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Journal articles on the topic 'Risk modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Agyemang, Boakye, Bashiru I I Saeed, Albert Luguterah, and Samuel Baffoe. "Modelling Adversarial Risk in Big Data." International Journal of Science and Research (IJSR) 10, no. 11 (2021): 585–89. https://doi.org/10.21275/sr211030025426.

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2

Niklis, Dimitrios, Michalis Doumpos, and Constantin Zopounidis. "Credit Risk Modelling." International Journal of Sustainable Economies Management 7, no. 3 (2018): 50–64. http://dx.doi.org/10.4018/ijsem.2018070105.

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The assessment of businesses' credit risk is a difficult and important process in the area of financial risk management. In a classical multivariate model, financial ratios are combined in order to achieve a credit risk score, which signals if a loan application is approved or discarded. Despite their good performance, the developed multivariate models using statistical methods have been widely criticized. They are based on models that use accounting data, which have the disadvantage of being static and so often fail to follow the changes in the economic and business environment. In recent yea

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3

Dimakos, Xeni K., and Kjersti Aas. "Integrated risk modelling." Statistical Modelling: An International Journal 4, no. 4 (2004): 265–77. http://dx.doi.org/10.1191/1471082x04st079oa.

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4

Skerratt, L. C. L., and A. Woodhead. "Modelling audit risk." British Accounting Review 24, no. 2 (1992): 119–37. http://dx.doi.org/10.1016/s0890-8389(05)80003-4.

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5

Hoti, Suhejla, Michael McAleer, and Laurent L. Pauwels. "Modelling environmental risk." Environmental Modelling & Software 20, no. 10 (2005): 1289–98. http://dx.doi.org/10.1016/j.envsoft.2004.08.010.

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6

Parbat, Tejas. "Credit Risk Modelling." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 595–98. http://dx.doi.org/10.22214/ijraset.2024.58397.

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Abstract: Modern society recognizes the significance of Identity and Access Management. It's the method through which information is controlled in terms of who gets access to what and when. Creation of user and system identities is an IAM activity. Data and information sharing relies heavily on safe user access. In addition, most businesses are realizing the growing value of electronic data. Strong authentication is a common solution to this problem and is becoming more and more necessary as the standards for access protection rise. The two most critical IAM concepts that must be handled by th

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7

Vishwakarma, Saketh Kumar. "AI-Driven Predictive Risk Modelling for Aerospace Supply Chains." International Interdisciplinary Business Economics Advancement Journal 6, no. 5 (2025): 102–34. https://doi.org/10.55640/business/volume06issue05-06.

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In the aerospace supply chain, a complex, high-stakes ecosystem is at risk of multiple risk categories such as component shortage, cyber threats, and noncompliance with regulations. Traditional risk mitigation strategies are not enough. They are now offered as measures reactive to risks and static contingency plans. This paper investigates how AI-driven predictive risk modeling can break these limitations of the current risk management practices and allow risk management to change from reactionary to proactive across the aerospace supply chain. These models leverage the power of machine learni

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8

Kountzakis, Christos E., and Maria P. Koutsouraki. "On Quantum Risk Modelling." Journal of Mathematical Finance 06, no. 01 (2016): 43–47. http://dx.doi.org/10.4236/jmf.2016.61005.

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9

Klein, Jonathan H. "Modelling Risk Trade-Off." Journal of the Operational Research Society 44, no. 5 (1993): 445. http://dx.doi.org/10.2307/2583911.

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10

Klein, Jonathan H. "Modelling Risk Trade-Off." Journal of the Operational Research Society 44, no. 5 (1993): 445–60. http://dx.doi.org/10.1038/sj/jors/0440503.

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11

Susan, Mwelu. "Modelling Oil Price Risk." American Journal of Theoretical and Applied Statistics 4, no. 6 (2015): 539. http://dx.doi.org/10.11648/j.ajtas.20150406.25.

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12

Allen, David E. "Measuring and modelling risk." Global Business and Economics Review 11, no. 3/4 (2009): 199. http://dx.doi.org/10.1504/gber.2009.031169.

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13

Klein, Jonathan H. "Modelling Risk Trade-Off." Journal of the Operational Research Society 44, no. 5 (1993): 445–60. http://dx.doi.org/10.1057/jors.1993.81.

