Relevant bibliographies by topics / Risk modelling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Risk modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Risk modelling"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Risk modelling"

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Gilbert, Emmeleen Ulita. "Risk-return portfolio modelling." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/19030.

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Markowitz introduced the concept of modelling the risk associated with a given security as the variance of the expected return and showed how under certain conditions an investors portfolio can be managed by balancing the expected return of the portfolio and its variance. Building on Markowitz original framework, William Sharpe, extended these ideas by connecting a portfolio to a risky asset. This extension became known as the Sharpe Index Model. There are number of assumptions governing the residuals of the Sharpe index model, one being that the error terms of the stocks are uncorrelated. The Troskie-Hossain innovation to the Sharpe Index model relaxes this assumption. We evaluate the Troskie-Hossain model relative to the Sharpe Index Model and Markowitz portfolio, and find that the Troskie-Hossain model approximates the Markowitz efficient frontier and optimal portfolio very closely. Further examining the residuals, we find evidence of autocorrelation and heteroskedasticity. Using ARMA to model the autocorrelation of the residuals has very little impact on the efficient frontier when working with log returns. However when working with simple returns the ARMA shifts the efficient frontier to the left. We find that GARCH(l , 1) models capture most of the autocorrelation in the squared residuals for both simple returns and log returns and shifts the efficient frontier to the left. Modelling a non-constant conditional mean and non-constant conditional variance (ARMA and GARCH) has proven difficult. The more complex a model becomes the more difficult the estimation. We investigate the effects of dividend yields on the efficient frontier, as well as using simple returns vs log returns in portfolio construction. Including dividend yields in our return data shifts the efficient frontier upwards. However only the a's are increased, and the f3's and f3 t-statistics of the shares remain the same. This shift effect of dividends has no impact on the time series or heteroskedastic models. The simple returns efficient frontier lies above that of the log returns efficient frontier. The a 's for simple returns are very different to those of log returns, however the f3's lie in a similar region to those of log returns.
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Shao, Jia. "Modelling catastrophe risk bonds." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2033679/.

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Insurance companies are seeking more adequate liquidity funds to cover the insured property losses related to nature and man-made disasters. Past experience shows that the losses caused by catastrophic events, such as earthquakes, tsunamis, floods or hurricanes, are extremely large. One of the alternative methods of covering these extreme losses is to transfer part of the risk to the financial markets, by issuing catastrophe-linked bonds. This thesis focuses on model and value Catastrophe (CAT) risk bonds. The findings of this thesis is twofold. First, we study the pricing process for CAT bonds with different model setups. Second, based on different framework, we structured three catastrophe based (earthquake, general and nuclear risk) bonds, estimated the parameters of the model by employing real world data and obtained numerical results using Monte Carlo simulation. Comparison between different models is also conducted. The first model employed the structure of n financial and m catastrophe-independent risks, and obtain the valuation framework. This generalized extension allows an easier application in the industry. As an illustration, a structured earthquake is considered with parametric trigger type -- annual maximum magnitude of the earthquake -- and the pricing formulas are derived. The second model presents a contingent claim model with the aggregate claims following compound forms where the claim inter-arrival times are dependent on the claim sizes by employing a two-dimensional semi-Markov process. The final model derives nuclear catastrophe (N-CAT) risk bond prices by extending the previous model. A two-coverage type trigger CAT bond is analysed by adding a perturbed state into the claims system, i.e. the system stops (N-CAT bond contract terminated) immediately after a major catastrophe.
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Aas, Kjersti. "Statistical Modelling of Financial Risk." Doctoral thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1780.

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Esparragoza, Rodriguez Juan Carlos. "Large portfolio credit risk modelling." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486274.

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A model for large portfolio credit risk is developed by using results on the asymptotic behaviour of stochastic networks. We analyse some of the charac- teristics of the model by studying the infinitesimal generator of the portfolio default process using some results of the theory of Piecewise Deterministic processes (PDPs). An efficient pricing technique is proposed using a newly- 1ntroduced quadrature algorithm using a decomposition of the sample space similar to the canonical Poisson space decomposition. Accurate calibration to iTraxx spreads is demonstrated.
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Scarrott, Carl John. "Reactor modelling and risk assessment." Thesis, Lancaster University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414910.

