insurance is connected not with the policyholder’s wrongful behavior but, on the contrary, with the possible dishonesty of their contracting party.
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6

Chi, Yichun, and Wei Wei. "OPTIMUM INSURANCE CONTRACTS WITH BACKGROUND RISK AND HIGHER-ORDER RISK ATTITUDES." ASTIN Bulletin 48, no. 3 (April 25, 2018): 1025–47. http://dx.doi.org/10.1017/asb.2018.20.

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AbstractIn this paper, we study an optimal insurance problem in the presence of background risk from the perspective of an insured with higher-order risk attitudes. We introduce several useful dependence notions to model positive dependence structures between the insurable risk and background risk. Under these dependence structures, we compare insurance contracts of different forms in higher-order risk attitudes and establish the optimality of stop-loss insurance form. We also explicitly derive the optimal retention level. Finally, we carry out a comparative analysis and investigate how the change in the insured's initial wealth or background risk affects the optimal retention level.
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7

Rogoziński, Dawid. "Securing Bank Claims by means of Credit Risk Insurance versus Insurance Recourse." Prawo Asekuracyjne 3, no. 100 (September 15, 2019): 47–61. http://dx.doi.org/10.5604/01.3001.0013.5733.

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This article examines the specific nature of the insurance of risks directly related to lending. The dynamic development of cooperation between banking and insurance industries has resulted not only in a greater popularity of the coverages already existing on the market, but also in new types of insurance products directly linked to banking operations and covering risks that were traditionally non-transferable to insurance undertakings. Further comments refer to the functions of insurance recourse in relations with banks. However, the main focus of this study is the confrontation of results of those analyses with the phenomenon of directing recourse claims to the entities carrying the actual and final burden of the insurance cost (borrowers). Moreover, practical solutions adopted by credit institutions which involve the treatment of credit risk insurances as payment protection methods and consequently shift the burden of insurance premium onto the borrower have been assessed.
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8

Sukach, Olena, and Svitlana Kozlovska. "Insurance Market Risk Management." Modern Economics 25, no. 1 (February 23, 2021): 142–47. http://dx.doi.org/10.31521/modecon.v25(2021)-22.

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Abstract. Introduction. The modern insurance market is characterized by a negative trend of reduction of companies-participants of the market. This situation is associated with a number of factors: crisis phenomena in the economy, a decrease in solvent demand, increased risks, growth of unprofitability of the insurance sector, regulatory work of the state. Рurpose. The main purpose of the study is to analyze the domestic insurance market, to identify modern methods and approaches to risk management in the market. The research methodology is based on modern provisions of statistical and economic analysis, empirical research, as well as methods of expert assessments. Results. The article reveals the risks of insurers taking into account the specifics of their manifestation, as well as the specific features of risk management of insurance companies. The problems of managing risks that affect financial stability in insurance companies in modern conditions are examined in the article. A classification of insurance risks and their impact on insurance companies are prepared. It is shown that today a wide range of techniques for estimating the insurance risks exist. The reference points that should be included in the system of risk management of the insurance organization at the present stage are determined. Conclusions. According to the results of the study, a decrease in insurance companies operating in the market, a decrease in premiums and total assets was noted. The expediency of building an optimal risk management system that affects the financial stability of the insurance business has been determined. The application of an integrated approach to risk management of insurance companies has been substantiated. Keywords: risk; risk management; insurance; insurance market; insurer; government regulation; risk classification; risk management strategy.
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9

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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10

Cox, Arthur T., A. Frank Thompson, Mark Greene, James Trieschmann, and Sandra Gustavson. "Risk and Insurance." Journal of Risk and Insurance 60, no. 3 (September 1993): 521. http://dx.doi.org/10.2307/253043.

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Дисертації з теми "Risk (Insurance)"

1

Kang, Yu. "Risk, ambiguity, and insurance /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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2

Matevosyan, Hasmik <1993&gt. "Risk Prediction in Automobile Insurance." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/13433.

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The relationship between individual risks and the number of claims in automobile insurance has received growing attention over recent years among industry practitioners. The number of people who purchase cars grows exponentially. Risk managers of insurance companies need to deal with a variety of risks concerning vehicles and try to predict them as accurate as possible to avoid losses or to minimize them. For this reason, in this thesis I will examine different risk variables and models for these variables in the car insurance sector. The analyses are done based on the econometric approach. The main problem of the thesis concerns the risk prediction by count data models in the automobile insurance. First, the analysis will be done by Poisson regression model using maximum likelihood estimator. Then the Gamma heterogeneity will be taken into account and the negative-binomial regression model will be discussed. The latter provides a framework for the bonus-malus scheme. The extensions of Poisson regression will also be considered in this thesis. The models will be applied to the real data and the estimation results will be discussed at the end of the thesis.
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3

Karabey, Ugur. "Risk capital allocation and risk quantification in insurance companies." Thesis, Heriot-Watt University, 2012. http://hdl.handle.net/10399/2566.