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14

Dancer, Diane M., and Denzil G. Fiebig. "Modelling Students at Risk." Australian Economic Papers 43, no. 2 (2004): 158–73. http://dx.doi.org/10.1111/j.1467-8454.2004.00222.x.

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15

Hand, D. J. "Modelling consumer credit risk." IMA Journal of Management Mathematics 12, no. 2 (2001): 139–55. http://dx.doi.org/10.1093/imaman/12.2.139.

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16

Sweeting, P. J. "Modelling and Managing Risk." British Actuarial Journal 13, no. 3 (2007): 579–621. http://dx.doi.org/10.1017/s1357321700001562.

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ABSTRACTThis paper looks at the risks faced by financial institutions, and how they can be modelled and managed. I compare the way in which each of the risks affects different types of financial institution and look for similarities (and differences) across industries. Finally, I consider what makes a good risk management system.

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17

Sweeting, P. J. "Modelling and Managing Risk." British Actuarial Journal 14, no. 1 (2008): 111–25. http://dx.doi.org/10.1017/s1357321700001641.

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18

Oko-Odion, Courage. "An Integration of Time Series Analysis into Quantitative Risk Modelling Frameworks for Enhanced Risk Management." International Journal of Research Publication and Reviews 6, no. 6 (2025): 5085–99. https://doi.org/10.55248/gengpi.6.0125.0649.

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19

Aldibekova, N. B., A. V. Tyan, I. G. Omarov, A. Mohamed та L. M. Alimzhanova. "Использование математического моделирования и программного обеспечения в управлении проектными рисками". INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 2, № 1(5) (2021): 119–28. http://dx.doi.org/10.54309/ijict.2021.05.1.016.

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The study describes the existing methods for quantitative and qualitative risk analysis and risk assessment software. The field of mathematical modelling is very much developing in economics, allow-ing for more in-depth research. Risk management also requires accurate justification of decisions about the importance of risk, which is made possible by accurate quantitative calculations, including mathematical modelling. The decision-making process within the project is based on the results of visual analysis, i.e., the study of the risk profile and the cumulative risk profile derived from the si

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20

Nadraga, Vasiliy, and Anatoliy Balanda. "Macro-level Modelling of Budget Support for Social Risk Management." Central Ukrainian Scientific Bulletin. Economic Sciences, no. 2(35) (2019): 144–51. http://dx.doi.org/10.32515/2663-1636.2019.2(35).144-151.

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21

Allen, David Edmund, and Elisa Luciano. "Risk Analysis and Portfolio Modelling." Journal of Risk and Financial Management 12, no. 4 (2019): 154. http://dx.doi.org/10.3390/jrfm12040154.

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Financial risk measurement is a challenging task because both the types of risk and their measurement techniques evolve quickly. This book collects a number of novel contributions for the measurement of financial risk, which addresses partially explored risks or risk takers in a wide variety of empirical contexts.

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22

He, Y., Mahmut Horasan, P. Taylor, and G. Ramsay. "Stochastic Modelling For Risk Assessment." Fire Safety Science 7 (2003): 333–44. http://dx.doi.org/10.3801/iafss.fss.7-333.

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23

Singh, Manmohan, and M. D. Jaybhaye. "Risk Quantification and Evaluation Modelling." Defence Science Journal 64, no. 4 (2014): 378–84. http://dx.doi.org/10.14429/dsj.64.6366.

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24

Visagie, S. E., and A. H. Ghebretsadik. "MODELLING RISK IN FARM PLANNING." Agrekon 44, no. 4 (2005): 561–85. http://dx.doi.org/10.1080/03031853.2005.9523728.

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25

Kasprowicz, Tomasz, and Andrzej Bednorz. "Threshold Theory – modelling risk attitude." e-Finanse 13, no. 4 (2017): 97–109. http://dx.doi.org/10.1515/fiqf-2016-0039.