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Anastassopoulou, Nikolitsa. "Credit risk measurement and modelling." Thesis, City University London, 2006. http://openaccess.city.ac.uk/8497/.

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This thesis aims to make a contribution to the understanding of the key economic and company specific components of credit spreads in the investment and non-investment grade US bond market for different maturing bond indices. It calls for the full integration of different market andfirm specific variables into a unique framework, in order to predict credit spread changes. Key determinants of default risk are employed to determine credit migration risk. Particularly, this thesis provides evidence as to the relation between different macroeconomic factors and credit spread changes in all different maturities and rating categories, it supports the use of the consumer confidence index, as the most important variable explaining changes in credit spreads in investment and high yield companies, but most importantly it provides support for the strong informational content of high yield spreads as predictors of output growth, based on Option Adjusted Spreads. It favours the inclusion of implied volatilities in explaining credit spread changes, while it criticises the incorporation of historical ones. Throughout the thesis, it becomes evident that BBB-rated bonds exhibit highly volatile patterns and are very difficult to model. Financial ratios adjusted to reflect depreciation and amortisation expenses, which are usually very high for non-investment grade companies, prove to be very important in explaining changes of high yield spreads. However, firm specific risk, accounts only for a small fraction of the variation in the investment grade category. Ultimately, it is shown that by using solely market (equity and macro variables) and firm specific variables, i. e. some of the key determinants of default risk and the price of credit risky debt in most Merton-type models, we can accurately forecast credit spread changes at least one year ahead, particularly based on results provided from the investment grade sample. Moreover, credit spread forecasts, based on our set of OAS, tend to be overestimated rather than underestimated, as opposed to results provided by previous studies. This makes forecasts more conservative and therefore more appealing for risk management purposes. In particular, this thesis is focused on the main drivers of credit spread movements in the US corporate bond market. There are four issues mainly considered. The first part of the thesis examines a question that is a point of central focus in the fixed income literature, i. e. the relation between credit spread changes and the macroeconomic cycle. This chapter is inspired by the relatively little work that has been done on the empirical relationship between credit spread changes and the macroeconomy, since most of the literature on this issue focuses on macroeconomic variables and the modelling of default risk. We investigate how this relation evolves, not only with respect to short, medium and long term maturities but also for investment and non-investment rated companies, by testing the direction of causation among economic variables and credit spreads and by employing different sets of data and estimation techniques to explore the relation. We find that irrespective of the statistical method used or the time period tested that the most important variable in explaining the variation of credit spread changes is the US Consumer Confidence Index. We affirm the negative relation between the consumer confidence index, money supply and changes in credit spreads but not for the variables of GDP and industrial production. The negative relation between the term structure and credit spreads is also asserted for investment grade bonds of all maturities, consistent with the structural model's theory, while we find this relation to be positive for non-investment grade companies. Results from the OLS regressions suggest that macroeconomic variables alone, can explain at best a 17% of the variation in medium and long term maturing indices, and a 20.5% in short term indices. Findings from cross sectional regressions suggest that macroeconomic factors alone can explain 27.9% of the variation in credit spreads for investment grade bonds and a 44.4% for high yield ones. When testing the direction of causation, wefind thatfor long and medium term maturity investment grade indices we reject the null hypothesis that macroeconomic variables don't granger cause changes in credit spreads, but not for short term maturities and the high yield sector. Indeed, results provided on that respect from the high yield category, provide evidence that non-investment grade spreads may be a good proxy for predicting estimating overall financial conditions. Secondly, the relation between credit spreads and equities together with their implied and historical volatilities is examined. This chapter constitutes an effort to fill the gap in the existing literature, which has focused mainly on bond returns or yield changes, while very limited work has been done in modelling credit spread changes. 12 Empirical evidence points out to the fact that debt markets not only in the US but also in Europe and elsewhere seem to be greatly affected by the movements in the equity markets. If that is the case we should expect changes in equity prices to affect changes in credit spreads. This assumption is tested on a cross sectional and time series basis, for quarterly and monthly frequencies and by using company specific equity prices against the respective credit spreads, but also by including equity and volatility indices. We find that there is a negative relation between credit spread and equity changes, irrespective of maturity or rating category. Results provided by univariate regressions, based on changes in equity prices alone, explain haýr of the variation of B-rated corporate spreads. Results affirm the positive relation between implied volatilities and their high explanatory power on credit spread changes while findings derived from historical volatilities although statistically significant don't even marginally support the hypothesis of explaining the variation in credit spreads. In particular, results from pooled regressions suggest that when implied volatilities are substitutedfor the historical ones, adjusted R2 sfell to 6% and 28%for the investment and non-investment grade samples respectively (from 25% and 50.3% for investment and non-investment grade companies, when implied volatilities are considered). Resultsfrom OLS regressions, suggest that equity variables explain at best a 44% for short term maturing indices, and 35% and 37% for medium and long term maturing indices 2 as reflected by the adjusted R S. We also strongly reject the null hypothesis that implied volatilities don't granger cause changes in credit spreads but only with regards to short and medium term maturities. The next chapter of the thesis focuses on how changes in a company's financials, as those are presented by ratios, actually infiuence changes in credit spreads. The reason for including this chapter is due to the fact that although traditional ratio analysis has been widely investigated, it has mainly been tested within the context of default risk, while very limited literature exists on the use of traditional credit risk analysis in determining credit spread changes. Cross sectional analysis is employed in this chapter to test the hypothesis that credit spread changes are influenced by changes in accounting factors, both in investment and high yield categories. On a multivariate basis, we find that 63.5% of the variation in high yield credit spreads is explained by the changes in financial ratios, as reflected by the adjusted R2, compared to an adjusted R2 of 19.2% for investment grade companies. Consistently, 13 in the randomly selected group of companies, we find that traditional ratios can explain one third of the variation in credit spreads in the high yield sector, although less than 10% in the investment grade sample. A reason for the higher explanatory power in the high yield sector entails the use of ratios adjusted, to reflect depreciation and amortisation expenses, which hasn't been considered before. The most statistically and economically significant coefficient was obtained from the current market capitalisation, which was used as a proxy for the firm's size. The last part of the thesis, constitutes an effort to combine all the above factors (macroeconomic, equity and financials), in order to forecast credit spread changes one and two years ahead. We show that on a multiple regression context, results provided are consistent with previous chapters and indeed highly significant in explaining credit spread variation, irrespective of the time period tested. For the total sample we get an adjusted R2 of 95% or 52% as part of the weighted and unweighted statistics respectively. A robust model is identified for forecasting credit spread changes one year ahead, with the employment of the dynamic solution method. The accuracy of the model doesn't fall below 85% within the first year, while we choose as the most vigorous method for estimating coefficients the GLS method adjustedfor heteroscedasticity, since it consistently provides more conservative forecasts.
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Zheng, Teng. "Model risk in financial modelling." Thesis, University of Kent, 2017. https://kar.kent.ac.uk/66707/.

