Rozprawy doktorskie na temat „Risk analysis”

The risk analysis process is the foundation of creating secure systems. An accurate and well defined risk analysis will therefore be a big help for any company, indicating what resources are needed and where they should be put to use. It can be difficult to know which risk analysis methodology to use given a set of parameters such as available resources, time, money etc. In this review we will introduce several different risk analysis methodologies and classify them using our risk analysis classification system. Our classification points out some of the pros and cons for each method, making it easier to choose the one best suited for a specific scenario. We will also connect the presented methods with real-world usage of said methods. To do this we have conducted interviews with IT-security experts at several major companies and we will present previous documented usage of risk analysis methods. Larger companies tend to develop their own methods for risk analysis, and smaller companies that do not have enough time or resources to develop their own methods are more likely to use already existing methods. With that said we believe that anyone that works with risk analysis could have use of our review.

This study reviews ship security assessment. The objectives are to explore the possibilities for quantifying and performing a more thorough ship security risk analysis than that described in the International Ship and Port Facility Security code and to evaluate to what extent this more detailed analysis increases ship security and facilitate the effective selection of risk control options. The study focuses on Somali-based maritime piracy, using piracy on the Indian Ocean as a case study. Data are collected using questionnaires and interviews with civilian and military security experts who possess firsthand experience of piracy off the coast of Somalia. The data are collected specifically for this study and describe and quantify the threat’s capability, intent and likelihood of exploiting a ship’s vulnerability. Based on the collected description of the threat, the study analyzes and describes: probability of detection by pirates, probability of successful approach, and probability of successful boarding. The performed work shows good agreement between calculated probabilities and frequencies in the cited incident reports. Also, the developed scenarios describe the most important influences on the analyzed areas. The research therefore shows that the proposed risk-based approach, which uses structurally collected and documented information on the threat, can increase ship security by assisting in selecting risk control options. The approach also allows for a better understanding of the causal relationship between threat and risk than that provided in today’s security analysis by ship owners, for example. This understanding is crucial to choosing effective and robust risk control options.

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Quantifying country risk – and in particular, political risk – poses great difficulties for business, institutions, and investors alike. As economic indicators are updated far less frequently than Facebook feeds, it can be challenging for political risk analysts to understand, and more importantly measure, what is taking place in real time on the ground. However, with the growing availability of big data from social media platforms such as Twitter, now is an opportune moment to examine the types of social media metrics that are available and the limitations to applying them to country risk analysis, particularly during episodes of political upheaval. This study, using the qualitative method of bibliographical research, identifies the current landscape of data available from Twitter, analyzes the current and potential methods of analysis, and discusses their possible application to the field of political risk analysis. After a thorough review of the field to date, and given the expected near- to medium-term technological advancements, this study concludes that despite obstacles like the cost of data storage, limitations of real-time analysis, and the potential for data manipulation, the potential benefits of the application of social media metrics to the field of political risk analysis, particularly for structured-qualitative and quantitative models, outweigh the challenges.
A quantificação do risco país – e do risco político em particular – levanta várias dificuldades às empresas, instituições, e investidores. Como os indicadores econômicos são atualizados com muito menos freqüência do que o Facebook, compreender, e mais precisamente, medir – o que está ocorrendo no terreno em tempo real pode constituir um desafio para os analistas de risco político. No entanto, com a crescente disponibilidade de 'big data' de ferramentas sociais como o Twitter, agora é o momento oportuno para examinar os tipos de métricas das ferramentas sociais que estão disponíveis e as limitações da sua aplicação para a análise de risco país, especialmente durante episódios de violência política. Utilizando o método qualitativo de pesquisa bibliográfica, este estudo identifica a paisagem atual de dados disponíveis a partir do Twitter, analisa os métodos atuais e potenciais de análise, e discute a sua possível aplicação no campo da análise de risco político. Depois de uma revisão completa do campo até hoje, e tendo em conta os avanços tecnológicos esperados a curto e médio prazo, este estudo conclui que, apesar de obstáculos como o custo de armazenamento de informação, as limitações da análise em tempo real, e o potencial para a manipulação de dados, os benefícios potenciais da aplicação de métricas de ferramentas sociais para o campo da análise de risco político, particularmente para os modelos qualitativos-estruturados e quantitativos, claramente superam os desafios.

