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Journal articles on the topic 'Risk modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 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 (November 27, 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 (July 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 years, market models (structural and reduced form models) have become popular among banks and financial institutions, because of their theoretical background and the use of updated information. The aim of this article is to present an overview of basic market models (structural models, reduced form models and market models used from credit institutions) together with their characteristics in order to outline their development throughout the last decades.

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3

Dimakos, Xeni K., and Kjersti Aas. "Integrated risk modelling." Statistical Modelling: An International Journal 4, no. 4 (December 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 (June 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 (October 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 (February 29, 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 the business are identity and access. More and more businesses are turning to an automated system to handle these tasks. But it opens up a new danger. Since these technologies lack the wit to make judgements on their own, we must supplement them with our own brainpower employing a variety of data mining algorithms. This allows us to save data for later model building. Everything you need to know about the difficulties of Identity and Access Management may be found in this document. A potential answer is provided for these problems.

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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 (May 22, 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 learning by poring over structured and unstructured data (telemetry data, supplier log files) and searching for patterns that predict future disruptions. Core technologies that can ingest and process data in real-time, like Apache Kafka and Apache Spark, support dynamic risk calculation. Combining with the domain expertise, they provide precision to the model and compliance framework (FAA, ITAR, AS9100) for legal compliance. The document also mentions some architectural shifts from monolith to microservice systems and the use of design patterns such as CQRS, the Strangler pattern, and ModelOps in the model deployment. Quantifiable benefits, as shown in a case study in a major aerospace OEM, include reduced downtime, decreased procurement times, and better prediction. Results suggest that stakeholders must be involved, ethical AI governance should be implemented, and iterative validation should be used to build trust and alignment in the system. Edge AI, blockchain, and quantum computing are moving in the right direction in the industry and predictive analytics. The guide is a strategic tool for converting their operation to systems with resilient and intelligent supply chains that the aerospace industry’s professionals aspire to embrace.

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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 (May 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 (May 1, 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 (May 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 (June 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 (October 1, 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 (September 1, 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 (March 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 (January 2025): 5085–99. https://doi.org/10.55248/gengpi.6.0125.0649.

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19

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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20

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) (26 березня 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 simulation. This research shows that quantitative risk methods in the project manager's toolkit are helpful in ob-taining complex calculations, but risk management cannot currently exclude the role of a project risk manager from the risk assessment pro-cess.

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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 (September 21, 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 (July 21, 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 (December 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 (December 1, 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 decisions, corporate governance and other agency problems. Further, we examine CEO decisions as a function of time to the CEO’s retirement to test predictions of the Theory.

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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 (May 28, 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 (October 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 (February 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 (October 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 (May 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, I. Chetter, P. Renwick, B. F. Johnson, A. R. Wilkinson, and P. T. McCollum. "Risk modelling for carotid endarterectomy." British Journal of Surgery 88, no. 4 (April 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 (March 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 (January 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 (October 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 marshes to coastal protection (Wu et al., 2017). In this study, we present a predictive process-based model with a newly validated vegetation module to study the potential of salt marshes to contribute to coastal protection for a case study site in West Wales, United Kingdom.

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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 might happen in the future, rather than rely on a retrospective analysis of the non-stationary and comparatively brief historical record.Another feature of prospective longevity risk analysis, which is shared with catastrophe risk modelling, is the objective of capturing vulnerability data at a high resolution, to maximise the benefit of detailed modelling capability down to individual risk factor level. Already, the use by insurers of postcode data for U.K. flood risk assessment has carried over to U.K. mortality assessment. Powered by fast numerical computation and parameterised with high quality geographical data, hydrological models of flood risk have superseded the traditional statistical insurance loss models. A decade later, medically-motivated computational models of mortality risk can be expected to gain increasing prominence in longevity risk management.

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40

Pesenti, Silvana M. "Reverse Sensitivity Analysis for Risk Modelling." Risks 10, no. 7 (July 18, 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 the stressed model, including the stressed distribution of the inputs, which can be calculated in a numerically efficient way from a set of baseline Monte Carlo samples and which is implemented in the R package SWIM on CRAN. The proposed reverse sensitivity analysis framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse sensitivity analysis framework suitable for risk models.

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41

Juhász, Péter. "Risk analysis in corporate financial modelling." Economy & finance 7, no. 1 (March 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 (January 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 (March 31, 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 (April 1, 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 historically highest spike in the downside risk interconnectedness among the major equity market indices, suggesting higher financial market vulnerability in the coronavirus pandemic than during the global financial crisis.

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45

Agrawal, Khushbu, and Yogesh Maheshwari. "Default risk modelling using macroeconomic variables." Journal of Indian Business Research 6, no. 4 (November 11, 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 variable. Findings – Sensitivity to changes in the stock market (stock market sensitivity) and sensitivity to changes in inflation [Consumer Price Index (CPI) sensitivity] have a significant impact on the default probability of a firm. Stock market sensitivity has a significant positive relationship with the probability of default, and CPI sensitivity has a significant negative relationship with the probability of default. Originality/value – The study links the developments in the external environment to the firm’s susceptibility to default. Furthermore, it highlights the significance of sensitivity of a firm to uncertainties in the macroeconomic environment and its impact on default risk. This establishes the fact that each firm is uniquely affected by the changes in the overall macroeconomic environment. The findings could be valuable to lenders such as banks and financial institutions, investors and policymakers.

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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 (May 26, 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 (July 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 (March 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 (January 8, 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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