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Motivated by current post-crisis discussions and the corresponding shift in regulatory requirements, this thesis is dedicated to the study of model risk in financial modelling. It is well-known that the majority of finance quantities that are involved in asset pricing, trading, and risk management activities are dependent on the chosen financial models. This gives rise to model risk in all financial activities. Even when the chosen model form is appropriate, model outputs are still subject to parameter estimation uncertainty. Therefore, among different sources of model risk, we mainly focused on investigating the impact of parameter estimation risk and model selection risk in different financial models. Models investigated in this thesis are key models in option pricing, credit risk management, stochastic process of security returns and hedge fund return forecasting. We provoke a solution, which naturally stems from the Bayesian framework. Regarding parameter estimation risk, instead of focusing on point estimation value, it is possible to gauge the rich information about parameter uncertainty from the posterior distribution of parameters. Subsequent impact to model final outputs can be easily accessed by inserting the posterior distribution of parameters into the model. Depending on the related financial activities, model users may find it useful to adopt the estimated value at a certain percentile (e.g. 97.5%) of the posterior distribution as an overlay to the estimated mean value. While more than one candidate model is considered, posterior or predictive probability of a candidate model derived from the likelihood of the model output in fitting the data is applied for a model averaging exercise to account for model selection risk.
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Kratz, Gutstav. "Risk Modelling in Payment Guarantees." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229418.

