Operational Risk: Emerging Markets, Sectors and Measurement

The role of decision support systems in mitigating operational risks in firms is well established. However, there is a lack of investment in decision support systems in emerging markets, even though inadequate operational risk management is a key cause of discouraging external investment. This has also been exacerbated by insufficient understanding of operational risk in emerging markets, which can be attributed to past operational risk measurement techniques, limited studies on emerging markets and inadequate data. In this paper, using current operational risk techniques, the operational risk of developed and emerging market firms is measured for 100 different companies, for 4 different industry sectors and 5 different countries. Firstly, it is found that operational risk is consistently higher in emerging market firms than in the developed markets. Secondly, it is found that operational risk is not only dependent upon the industry sector but also that market development is the more dominant factor. Thirdly, it is found that the market development and the sector influence the shape of the operational risk distribution, in particular tail and skewness risk. Furthermore, an operational risk measurement method is provided that is applicable to emerging markets. Our results are consistent with under investment in decision support systems in emerging markets and imply operational risk management can be improved by increased investment

Climate Adaptation decision support Tool for Local Governments: CATLoG

AbstractThe Intergovernmental Panel on Climate Change (IPCC), the globally-recognised reference body for climate-related research, describes warming of the climate system as ‘unequivocal’. The changing climate is likely to result in the occurrence of more frequent and intense extreme weather events. This demands preventative and preparatory actions (mitigation and adaptation) from all levels of government including local governments. No matter how robust the mitigation responses will be, adaptation actions will still be required to prepare for the already committed changes on the climate.The study of climate extremes is particularly important because of their high impact nature. Analysis of the extreme events are challenging because of their rare occurrences resulting in very few past observations that can help in any statistical analysis or conclusions. Currently available climate projections especially for extreme events at local scales are associated with a wide range of uncertainties. Apart from that, analysis and damage assessment of the extremes over a period of time also present a lot of uncertainties related to economic analysis (e.g. discount rate, growth rate) and the unknown future.Unfortunately, often end users do not understand the range of uncertainties surrounding the research outputs they use for extreme events. This research project was designed to develop a pilot tool to enable end users to analyse and prepare for extreme events in a less predictable, complex world. Due to the lack of historical data, the tool relies on expert judgements on the frequency and severity of such events. It is important to point out that the results of the analysis are highly dependent on the quality of these judgements such that the reliability of the results depends on finding appropriate experts in the field who can provide appropriate estimates for frequency and impact of the considered events. The Tool uses a combination of quantitative (Cost-Benefit Analysis) and qualitative (Multi-Criteria Analysis) methods to frame the decision support Tool. The current version of the Tool allows users to conduct sensitivity tests, examine the impact of uncertain parameters ranging from climate impacts to discount rates. The final product is a user-friendly decision tool in the form of an Excel add-in together with a user manual booklet that demonstrates sample worked out projects. The Tool is made flexible so that stakeholders can adopt or refine or upgrade it for their context specific applications.The Intergovernmental Panel on Climate Change (IPCC), the globally-recognised reference body for climate-related research, describes warming of the climate system as ‘unequivocal’. The changing climate is likely to result in the occurrence of more frequent and intense extreme weather events. This demands preventative and preparatory actions (mitigation and adaptation) from all levels of government including local governments. No matter how robust the mitigation responses will be, adaptation actions will still be required to prepare for the already committed changes on the climate.The study of climate extremes is particularly important because of their high impact nature. Analysis of the extreme events are challenging because of their rare occurrences resulting in very few past observations that can help in any statistical analysis or conclusions. Currently available climate projections especially for extreme events at local scales are associated with a wide range of uncertainties. Apart from that, analysis and damage assessment of the extremes over a period of time also present a lot of uncertainties related to economic analysis (e.g. discount rate, growth rate) and the unknown future.Unfortunately, often end users do not understand the range of uncertainties surrounding the research outputs they use for extreme events. This research project was designed to develop a pilot tool to enable end users to analyse and prepare for extreme events in a less predictable, complex world. Due to the lack of historical data, the tool relies on expert judgements on the frequency and severity of such events. It is important to point out that the results of the analysis are highly dependent on the quality of these judgements such that the reliability of the results depends on finding appropriate experts in the field who can provide appropriate estimates for frequency and impact of the considered events. The Tool uses a combination of quantitative (Cost-Benefit Analysis) and qualitative (Multi-Criteria Analysis) methods to frame the decision support Tool. The current version of the Tool allows users to conduct sensitivity tests, examine the impact of uncertain parameters ranging from climate impacts to discount rates. The final product is a user-friendly decision tool in the form of an Excel add-in together with a user manual booklet that demonstrates sample worked out projects. The Tool is made flexible so that stakeholders can adopt or refine or upgrade it for their context specific applications.Please cite this report as:Trueck, S, Mathew, S, Henderson-Sellers, A, Taplin, R, Keighley, T, Chin, W 2013 Climate Adaptation Decision Support Tool for Local Governments: CATLog: Developing an Excel Spreadsheet Tool for Local Governments to compare and prioritise investment in climate change adaptation, National Climate Change Adaptation Research Facility, Gold Coast, pp. 39