Due to increased supply chain complexity, ensuring the quality of supply materials or products from upstream suppliers has become a challenge for firms. A great deal has been written on possible solutions and strategies to deal with supply chain quality risk (SCQR) in recent years. However, the manager’s decision-making process in relation to SCQR has not been fully researched. To close this gap, the aims of this study were to scrutinise managers’ perceptions of SCQR, as well as the antecedents of and decision-making related to perceived SCQR. The relevant literature was comprehensively reviewed in order to build a foundation for the conceptualisation of perceived SCQR. This study proposed that the managerial perception of SCQR was a multi-dimensional concept with four representations. A rigorous scale development process was adopted to develop a set of reliable instruments to measure perceived SCQR. With a sample of 316 Chinese manufacturers, the validity and reliability of the measurement scales for the representations of SCQR perception were assessed. Based on the risky-decision making model, a theoretical framework of the managerial decision-making process in relation to perceived SCQR was proposed. Furthermore, the agency theory and resource dependence theory were drawn on to identify factors related to the supply chain relationship and supply chain quality barriers that might have effects on three of the representations of SCQR. Based on the resource dependence theory, it was found that buyer and supplier dependence affect the representations of SCQR differently. The empirical results indicate that the inability to test and inability to trace are significant drivers of the perception of SCQR. The representation of SCQR impact on the intention of adopting two oriented quality management practices was also examined. The result reveals that when managers face increasing SCQR, they tend to be conservative in applying the quality management practices.

Mestrado em Ciências Actuariais
Uma parte considerável do negócio bancário inclui naturalmente o empréstimo de dinheiro. Inerentemente, o risco de não receber de volta o montante emprestado é assumido pela instituição bancária. Neste trabalho, o risco de incumprimento é estudado através da função de distribuição das perdas agregadas. Depois de feita a ponte entre as características de uma carteira de empréstimos de um banco e as características de uma carteira de apólices de seguros vida, os resultados da Teoria de Risco podem ser aplicados à carteira em estudo. O CreditRisk+, geralmente classificado como o modelo actuarial, é um modelo de risco de crédito que tem por base esta ponte. Para aplicação deste modelo, é necessária informação relativa às probabilidades de incumprimento de cada devedor e a exposição ao risco, que no nosso caso é igual ao montante em dívida. Na primeira parte deste trabalho é estimada a probabilidade de incumprimento através de um modelo logit, tendo em conta alguns indicadores financeiros da empresa. Seguidamente, no contexto de um modelo de risco coletivo, é aplicado o método iterativo de Panjer. Seguindo a metodologia proposta pelo modelo CreditRisk+, a carteira é seguidamente dividida em setores e, em cada setor, é introduzida volatilidade à probabilidade de incumprimento. No final, conclui-se que conseguem ser obtidos resultados semelhantes utilizando métodos de aproximação menos dispendiosos, nomeadamente com a aproximação NP. Finalmente, a taxa de juro média que o banco deveria aplicar aos empréstimos em carteira é calculada, assim como a reserva que deveria ter sido constituída.
A considerable part of the banking business includes the lending of money. Inherently, a bank incurs the risk of not receiving back the money lent. In this work, default risk is studied through the distribution function of the aggregate losses. After making the link between the characteristics of a portfolio of loans and of a life insurance policies portfolio, Risk Theory results are applied to the portfolio of loans under study. CreditRisk+, usually classified as the actuarial model, is a credit risk model which uses this link. As an input to this model, both the individual probabilities of default for each obligor and the exposure at risk are needed. The first part of this work focus on the estimation of the probability of default through a logit model, taking into account some financial indicators of the company. Then, in the context of a collective risk model, Panjer?s recursive algorithm is applied. Following the methodology of CreditRisk+, the portfolio is then divided into sectors and default volatility is introduced in each sector, reaching a different aggregate loss distribution function. At the end, we find that similar results are obtained with less time consuming approximation methods, particularly with NP approximation. Finally, the average interest rate that the bank should have charged to the loans in the portfolio is found as well as the amount of money that should have been reserved to account for losses.
info:eu-repo/semantics/publishedVersion

