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Relevant bibliographies by topics / Risk – Mathematical models

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Academic literature on the topic 'Risk – Mathematical models'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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Journal articles on the topic "Risk – 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 (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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Yarygina, I. Z., V. B. Gisin, and B. A. Putko. "Fractal Asset Pricing Models for Financial Risk Management." Finance: Theory and Practice 23, no. 6 (2019): 117–30. http://dx.doi.org/10.26794/2587-5671-2019-23-6-117-130.

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The article presents the analysis findings of the problems and prospects of using the fractal markets theory to mathematically predict the price dynamics of assets as part of a financial risk management strategy. The aim of the article is to find out the features of value of bank assets and to develop recommendations for assessing financial risks based on mathematical methods for forecasting economic processes. Theoretical and empirical research methods were used to achieve the aim. The article reveals the features of mathematical modeling of economic processes related to asset pricing in a vo

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&NA;. "Biologically Based Mathematical Models of Lung Cancer Risk." Epidemiology 4, no. 3 (1993): 193–94. http://dx.doi.org/10.1097/00001648-199305000-00002.

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Park, Colin N. "Mathematical models in quantitative assessment of carcinogenic risk." Regulatory Toxicology and Pharmacology 9, no. 3 (1989): 236–43. http://dx.doi.org/10.1016/0273-2300(89)90062-7.

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Antucheviciene, Jurgita, Gang Kou, Vida Maliene, and Egidijus Rytas Vaidogas. "Mathematical Models for Dealing with Risk in Engineering." Mathematical Problems in Engineering 2016 (2016): 1–3. http://dx.doi.org/10.1155/2016/2832185.

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Ifrim, Ana Maria. "Mathematical Models in Quality Engineering." International Journal of Innovation in the Digital Economy 8, no. 3 (2017): 18–34. http://dx.doi.org/10.4018/ijide.2017070102.

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The present paper deals with the factors that contribute to assuring the quality of the processes involved in project management. The novelty of the approach consists in the fact that the project management processes are analysed with the help of quality indicators in case of time variance. By studying the numeric variable for the proposed economic phenomenon, a smaller discrete interval is obtained, which accounts for the numeric variable being treated as a continuous variable. The practical application of such an analysis is that a risk management plan can be designed based on the parameters

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Bondareva, Irina Olegovna, Sabina M. Sidagalieva, and Evgeniya T. Nesterova. "MATHEMATICAL MODELING OF RISK MANAGEMENT IN TRANSPORT LOGISTICS." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2021, no. 2 (2021): 75–88. http://dx.doi.org/10.24143/2072-9502-2021-2-75-88.

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The article considers the business processes at the transport logistics enterprises as  a chain of clear regulations, where noncompliance or delay of one of them results in disruption of the whole process. Risk management is one of the key tasks requiring the development of modeling tools and prevention of undesirable situations. There has been shown a structural model of the risk of failure to achieve the strategic goal of a cargo port, supplemented by several levels of consideration. The tree of goals of the transport logistics enterprise was built. Failure to achieve a particular goal

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Gosling, John Paul. "Harnessing mathematical models and uncertainty in toxicological risk assessments." Toxicology Letters 229 (September 2014): S160. http://dx.doi.org/10.1016/j.toxlet.2014.06.551.

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Kodell, Ralph L. "The use of mathematical models in carcinogenesis risk assessment." Mathematical and Computer Modelling 11 (1988): 146–51. http://dx.doi.org/10.1016/0895-7177(88)90470-0.

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Voina, O. A., and A. O. Voyna. "Mathematical Models of Risk Control for Regenerating Markov Processes." Cybernetics and Systems Analysis 55, no. 5 (2019): 817–27. http://dx.doi.org/10.1007/s10559-019-00192-x.

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Dissertations / Theses on the topic "Risk – Mathematical models"

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Capelli, Giacomo <1991&gt. "Mathematical Models for Operational Risk Management." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8599.

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The thesis presents the state-of-the-art mathematical statistical models for Operational Risk measurement and management as well as the organisational and managerial processes supporting the creation of risk OR measures. We emphasise the most interesting probabilistic ideas employed in the field and apply the models on real data; these are used in the actuarial modelling paradigm following a Loss Distribution Approach. Extreme Value Theory, convolution transforms and copulæ theory will all be part of OR analysis to arrive to a regulatory risk measure. The thesis accompanies the theoretical mod

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Ngwenza, Dumisani. "Quantifying Model Risk in Option Pricing and Value-at-Risk Models." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31059.