Fatores condicionantes do valor em risco do lucro operacional : uma análise de dados em painel das corporações brasileiras no período de 2008 a 2017

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Departamento de Administração, 2018.Gestão de riscos é uma das principais funções de todo e qualquer negócio e as práticas atuais de gestão destes ainda estão, quando se trata de empresas não financeiras, frágeis. Gestão de risco consiste na investigação de quatro variações de riscos altamente significantes para uma firma ou um portfólio: risco de mercado, risco de crédito, risco de liquidez e risco operacional. Diversos estudos recentes contemplam a influência do mercado (variáveis exógenas à empresa) na exposição ao risco das organizações nele inseridas. Contudo, justamente por serem exógenas à empresa, a atuação da alta gerência das firmas se limita à uma análise contínua da evolução do mercado e a busca incessante por se adaptar às mudanças. O objetivo dessa pesquisa foi aprimorar o entendimento no que tange à influência das políticas e estratégias das organizações em seu nível de risco a partir da verificação da existência de relação deste com fatores endógenos e, em decorrência disso, fornecer insumos para a formulação estratégica das empresas brasileiras. Para tanto, foi realizada uma análise de dados em painel, cuja variável dependente foi o LAJIR. Conclui-se de forma geral que, apesar da limitação deste trabalho por ser um estudo muito abrangente, as variáveis exógenas à empresa à administração da organização de fato possuem grande relevância no momento de se elaborar um modelo para análise do valor em risco do lucro operacional de uma determinada empresa. Dentre as variáveis estudadas, destaca-se a importância para o modelo proposto de determinação do valor em risco do lucro operacional das empresas brasileiras da Taxa Selic e da variação do PIB.Risk management is one of the major functions of any business and the current management practices of these are still, when it comes to non-financial companies, fragile. Risk management consists of investigating four highly significant risk variations for a firm or portfolio: market risk, credit risk, liquidity risk and operational risk. Several recent studies contemplate the influence of the market (variables exogenous to the company) in the exposure to the risk of the organizations involved in it. However, precisely because they are exogenous to the company, the actions of the top managers of the firms are limited to a continuous analysis of the evolution of the market and the incessant search for adapting to the changes. The objective of this research was to improve the knowledge regarding the influence of company policies and strategies in their level of risk from the verification of the existence of its relation with endogenous factors and, as a result, provide inputs for the strategic formulation of the companies Brazilians. For that, a panel data analysis was performed, whose dependent variable was the LAJIR. It is generally concluded that, in spite of the limitation of this work as a very comprehensive study, the variables exogenous to the company to the management of the organization indeed do have great relevance in the moment of elaborating a model for value-at-risk analysis of the operational profit of a particular company. Among the variables studied, we highlight the importance of the following variables to the model proposed to determine the value at risk of the operating income of Brazilian companies: the Selic rate and GDP variation