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The Swedish Export Credit Agency (EKN) issues payment guarantees to Swedish companies who face the risk of non-payments in export transactions. Commitments are typically correlated, as defaults of companies are driven by other factors than factors specific to that company, such as the economic cycle or the regional conditions. In deciding upon how much capital to be reserved to remain solvent even in an unlikely scenario, this has to be accounted for in order to not underestimate financial risks.By studying models for credit risk and the research available in the area, the popular CreditRisk+ has been chosen as a suitable model for EKN to use in risk assessments. The model together with a few customizations are described in detail and tested on data from EKN.<br>Exportkreditnämnden (EKN) utfärdar betalningsgarantier till svenska exportörer som riskerar inställda betalningar. Fallissemang hos olika motparter är typiskt korrelerade. Vid bedömning av risken i portföljen av garantier måste detta tas i beaktning, för att inte underskatta risken väsentligt. Genom att studera befintliga kreditriskmodeller och tillgänglig forskning inom området har en modell föreslagits som kan användas i EKN:s riskbedömningar. Modellen beskrivs i detalj och testas på data från EKN.
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Kasprowicz, Tomasz. "Threshold Theory--modelling risk attitude /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1650506301&sid=11&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Tran, Cao Son. "Structures in credit risk modelling." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/207989/1/Cao%20Son_Tran_Thesis.pdf.

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The thesis is concerned with the design of conceptual structures in credit risk modelling. The focus is on designing mathematical constructs that serve as a unified framework for reasoning about credit risk modelling. Three contributions are made to this area of research: category theory, providing a powerful tool to study the relations of common structures underlying credit risk modelling; stacking model, to address issues of inconsistent and biased performance measurement; and the Kelly criterion, shifting the focus from dichotomous classification to optimal credit risk allocation.
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Books on the topic "Risk modelling"

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Bolder, David Jamieson. Credit-Risk Modelling. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94688-7.

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de Rocquigny, Etienne. Modelling Under Risk and Uncertainty. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119969495.

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Zioło, Magdalena. Environmental Risk Modelling in Banking. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003310099.

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Kitsos, Christos P., Teresa A. Oliveira, Francesca Pierri, and Marialuisa Restaino, eds. Statistical Modelling and Risk Analysis. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39864-3.

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Foote, Matthew, John Hillier, Kirsten Mitchell-Wallace, and Matthew Jones. Natural catastrophe risk management and modelling. Oxford, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118906057.

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Shevchenko, Pavel V. Modelling Operational Risk Using Bayesian Inference. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15923-7.

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Zhang, Jian-Min, Limin Zhang, and Rui Wang, eds. Dam Breach Modelling and Risk Disposal. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46351-9.

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Marthinsen, O. K. Financial risk modelling of boot projects. Manchester: UMIST, 1993.

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Michael, McAleer, ed. Modelling the riskiness in country risk ratings. Amsterdam: Elsevier, 2005.

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Rees, Michael, ed. Business Risk and Simulation Modelling in Practice. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118904022.

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Book chapters on the topic "Risk modelling"

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Cowell, Frances. "Risk Modelling." In Risk-Based Investment Management in Practice, 65–84. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137346407_5.

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Emberchts, Paul, Claudia Klüppelberg, and Thomas Mikosch. "Risk Theory." In Modelling Extremal Events, 21–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-33483-2_2.

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Bolder, David Jamieson. "Risk Attribution." In Credit-Risk Modelling, 351–427. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94688-7_7.

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Knobloch, Martin. "Thread Risk Modelling." In Web Application Security, 23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16120-9_12.

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Vinnem, Jan-Erik, and Willy Røed. "Fire Risk Modelling." In Springer Series in Reliability Engineering, 249–87. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7444-8_7.

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Vinnem, Jan-Erik, and Willy Røed. "Explosion Risk Modelling." In Springer Series in Reliability Engineering, 289–338. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7444-8_8.

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Vinnem, Jan-Erik, and Willy Røed. "Collision Risk Modelling." In Springer Series in Reliability Engineering, 339–93. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7444-8_9.

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Vinnem, Jan Erik. "Collision Risk Modelling." In Offshore Risk Assessment, 278–315. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-2471-5_10.