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AbstractIn this paper we offer an alternative framework for examining why risk matters in the decisions of economic agents, and how the agent’s risk attitude affects his decisions. This “Threshold Theory” framework is based on a real options approach and the observation that in many situations an agent faces one or more thresholds in the payoff function. These thresholds influence the agent’s risk attitude. The theory’s predictions help to explain many anomalies that the standard expected utility model cannot. Threshold Theory can also model behavior in contexts such as individual investor dec

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26

Portier, Christopher J. "Mechanistic Modelling and Risk Assessment." Pharmacology & Toxicology 72 (February 1993): 28–32. http://dx.doi.org/10.1111/j.1600-0773.1993.tb01665.x.

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27

Koks, Elco. "Moving flood risk modelling forwards." Nature Climate Change 8, no. 7 (2018): 561–62. http://dx.doi.org/10.1038/s41558-018-0185-y.

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28

Alegakis, Athanasios, Vasilis Androutsopoulos, Spyros Karakitsios, and Dimosthenis Sarigiannis. "Modelling risk for chemical mixtures." Toxicology Letters 238, no. 2 (2015): S19. http://dx.doi.org/10.1016/j.toxlet.2015.08.185.

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29

Spuchľakova, Erika, and Juraj Cug. "Credit Risk and LGD Modelling." Procedia Economics and Finance 23 (2015): 439–44. http://dx.doi.org/10.1016/s2212-5671(15)00379-2.

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30

Cuny, X., and M. Lejeune. "Statistical modelling and risk assessment." Safety Science 41, no. 1 (2003): 29–51. http://dx.doi.org/10.1016/s0925-7535(01)00056-x.

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31

Heino, P., and R. Kakko. "Risk assessment modelling and visualisation." Safety Science 30, no. 1-2 (1998): 71–77. http://dx.doi.org/10.1016/s0925-7535(98)00037-x.

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32

D'Ecclesia, R. L. "Financial modelling and risk management." European Journal of Operational Research 163, no. 1 (2005): 1–4. http://dx.doi.org/10.1016/j.ejor.2003.12.003.

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33

Kuhan, G., E. Gardiner, A. Abidia, et al. "Risk modelling for carotid endarterectomy." British Journal of Surgery 88, no. 4 (2001): 622. http://dx.doi.org/10.1046/j.1365-2168.2001.01757-62.x.

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34

Crook, Jonathan, and David Edelman. "Special issue credit risk modelling." Journal of the Operational Research Society 65, no. 3 (2014): 321–22. http://dx.doi.org/10.1057/jors.2014.6.

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35

Nugrahani, E. H. "Risk Modelling of Agricultural Products." IOP Conference Series: Earth and Environmental Science 58 (March 2017): 012055. http://dx.doi.org/10.1088/1755-1315/58/1/012055.

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36

Copas, John. "Statistical Modelling for Risk Assessment." Risk Management 1, no. 1 (1999): 35–49. http://dx.doi.org/10.1057/palgrave.rm.8240013.

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37

Mitra, Gautam. "Introduction: Optimization and Risk Modelling." Computational Optimization and Applications 32, no. 1-2 (2005): 5–8. http://dx.doi.org/10.1007/s10589-005-2051-x.

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38

Van Veelen, Thomas J., Harshinie Karunarathna, Tom P. Fairchild, William G. Bennett, John Griffin, and Dominic E. Reeve. "IMPROVING PREDICTIVE MODELLING OF COASTAL PROTECTION BY SALT MARSHES." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 95. http://dx.doi.org/10.9753/icce.v36.risk.95.

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Salt marshes are vegetated tidal wetlands, which can typically be found at sheltered coastal areas in moderate climate zones. Their potential as natural coastal protection by wave attenuation (MÃ¶ller et al, 2014), reduction of flood-surge propagation (Stark et al., 2016) and shoreline stabilization (Bouma et al, 2014) has been increasingly recognized among scientists and engineers, but it comes with risks. Our understanding of the biogeomorphological dynamics between salt marsh vegetation, hydrodynamics and sediment is limited, while these are essential to identify the protective value of mar

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39

Woo, G., C. J. Martin, C. Hornsby, and A. W. Coburn. "Prospective Longevity Risk Analysis." British Actuarial Journal 15, S1 (2009): 235–47. http://dx.doi.org/10.1017/s1357321700005584.