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Financial practitioners use models in order to price, hedge and measure risk. These models are reliant on assumptions and are prone to ”model risk”. Increased innovation in complex financial products has lead to increased risk exposure and has spurred research into understanding model risk and its underlying factors. This dissertation quantifies model risk inherent in Value-at-Risk (VaR) on a variety of portfolios comprised of European options written on the ALSI futures index across various maturities. The European options under consideration will be modelled using the Black-Scholes, Heston a

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Siu, Kin-bong Bonny, and 蕭健邦. "Expected shortfall and value-at-risk under a model with market risk and credit risk." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37727473.

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Gu, Jiawen, and 古嘉雯. "On credit risk modeling and credit derivatives pricing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/202367.

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In this thesis, efforts are devoted to the stochastic modeling, measurement and evaluation of credit risks, the development of mathematical and statistical tools to estimate and predict these risks, and methods for solving the significant computational problems arising in this context. The reduced-form intensity based credit risk models are studied. A new type of reduced-form intensity-based model is introduced, which can incorporate the impacts of both observable trigger events and economic environment on corporate defaults. The key idea of the model is to augment a Cox process with trigge

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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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Liu, Binbin, and 刘彬彬. "Some topics in risk theory and optimal capital allocation problems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48199291.

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In recent years, the Markov Regime-Switching model and the class of Archimedean copulas have been widely applied to a variety of finance-related fields. The Markov Regime-Switching model can reflect the reality that the underlying economy is changing over time. Archimedean copulas are one of the most popular classes of copulas because they have closed form expressions and have great flexibility in modeling different kinds of dependencies. In the thesis, we first consider a discrete-time risk process based on the compound binomial model with regime-switching. Some general recursive formu

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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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Rong, Yian, and 戎軼安. "Applications of comonotonicity in risk-sharing and optimal allocation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/207205.

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Over the past decades, researchers in economics, financial mathematics and actuarial science have introduced results to the concept of comonotonicity in their respective fields of interest. Comonotonicity is a very strong dependence structure and is very often mistaken as a dependence structure that is too extreme and unrealistic. However, the concept of comonotonicity is actually a useful tool for solving several research and practical problems in capital allocation, risk sharing and optimal allocation. The first topic of this thesis is focused on the application of comonotonicity in optim

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Basak, Rishi. "Environmental management systems and the intra-firm risk relationship." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0034/MQ64316.pdf.

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Li, Tang, and 李唐. "Markov chain models for re-manufacturing systems and credit risk management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203700.

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Books on the topic "Risk – Mathematical models"

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

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Marshall, E. I. Mathematical models and political risk. Bradford University Management Centre, 1986.

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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. University of Aarhus, Dept. of Theoretical Statistics, Institute of Mathematical Sciences, 2000.

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Gaspar, Raquel M. Credit risk & forward price models. Stockholm School of Economics, 2006.

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

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Sébastien, Lleo, ed. Risk-sensitive investment management. World Scientific, 2015.

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Bertrand, Munier, and Machina Mark J, eds. Models and experiments in risk and rationality. Kluwer Academic, 1994.

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

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

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Mathematical methods of environmental risk modeling. Kluwer Academic Publishers, 2001.

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Book chapters on the topic "Risk – Mathematical models"

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

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Kusuoka, Shigeo. "A Remark on default risk models." In Advances in Mathematical Economics. Springer Japan, 1999. http://dx.doi.org/10.1007/978-4-431-65895-5_5.

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

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Ammann, Manuel. "Review of Credit Risk Models." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-22330-7_3.

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Tapiero, Charles S., and Pierre Vallois. "Financial Modelling and Memory: Mathematical System." In Future Perspectives in Risk Models and Finance. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07524-2_6.

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Hill, M. D. "Use of Mathematical Models in Risk Assessment and Risk Management." In Radionuclides in the Food Chain. Springer London, 1988. http://dx.doi.org/10.1007/978-1-4471-1610-3_24.

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Kusuoka, Shigeo, and Takenobu Nakashima. "A remark on credit risk models and copula." In Advances in Mathematical Economics. Springer Japan, 2012. http://dx.doi.org/10.1007/978-4-431-54114-1_3.

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Spangler, Manuela. "Credit Risk Models: A Literature Review." In Mathematische Optimierung und Wirtschaftsmathematik | Mathematical Optimization and Economathematics. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-23915-2_3.

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Arai, Takuji. "Local Risk-Minimization for Barndorff-Nielsen and Shephard Models with Volatility Risk Premium." In Advances in Mathematical Economics Volume 20. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0476-6_1.

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Benkert, Christoph. "On the Economic Content of Models of Default Risk." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17039-3_2.

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Conference papers on the topic "Risk – Mathematical models"

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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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Ajmal, Ayesha, Seemal Irfan, Nosheen Sabahat, and Saad Bin Saleem. "Analysis of Flood Risk Management in the Context of Mathematical Models." In 2019 International Conference on Innovative Computing (ICIC). IEEE, 2019. http://dx.doi.org/10.1109/icic48496.2019.8966678.