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Vinnem, Jan Erik. "Fire Risk Modelling." In Offshore Risk Assessment, 212–40. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-2471-5_8.

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Vinnem, Jan Erik. "Explosion Risk Modelling." In Offshore Risk Assessment, 241–77. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-2471-5_9.

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Conference papers on the topic "Risk modelling"

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Shivadhanush, B. R., N. J. Laycock, M. Geeta, F. Samie, P. Panchami, and J. Hartog. "Corrosion and Risk Modelling Analytics." In CONFERENCE 2024, 1–12. AMPP, 2024. https://doi.org/10.5006/c2024-20833.

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Abstract The CARMA (Corrosion and Risk Modelling Analytics) project aims to provide improved corrosion prediction capability by using Machine Learning techniques. For the selected system, the initial CARMA model is trained by using a combination of online process monitoring data as the input, combined with online corrosion monitoring data as the output. Once trained and validated, the resulting model can then be used to predict corrosion rates in similar systems or in other parts of the same system, without the need to install corrosion monitoring devices or carry out routine physical inspections at the same frequency as in the past. This paper provides an overview of the model development process for one example system, incorporating data from both online monitoring in the plant and from laboratory experiments.
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Tiedemann, K. H. "Landslide Risk Management and Risk Assessment: Modelling Mobility in Rockslides and Rock Falls." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.699-049.

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Deshani, Sarala, and Niranga Amarasingha. "Modelling the Risk of Pedestrians in Walkways." In The SLIIT International Conference on Engineering and Technology 2022. Faculty of Engineering, SLIIT, 2022. http://dx.doi.org/10.54389/gdos6613.

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Pedestrians engaged crashes were increased day by day in the world. There is a high risk of accidents for pedestrians when crossing the road than walking on the road. To minimize the crash rate on pedestrians it is important to know about the risks faced by pedestrians on the road. This study aimed to report pedestrians’ perceptions of risks while walking and crossing the road. A questionnaire survey was carried out to get the data about pedestrians’ perceptions of risks while walking and crossing the road in Matara district. Data were collected with questions with a five-point scale during August and September 2021 from 225 females and 175 males. The collected data were validated by estimating the Cronbach Alpha values and analyzed using chi-square tests and multinomial logistic regression methods. The results of the study were shown the usage of that technical device while walking on the road is the most reported (66.2%) pedestrian risk in the walkways. Whereas crossing the road without using pedestrian crossings is the most reported risk (73%) during the time of crossing the road. The chisquare test results of the survey were indicated that some of the self-reported risks have a significant association with age and gender. Male pedestrians involved with risky behaviours than female pedestrians because male pedestrians have high observed values than the expected values in the reported risks. Age groups, less than 18 years and 18-30 pedestrians were mostly engaged with risky behaviours on the road. Their observed values in the pedestrian risks especially in using technical devices on the road are higher than the observed values compared to other age groups. When the average walking distance of pedestrians per day is increased, accidents happening on pedestrians is also increased. The results of this study would help infrastructure designers to make safer roads. KEYWORDS: crossing, pedestrians, self-reported risks, walking, age, gender
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Popov, V., R. Gospavic, J. Kreyenschmidt, and S. Bruckner. "Microbial growth modelling under variable temperature conditions." In ENVIRONMENTAL HEALTH RISK 2009. Southampton, UK: WIT Press, 2009. http://dx.doi.org/10.2495/ehr090311.

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Baker, C. "Dependency modelling: risk mapping." In IEE Symposium on Systems Engineering in Business. IEE, 2000. http://dx.doi.org/10.1049/ic:20000380.

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De Souza, E. "Designing safe and stable tailings dam structures with centrifuge modelling." In RISK ANALYSIS 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/risk100511.

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Žutautaitė-Šeputienė, I., J. Augutis, and E. Ušpuras. "Modelling of the node immunity change process in a network system." In RISK ANALYSIS 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/risk080351.

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GAUR, AYUSHI, ABHISHEK GAUR, and SLOBODAN P. SIMONOVIC. "MODELLING OF FUTURE FLOOD RISK ACROSS CANADA DUE TO CLIMATE CHANGE." In RISK ANALYSIS 2018. Southampton UK: WIT Press, 2018. http://dx.doi.org/10.2495/risk180131.