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ABSTRACTMortality improvement has traditionally been analysed using an array of statistical methods, and extrapolated to make actuarial projections. This paper presents a forward-looking approach to longevity risk analysis which is based on stochastic modelling of the underlying causes of mortality improvement, due to changes in lifestyle, health environment, and advances in medical science. The rationale for this approach is similar to that adopted for modelling other types of dynamic insurance risk, e.g. natural catastrophes, where risk analysts construct a stochastic ensemble of events that

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40

Pesenti, Silvana M. "Reverse Sensitivity Analysis for Risk Modelling." Risks 10, no. 7 (2022): 141. http://dx.doi.org/10.3390/risks10070141.

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We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as the output) changes under a stress on the output’s distribution. Specifically, for a stress on the output random variable, we derive the unique stressed distribution of the output that is closest in the Wasserstein distance to the baseline output’s distribution and satisfies the stress. We further derive

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41

Juhász, Péter. "Risk analysis in corporate financial modelling." Economy & finance 7, no. 1 (2020): 47–55. http://dx.doi.org/10.33908/ef.2020.1.2.

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42

Tammemäe, Olavi, and Hardi Torn. "Risk Management in environmental geotechnical modelling." Geologija 50, no. 1 (2008): 44–48. http://dx.doi.org/10.2478/v10056-008-0006-5.

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43

Iosub, Marina, and Andrei Enea. "Flood Early Warning and Risk Modelling." Hydrology 9, no. 4 (2022): 57. http://dx.doi.org/10.3390/hydrology9040057.

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The evolution of mankind during the last 2 centuries has generated an ever growing thrive for increased production, for the need to create novel means to generate energy and for society to change into a more consumerism-oriented version [...]

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44

Ahelegbey, Daniel Felix. "Statistical Modelling of Downside Risk Spillovers." FinTech 1, no. 2 (2022): 125–34. http://dx.doi.org/10.3390/fintech1020009.

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We study the sensitivity of stock returns to the tail risk of major equity market indices, including the G10 countries. We model the sensitivity relationship via extreme downside hedging and estimate the parameters via a Bayesian graph structural learning method. The empirical application examines whether downside risk connections among the major stock markets are merely anecdotal or provide a signal of contagion and the nature of sensitivity among major equity markets during the global financial crisis and the coronavirus pandemic. The result showed that the COVID-19 crisis recorded the histo

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45

Agrawal, Khushbu, and Yogesh Maheshwari. "Default risk modelling using macroeconomic variables." Journal of Indian Business Research 6, no. 4 (2014): 270–85. http://dx.doi.org/10.1108/jibr-04-2014-0024.

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Purpose – This paper aims to find out significant macroeconomic variables, incorporated as sensitivity variables (macroeconomic sensitivities), affecting financial distress for a sample of listed Indian firms. Design/methodology/approach – The study uses a matched pair sample of defaulting and non-defaulting listed Indian firms. It uses two alternative statistical techniques, viz., logistic regression and multiple discriminant analysis. The macroeconomic sensitivities are estimated by regressing the monthly stock return of the individual firm on the monthly changes in each macroeconomic variab

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46

Aqlan, Faisal, and Sarah S. Lam. "Supply chain risk modelling and mitigation." International Journal of Production Research 53, no. 18 (2015): 5640–56. http://dx.doi.org/10.1080/00207543.2015.1047975.

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47

Ndiaye, Papa Momar, François Oustry, and Véronique Piolle. "Semidefinite optimisation for global risk modelling." Journal of Asset Management 7, no. 2 (2006): 142–53. http://dx.doi.org/10.1057/palgrave.jam.2240209.

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48

Leighton, Christopher. "Risk Modelling for a New Railway." Safety and Reliability 15, no. 1 (1995): 10–14. http://dx.doi.org/10.1080/09617353.1995.11690643.

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49

Shiraishi, Hiroshi, and Zudi Lu. "Review of statistical actuarial risk modelling." Cogent Mathematics 3, no. 1 (2016): 1123945. http://dx.doi.org/10.1080/23311835.2015.1123945.

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50

Chang, Chia-Lin, David E. Allen, Michael McAleer, and Teodosio Perez Amaral. "Risk modelling and management: An overview." Mathematics and Computers in Simulation 94 (August 2013): 159–63. http://dx.doi.org/10.1016/j.matcom.2013.08.001.

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