Grasping the nettle? Considering the contemporary challenges of risk assessment

The process of risk regulation is crucial across a range of institutions, sectors and industries. Regulatory bodies worldwide are confronted with a plethora of challenges in managing risks and uncertainties. The precise sources of these challenges are diverse, but are commonly associated with the degree of confidence in predicting and quantifying risks. When the level of confidence is high, regulators tend to specify the outputs and take quantitatively informed preventive measures. However, when levels of confidence are lower, regulators may favour an inflection toward more qualitative considerations of risks to inform resilience building and absorption of adverse consequences. As part of an ongoing research project designed to explore the potentialities of developing a holistic framework for risk assessment which blends qualitative and quantitative methods, this article maps out the key challenges involved in evaluation and decision-making within risk regulatory bodies. In defining the problems and issues faced both by organizations in general and practitioners involved in everyday assessment and management of risk, we have developed a heuristic designed to assist in understanding, categorising and evaluating risk. It is anticipated that the development of knowledge in this area can contribute toward progressive process modifications, improved decision-making at senior management level, and enhance risk management practices amongst regulatory agencies. The project involved semi-structured interviews with practitioners working in risk regulatory bodies from the UK, Germany, France, Belgium, the Netherlands and New Zealand. In coalescing the findings of empirical studies, the sources of these challenges were discussed as being related to rational, technical and expert factors. The main areas of analysis focused on in this article revolve around the process of evaluation, organisational strategies, structural factors and expert perceptions

Emergency call centers and large scale incidents: A comparison of the operators' perspective and a resilience model

A key feature of civil safety is the ability to respond to unwanted incidents. The first link in the response chain is usually the emergency call centers, and the purpose of this study was to investigate the work at an emergency call center in connection with large scale incidents. The delivery of the study is two-fold: firstly, a bottom-up analysis was performed in order to outline work at the emergency call center during large scale incidents. This approach resulted in a content model depicting the features of work during large scale incidents. Secondly, a theory driven top-down analysis was performed based on the same data in order to investigate whether a resilience perspective on safety is an appropriate framework for work at the emergency call center during large scale incidents. The data consisted of emergency call center operators’ reflections around their own work during large scale incidents, and was gathered through semi-structured interviews. Statistical comparisons between the resilience model and the content model revealed that the former was not able to account for all the statements captured in the content model, and hence it does not provide a complete framework for understanding work at the emergency call center during large scale incidents. This study provides insight to a field that has received little attention from previous research and contributes to a better understanding of the role emergency call centers play with regard to emergency management and hence to civil safety

Designing supply chains resilient to nonlinear system dynamics

Purpose: To propose an analytical framework for the design of supply chains that are resilient to nonlinear system dynamics. For this purpose, it is necessary to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. Moreover, by reviewing the literature in nonlinear control engineering, this work provides a systematic procedure for the analysis of the impact of nonlinear control structures on systems behaviour. Design/method/approach: The Forrester and APIOBPCS models are used as benchmark supply chain systems. Simpli�cation and nonlinear control theory techniques, such as low order modelling, small perturbation theory and describing functions, are applied for the mathematical analysis of the models. System dynamics simulations are also undertaken for cross-checking results and experimentation. Findings: Optimum solutions for resilience yield increased production on-costs. Inventory redundancy has been identi�ed as a resilience building strategy but there is a maximum resilience level that can be achieved. A methodological contribution has also been provided. By using nonlinear control theory more accurate linear approximations were found for reproducing nonlinear models, enhancing the understanding of the system dynamics and actual transient responses. Research limitations/implications: This research is limited to the dynamics of single-echelon supply chain systems and focus has been given on the analysis of individual nonlinearities. Practical Implications: Since that the resilience performance trades-o� with production, inventory and transportation on-costs, companies may consider to adjust the control parameters to the resilience `mode' only when needed. Moreover, if companies want to invest in additional capacity in order to become more resilient, manufacturing processes should be prioritised. Originality/value: This research developed a framework to quantitatively assess supply chain resilience. Moreover, due consideration of capacity constraint has been given by conducting in-depth analyses of systems nonlinearities