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Razzaghi, Mehdi. "Teaching Statistical Models for Risk Assessment." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t6c1.

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Risk analysis is used across many disciplines. Biological scientists use risk analysis to determine the chance of an adverse effect occurring. Engineers use it to assess the risk of structural failures. People in business and industry perform risk analysis to estimate profit and loss in decision making. Although there are many definitions of risk, the most accurate definition is based on probabilistic principles. To estimate risk quantitatively, a statistical model is used to describe the process and the associated risk is thereupon calculated. The underlying model and derivation of risk can l

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Kovtun, Dmitriy, Matvey Koptelov, and Anna Guseva. "Megaproject Risk Management Based on Loyalty Program Using Neural Network Models." In 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA). IEEE, 2020. http://dx.doi.org/10.1109/summa50634.2020.9280581.

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Balaji, N., N. B. Sanjana, E. P. Siva, R. Lokesh, and A. J. Catherina. "Risk and effective analysis of COVID-19 vaccine using mathematical models in the pandemic situation." In 2ND INTERNATIONAL CONFERENCE ON MATHEMATICAL TECHNIQUES AND APPLICATIONS: ICMTA2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0108456.

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Zappalà, G., S. Bonamano, A. Madonia, G. Caruso, and M. Marcelli. "Microbiological risk assessment in a coastal marine environment through the use of mathematical models." In WATER POLLUTION 2012. WIT Press, 2012. http://dx.doi.org/10.2495/wp120011.

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Schnabel, Freya, Jennifer Chun, Shira Schwartz, et al. "Abstract A29: Mathematical models are not the be-all and end-all for breast cancer risk assessment." In Abstracts: AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; November 16-19, 2016; Orlando, FL. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7755.carisk16-a29.

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Moskvichev, V. V., U. S. Postnikova, and O. V. Taseiko. "Cluster analysis and individual anthropogenic risk." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.54.88.063.

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Models and assessment methods of anthropogenic risk are analyzed at this article, general basis of mathematical approach for risk analysis is disclosed. Based on multivariate statistic methods, algorithm of analysis for Siberian territories safety is formulated, it allows to define acceptable level of risk for each territorial group (cities with population density more than 70 000, towns with population less than 70 000, and municipals areas).

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Manan, Norhafizah A., and Basir Abidin. "Risk prediction models for major adverse cardiac event (MACE) following percutaneous coronary intervention (PCI): A review." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907524.

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Sakli, Leila, Jean Marc Mercantini, and Jean Claude Hennet. ""Study of supply chain vulnerabilities based on cognitive engineering and ARIMA formal models"." In The 11th International Conference on Integrated Modeling and Analysis in Applied Control and Automation. CAL-TEK srl, 2018. http://dx.doi.org/10.46354/i3m.2018.imaaca.009.

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"This research concerns the formulation of models and methods for supply chains risk analysis. An ontological approach using the KOD method (Knowledge Oriented Design) has been implemented to clearly identify relationships between the concepts of supply chain, risk, vulnerability and disturbances (critical scenarios). As a result, conceptual models of supply chains facing risk situations and critical scenarios are proposed. From the resulting conceptual models and mathematical models proposed in the literature, a multi-stage supply chain model using ARIMA models incorporating the randomness of

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Reports on the topic "Risk – Mathematical models"

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Shabelnyk, Tetiana V., Serhii V. Krivenko, Nataliia Yu Rotanova, Oksana F. Diachenko, Iryna B. Tymofieieva, and Arnold E. Kiv. Integration of chatbots into the system of professional training of Masters. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4439.

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The article presents and describes innovative technologies of training in the professional training of Masters. For high-quality training of students of technical specialties, it becomes necessary to rethink the purpose, results of studying and means of teaching professional disciplines in modern educational conditions. The experience of implementing the chatbot tool in teaching the discipline “Mathematical modeling of socio-economic systems” in the educational and professional program 124 System Analysis is described. The characteristics of the generalized structure of the chatbot information

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Pritchett, Lant, Kirsty Newman, and Jason Silberstein. Focus to Flourish: Five Actions to Accelerate Progress in Learning. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-misc_2022/07.

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There is a severe global learning crisis. While nearly all children start school, far too many do not learn even the most foundational skills of reading, writing, and basic mathematics during the years they spend there. The urgent need to address this crisis requires no elaborate reasoning. If one starts with love for a child, a human universal, it is easy to see that in the modern world a child’s dignity, self-worth, and freedom to define their own destiny require an adequate education. An adequate education is what will then enable that child to lead a full adult life as a parent, community

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