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The objective of this thesis is to investigate risk capital allocation methods in detail for both non-life and life insurance business. In non-life insurance business loss models are generally linear with respect to losses of business-lines. However, in life insurance loss models are not generally a linear function of factor risks, i.e. the interest-rate factor, mortality rate factor, etc. In the first part of the thesis, we present the existing allocation methods and discuss their advantages and disadvantages. In a comprehensive simulation study we examine the allocations sensitivity to different allocation methods, different risk measures and different risk models in a non-life insurance business. We also show the possible usage of the Euclidean distance measure and rank correlation coefficients for the comparison of allocation methods. In the second part, we investigate the factor risk contribution theory and examine its application under a life annuity business. We provide two approximations that enable us to apply risk capital allocation methods directly to annuity values in order to measure factor risk contributions. We examine factor risk contributions for annuities with different terms to maturity and the annuities payable at different times in future. We also analyse the factor risk contributions under the extreme scenarios for the factor risks.
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4

Gong, Qi. "Gerber-Shiu function in threshold insurance risk models." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40987966.

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5

Schreiber, Irene. "Risk-minimization for life insurance liabilities." Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-153192.

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6

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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7

Apere, Pius Oyabramo. "Modelling life insurance new business risk." Thesis, City University London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.435038.

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8

Delaney, J. M. "Education : risk enhancing or insurance mechanism?" Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1558906/.

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In the first chapter, I examine the returns to education for both males and females with a particular focus on the effect of wage risk and periods of non-employment. I also account for selection in to the labour market using a Heckman selection equation and decompose earnings in to permanent and transitory components in an effort to understand the components of wage risk. My results suggest that failure to account for periods of non-employment, wage risk and selection in to the labour market when calculating returns to education leads to biased estimates. In the second chapter, along with my co-author, Paul Devereux, we look at the causal effect of education on earnings uncertainty and volatility and the effect of education on sheltering workers from the adverse effects of recessions. We use the 1973 change in compulsory schooling law to provide exogenous variation in education. Our regression discontinuity estimates suggest that men whose education was increased by the law subsequently had lower earnings volatility, less pro-cyclical earnings, and were less likely to experience real pay cuts. In the third chapter, I analyse the role of risk, family background, cognitive and noncognitive skills in determining college attendance. I use a structural life cycle model explicitly capturing the decision to go to college and incorporating important features which impact the returns to college such as savings, labour supply, human capital accumulation and depreciation, wage risk and employment risk. It is estimated that grants, parental background, non-cognitive skills and risk significantly impact the decision to go to college. However, the biggest factor in determining college attendance is cognitive skills. This is driven both by differences in returns to college conditional on cognitive skills and by the larger psychic costs faced by those with low cognitive skills.
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9

Bash-Taqi, A. Bubakarr. "Risk and insurance in rural Africa." Thesis, University of Sussex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496868.

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It is widely asserted that rural households In developing countries - including Ethiopia - are plagued by a plethora of shocks, which subsequently leads to significant risks to income and consumption. On the other hand, it is also conjectured that in the presence of these shocks and risks, households have access to a variety of formal and Informal mechanisms for dealing with these events as and when they occur. The existence of these mechanisms, It Is argued, explains why consumption does not exactly co-move with income. In other words, there are smaller consumption fluctuations than those that have been observed in income. Indeed, if this is the case, then it is plausible to assert that rural households possess consumption insurance against the relevant shocks.
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10

Siu, Tak-kuen. "Risk measures in finance and insurance." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2323426X.

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Книги з теми "Risk (Insurance)"

1

S, Trieschmann James, ed. Risk & insurance. 7th ed. Cincinnati: South-Western Pub. Co., 1988.

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2

Greene, Mark Richard. Risk & insurance. 8th ed. Cincinnati, Ohio: College Division, South-Western Pub. Co., 1992.

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3

Asmussen, Søren, and Mogens Steffensen. Risk and Insurance. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35176-2.

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4

Library of Congress. Congressional Research Service, ed. All-risk insurance. [Washington, D.C.]: Congressional Research Service, Library of Congress, 1992.

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5

G, Gustavson Sandra, and Hoyt Robert E, eds. Risk management & insurance. Cincinnati, Ohio: South-Western College Pub., 2001.

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6

Travis, Pritchett S., and Schmit Joan T, eds. Risk and insurance. 6th ed. St. Paul: West Pub. Co., 1989.

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7

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

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8

G, Gustavson Sandra, and Greene Mark Richard 1923-, eds. Risk management & insurance. 9th ed. Cincinnati, Ohio: South-Western College Pub., 1995.

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9

G, Gustavson Sandra, ed. Risk management & insurance. Cincinnati, Ohio: South-Western, 1998.

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10

Dickson, G. C. A. Risk and insurance. London: Chartered Insurance Institute, 1999.

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Частини книг з теми "Risk (Insurance)"

1

Jensen, Jesper Lyng, and Susanne Sublett. "Insurance." In Redefining Risk & Return, 71–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41369-3_7.