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"Combining risk forecasts." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.soltyk.

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Marchetti, D., G. D’Amato Avanzi, N. Sciarra, and M. Calista. "Slope stability modelling of a sandstone cliff south of Livorno (Tuscany, Italy)." In RISK ANALYSIS 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/risk080321.

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Reports on the topic "Risk modelling"

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Chang, S. E., R. P. Reynolds, and J. Kim. Risk dynamics modelling in metro Vancouver. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330542.

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Arts, G. H. P., J. D. M. Belgers, and S. M. Hennekens. Scientific data analysis to support modelling for aquatic risk assessment : Aquatic macrophyte data for risk assessment. Wageningen: Wageningen Environmental Research, 2024. http://dx.doi.org/10.18174/652968.

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Juhola, Sirkku, Jukka Käyhkö, Markku Ollikainen, Jouni Jaakkola, Janina Käyhkö, Fanny Groundstroem, Matti Hyyrynen, Juuso Suomi, Timo Hugg, and Meaza Degefa Markkanen. Developing Finland’s adaptation policy using risk modelling and effectiveness monitoring of adaptation measures. Suomen ilmastopaneeli, 2023. https://doi.org/10.31885/9789527457290.

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Weldon, James. Modelling the risks of Nymphoides peltata spread in Swedish lakes. Department of Aquatic Resources, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.nmtj4tgmer.

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Species distribution modelling is a valuable tool for identifying areas most at risk of the spread of potentially harmful species. Here we model the environmental factors governing the distribution of a harmful species of concern that is currently found in Sweden at only a limited number of locations: the aquatic macrophyte N. peltata (sjögull). The most important factors determining risk of establishment are water chemistry (sufficient amounts of calcium is required), temperature (the species is partly limited by colder temperatures in Sweden) and connectivity to other water bodies. The results how that there are potentially a large number of lakes in mid to southern Sweden where N. peltata could establish populations, with undesirable consequences for biodiversity and recreation.
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Weldon, James, and Carlotta Meriggi. Modelling the risks of invasive aquatic species spread in Swedish lakes. Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.r68r25qcb1.

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Species distribution modelling is a valuable tool for identifying areas most at risk of the spread of invasive species. Here we model the environmental factors governing the distributions of two invasive species of concern that are currently found in Sweden at only a limited number of locations: the aquatic macrophyte Elodea nuttallii (Nuttall’s waterweed / smal vattenpest) and the bivalve Dreissena polymorpha (Zebra mussel / vandrarmussla). For E.nuttallii, the greatest risk factors are connectivity with other water bodies (facilitating dispersion), human population density and length of growing season. This implies that it is principally well-connected lakes in populated areas of southern Sweden that are most at risk of further spread (although other areas of concern are identified). For D.polymorpha, water alkalinity and the proportion of agricultural land (a source of nutrient pollution) are the most important factors, and the models identify lakes Vänern and Vättern, waters in parts of Östergötland, Jämtland and Gotland as key areas of concern for further spread.
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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).
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Hobbs, T. E., J. M. Journeay, A. S. Rao, L. Martins, P. LeSueur, M. Kolaj, M. Simionato, et al. Scientific basis of Canada's first public national seismic risk model. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330927.

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Natural Resources Canada, in partnership with the Global Earthquake Model Foundation, has prepared a public Canadian Seismic Risk Model to support disaster risk reduction efforts across industry and all levels of government, and to aid in Canada's adoption of the Sendai Framework for Disaster Risk Reduction. Developing this model has involved the creation of a national exposure inventory, Canadian specific fragility and vulnerability curves, and adjustment of the Canadian Seismic Hazard Model which forms the basis for the seismic provisions of the National Building Code of Canada. Using the Global Earthquake Model Foundation's OpenQuake Engine (OQ), risk modelling is completed using both deterministic and probabilistic risk calculations, under baseline and simulated retrofit conditions. Output results are available in all settled regions of Canada, at the scale of a neighbourhood or smaller. We report on expected shaking damage to buildings, financial losses, fatalities, and other impacts such as housing disruption and the generation of debris. This paper documents the technical details of the modelling approach including a description of novel datasets in use, as well as preliminary results for a magnitude 9.0 earthquake on the Cascadia megathrust and nation-wide 500 year expected probabilistic losses. These kinds of results, such as earthquake scenario impacts, loss exceedance curves, and annual average losses, provide a quantitative base of evidence for decision making at local, regional, and national levels.
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Gamboa-Estrada, Fredy, and Jose Vicente Romero. Modelling CDS Volatility at Different Tenures: An Application for Latin-American Countries. Banco de la República de Colombia, May 2022. http://dx.doi.org/10.32468/be.1199.