Cybersecurity is currently an important and relevant issue, as more and more industries are taking advantage of the accessibility of storing information online. To create a secure system one must know the potential risks and attacks on that system, making risk analysis a very potent tool. In this study, we performed such an analysis using the risk analysis method OCTAVE Allegro on a company providing a cloud-based service to find out what risks a cloud service provider might be exposed to, and the usefulness of said risk analysis method in this circumstance. We found that OCTAVE Allegro is suitable to use on smaller companies and applicable to cloud services, and the most severe risks identified were connected to leakage of client data with a consequence of damaging the company's reputation. Common areas of concern for these risks were third parties hacking the cloud server or other company systems to gain access to sensitive information. Such risks will likely be found at any company providing a cloud service that manages sensitive data, increasing the importance of risk analysis at these companies.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 574-589).
Tunnel construction is increasing world wide. Although the majority of tunnel construction projects have been completed safely, there have been several incidents that have resulted in delays, cost overruns, and sometimes more significant consequences such as injury and loss of life. To help eliminate these accidents, it is necessary to systematically assess and manage the risks associated with tunnel construction. In order to better understand the conditions under which accidents occur, a database of 204 tunnel construction accidents was assembled. This is the most comprehensive database known to date. The database was analyzed to better understand the causes of accidents. Influence diagrams were constructed containing the main factors, and the interactions between them. These served as the basis of the risk assessment methodology presented in this work. The risk assessment methodology consists of combining a geologic prediction model that allows one to predict geology ahead of the tunnel construction, with a decision support model that allows one to choose amongst different construction strategies the one that leads to minimum risk. The geologic prediction model is based on Bayesian networks because of their ability to combine domain knowledge with data, encode dependencies among variables, and their ability to learn causal relationships.
(cont.) The combined geologic prediction - decision support model was then applied to the Porto Metro, in Portugal. The results of the geologic prediction model were in good agreement with the observed geology, and the results of the decision support model were in good agreement with the construction methods used. More significant, however, is the ability of the model to predict changes in geology and consequently changes in construction strategy. This was shown in two zones of the tunnel were accidents occurred, where the model predicted an abrupt change in geology, and the construction method should have been changed but was not. Using the model could have possibly avoiding the accidents. This risk assessment methodology provides a powerful tool with which planners and engineers can systematically assess and mitigate the inherent risks associated with tunnel construction.
by Rita L. Sousa.
Ph.D.

Master of Agribusiness
Department of Agricultural Economics
Kevin Dhuyvetter
The size of our dairy operation increased from 300 milk cows to 1,700 milk cows in 2003. Once the dairy operation increased, the dependency on milk price to support the entire operation also increased. This was due to the fact that the cropping side of the operation became more devoted to growing feed for the livestock as opposed to producing cash crops. Thus, the increase in the number of milk cows led to decreased diversity in our income potentially increasing the financial risk of the operation. The purpose of this thesis is to study different risk management tools and strategies to aid in the formulation of a risk management plan for milk sales in our operation. Risk management strategies using forward contracts, futures, put options, and cash were analyzed at different time periods and various minimum price levels. The strategies were analyzed over the last ten years (2001-2010) of available price data. Twenty-five risk management strategies were analyzed both with and without set minimum milk prices. Minimum price levels ranged from $14/cwt to $17/cwt in $1 increments. The time frame for the transaction ranged from zero to twelve months prior to production in three-month increments. Based on historical data, risk management strategies can be used to decrease the price risk faced by an operation. The risk management strategies did not affect the average price received at statistically significant levels typically considered. Different risk management opportunities are highlighted that need to be analyzed before fully implementing a risk management plan for dairy operations.

This paper aims to deliver insights into the field of structuring a business plan for start-up companies and implementing an efficient risk analysis in the course of business planning. In order to achieve this goal, this thesis challenges the theory with practice to equally reflect both perspectives. The theoretical part first reviews the literature dealing with business plan outlines and, then, presents the author's own systematic methodology to the risk analysis in business planning. The empirical part provides a real-life example of a structured business plan where the risk analysis concept designed by the author is applied. The conclusions from this paper are the following: The business plan outline is situational and cannot be standardized to one fixed pattern or framework. The risk analysis in a business plan increases its transparency and credibility and, therefore, it should be considered for its inherent part. The application of the risk analysis across the business plan shall follow a systematic approach, which is an adapted version of official risk management standards. In this paper, the author's own adapted framework of the risk analysis in the context of business planning is elaborated and applied to the real-life business plan.