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2

Weidinger, R. A. Patrick. "Insurance Problems." In Risk Management in Medicine, 129–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47407-5_18.

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3

Denuit, Michel, Donatien Hainaut, and Julien Trufin. "Insurance Risk Classification." In Springer Actuarial, 3–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25820-7_1.

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4

Kurshan, Robert P. "Insurance Mediates Risk." In Investment Industry Claims Debunked, 147–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76709-9_7.

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5

Ahmed, Maram. "Disaster Risk Insurance." In Palgrave Studies in Impact Finance, 175–211. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83209-4_7.

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6

Lee, Hongmu. "Insurance-Linked Security Types." In Risk Management, 169–82. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3468-0_13.

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Lee, Hongmu. "Derivatives and Insurance Derivatives." In Risk Management, 183–95. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3468-0_14.

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Jaeger, Axel-Volkmar, and Götz-Sebastian Hök. "Risk, Insurance and Exceptional Risk." In FIDIC - A Guide for Practitioners, 335–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02100-8_19.

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Roe, Steuart. "Portfolio Insurance." In Risk Management and Financial Derivatives, 482–99. London: Palgrave Macmillan UK, 1997. http://dx.doi.org/10.1007/978-1-349-14605-5_14.

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Zweifel, Peter, Roland Eisen, and David L. Eckles. "Risk: Measurement, Perception, and Management." In Insurance Economics, 27–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80390-2_2.

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Тези доповідей конференцій з теми "Risk (Insurance)"

1

Farkas, Walter, and Alexander Smirnow. "Intrinsic Risk Measures." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0007.

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Cohen, Samuel N. "Data and Uncertainty in Extreme Risks: A Nonlinear Expectations Approach." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0006.

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3

Gabrielli, Nicoletta, and Josef Teichmann. "Pathwise Construction of Affine Processes." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0008.

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4

Clark, Iain J., and Saeed Amen. "Implied Distributions from Risk-Reversals and Brexit/Trump Predictions." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0005.

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Bissiri, M., and R. Cogo. "Behavioral Value Adjustments for Mortgage Valuation." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0001.

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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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Brigo, Damiano, Jan-Frederik Mai, Matthias Scherer, and Henrik Sloot. "Consistent Iterated Simulation of Multivariate Defaults: Markov Indicators, Lack of Memory, Extreme-Value Copulas, and the Marshall–Olkin Distribution." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0003.

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Brigo, Damiano, and Nicola Pede. "Examples of Wrong-Way Risk in CVA Induced by Devaluations on Default." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0004.

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Shakourifar, Mohammad, Ranjan Bhaduri, Ben Djerroud, Fei Meng, David Saunders, and Luis Seco. "Fixed-Income Returns from Hedge Funds with Negative Fee Structures: Valuation and Risk Analysis." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0009.

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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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Звіти організацій з теми "Risk (Insurance)"

1

Brown, Jeffrey, Randall Kroszner, and Brian Jenn. Federal Terrorism Risk Insurance. Cambridge, MA: National Bureau of Economic Research, October 2002. http://dx.doi.org/10.3386/w9271.

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Fauntleroy, J. C., Ryan R. Wagner, and Laura A. Odell. Cyber Insurance - Managing Cyber Risk. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada623798.

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3

Koijen, Ralph, and Motohiro Yogo. The Fragility of Market Risk Insurance. Cambridge, MA: National Bureau of Economic Research, January 2018. http://dx.doi.org/10.3386/w24182.

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4

Mills, Evan. Risk transfer via energy savings insurance. Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/789175.

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5

Sinn, Hans-Werner. Social Insurance, Incentives, and Risk Taking. Cambridge, MA: National Bureau of Economic Research, November 1995. http://dx.doi.org/10.3386/w5335.

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6

Smith, V. Kerry, and Ben Whitmore. Amenities, Risk, and Flood Insurance Reform. Cambridge, MA: National Bureau of Economic Research, February 2019. http://dx.doi.org/10.3386/w25580.

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7

Jaspersen, Johannes, Marc Ragin, and Justin Sydnor. Predicting Insurance Demand from Risk Attitudes. Cambridge, MA: National Bureau of Economic Research, November 2019. http://dx.doi.org/10.3386/w26508.

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8

Layton, Timothy, Thomas McGuire, and Anna Sinaiko. Risk Corridors and Reinsurance in Health Insurance Marketplaces: Insurance for Insurers. Cambridge, MA: National Bureau of Economic Research, September 2014. http://dx.doi.org/10.3386/w20515.

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Barnes, Kayleigh, Arnab Mukherji, Patrick Mullen, and Neeraj Sood. Financial Risk Protection from Social Health Insurance. Cambridge, MA: National Bureau of Economic Research, September 2016. http://dx.doi.org/10.3386/w22620.

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Busch, Christopher, David Domeij, Fatih Guvenen, and Rocio Madera. Asymmetric Business-Cycle Risk and Social Insurance. Cambridge, MA: National Bureau of Economic Research, May 2018. http://dx.doi.org/10.3386/w24569.

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