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Assessing the dynamics of risk premium measures and its relationship with macroeconomic fundamentals is important for both macroeconomic policymakers and market practitioners. This paper analyzes the main determinants of CDS in Latin-America at different tenures, focusing on their volatility. Using a component GARCH model, we decompose volatility between permanent and transitory components. We find that the permanent component of CDS volatility in all tenors was higher and more persistent in the global financial crisis than during the recent COVID-19 shock. JEL Classification: C22, C58, G01, G15. Keywords: Credit default swaps (CDS), CDS in Latin-American countries, sovereign risk, volatility, crisis, component GARCH models
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Faverjon, Céline, Angus Cameron, and Marco De Nardi. Modelling framework to quantify the risk of AMR exposure via food products - example of chicken and lettuce. Food Standards Agency, April 2022. http://dx.doi.org/10.46756/sci.fsa.qum110.

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Antimicrobial resistance (AMR) is a complex issue where microorganisms survive antimicrobial treatments, making such infections more difficult to treat. It is a global threat to public health. To increase the evidence base for AMR in the food chain, the FSA has funded several projects to collect data to monitor the trends, prevalence, emergence, spread and decline of AMR bacteria in a range of retail foods in the UK. However, this data and information from the wider literature was yet to be used to create tools to aid in the production of quantitative risk assessment to determine the risk to consumers of AMR in the food chain. To assist with this, there was a need to develop a set of modular templates of risk of AMR within foods. This sought to allow the efficient creation of reproducible risk assessments of AMR to maintain the FSA at the forefront of food safety.
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Nechaev, V., Володимир Миколайович Соловйов, and A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.

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One of the well-known results of the theory of management is the fact, that multi-stage hierarchical organization of management is unstable. Hence, the ideas expressed in a number of works by Don Tapscott on advantages of network organization of businesses over vertically integrated ones is clear. While studying the basic tendencies of business organization in the conditions of globalization, computerization and internetization of the society and the results of the financial activities of the well-known companies, the authors arrive at the conclusion, that such companies, as IBM, Boeing, Mercedes-Benz and some others companies have not been engaged in their traditional business for a long time. Their partner networks performs this function instead of them. The companies themselves perform the function of system integrators. The Tapscott’s idea finds its confirmation within the framework of a new powerful direction of the development of the modern interdisciplinary science – the theory of the complex networks (CN) [2]. CN-s are multifractal objects, the loss of multifractality being the indicator of the system transition from more complex state into more simple state. We tested the multifractal properties of the data using the wavelet transform modulus maxima approach in order to analyze scaling properties of our company. Comparative analysis of the singularity spectrumf(®), namely, the difference between maximum and minimum values of ® (∆ = ®max ¡ ®min) shows that IBM company is considerably more fractal in comparison with Apple Computer. Really, for it the value of ∆ is equal to 0.3, while for the vertically integrated company Apple it only makes 0.06 – 5 times less. The comparison of other companies shows that this dependence is of general character. Taking into consideration the fact that network organization of business has become dominant in the last 5-10 years, we carried out research for the selected companies in the earliest possible period of time which was determined by the availability of data in the Internet, or by historically later beginning of stock trade of computer companies. A singularity spectrum of the first group of companies turned out to be considerably narrower, or shifted toward the smaller values of ® in the pre-network period. The latter means that dynamic series were antipersistant. That is, these companies‘ management was rigidly controlled while the impact of market mechanisms was minimized. In the second group of companies if even the situation did changed it did not change for the better. In addition, we discuss applications to the construction of portfolios of stock that have a stable ratio of risk to return.
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