Mines have an inherent risk of geotechnical failure in both rock excavations and tailings storage facilities. Geotechnical failure occurs when there is a combination of exceptionally large forces acting on a structure and/or low material strength resulting in the structure not withstanding a designed service load. The excavation of rocks can cause unintended rock mass movements. If the movement is monitored promptly, accidents, loss of ore reserves and equipment, loss of lives, and closure of the mine can be prevented. Mining companies routinely use deformation monitoring to manage the geotechnical risk associated with the mining process. The aim of this dissertation is to review the geotechnical risk management process to optimize the geotechnical risk management analysis. In order to perform a proper analysis of slope instability, understanding the importance as well as the limitations of any monitoring system is crucial. Due to the potential threat associated with slope stability, it has become the top priority in all risk management programs to predict the time of slope failure. Datasets from monitoring systems are used to perform slope failure analysis. Innovations in slope monitoring equipment in the recent years have made it possible to scan a broad rock face in a short period with sub-millimetric accuracy. Instruments like Slope Stability Radars (SSR) provide the quantitative data that is commonly used to perform risk management analysis. However, it is challenging to find a method that can provide an accurate time of failure predictions. Many studies in the recent past have attempted to predict the time of slope failure using the Inverse Velocity (IV) method, and to analyze the probability of a failure with the fuzzy neural networks. Various method investigated in this dissertation include: Minimum Inverse Velocity (MIV), Maximum Velocity (MV), Log Velocity (LV), Log Inverse Velocity (LIV), Spline Regression (SR) and Machine Learning (ML). Based on the results of these studies, the ML method has the highest rate of success in predicting the time of slope failures. The predictions provided by the ML showed ~86% improvement in the results in comparison to the traditional IV method and ~72% improvement when compared with the MIV method. The MIV method also performed well with ~75% improvement in the results in comparison to the traditional IV method. Overall, both the new proposed methods, ML and MIV, outperformed the traditional inverse velocity technique used for predicting slope failure.

This project focuses on risk analysis of corporate bond portfolios. We separate the total risk of the portfolio into three parts, which are market risk, credit risk and liquidity risk. The market risk component is quantified by value-at-risk (VaR) determined by change in yield to maturity of the bond portfolio. For the credit risk component, we calculate default probabilities and losses in the event of default and then compute credit VaR. Next, we define a factor called basis which is the difference between the Credit Default Swap (CDS) spread and its corresponding corporate bond yield spread (z-spread or OAS). We quantify the liquidity risk by using the basis. In addition, we also introduce a Fama-French multi-factor model to analyze factor significance to the corporate bond portfolio.

This project focuses on risk analysis of corporate bond portfolios. We divide the total risk of the portfolio into three parts, which are market risk, credit risk and liquidity risk. The market risk component is quantified by value-at-risk (VaR) which is determined by change in yield to maturity of the bond portfolio. For the credit risk component, we calculate default probabilities and losses in the event of default and then compute credit VaR. Next, we define a factor called `basis' which is the difference between the Credit Default Swap (CDS) spread and its corresponding corporate bond yield spread (z-spread or OAS). We quantify the liquidity risk by using the basis. In addition we also introduce a Fama-French multi-factor model to analyze the factor significance to the corporate bond portfolio.

Risk Management has emerged during the last decades and is now considered an indispensable component in management of projects. However, no attention has been directed towards Risk management in the bidding context. Uncertainties, but also the opportunities to affect project success, are extremely high during this phase. The purpose of this thesis has been to design a schedule risk analysis method with supporting methodology based on current research and to verify its usefulness in a business environment. We have conducted a case study at the Business Unit Gripen (BUG) subdivision of SAAB Aerospace. BUG produces large and complex offers of defense systems including the Gripen aircraft. Through interviews and participative observation we have gained an understanding of the bidding context and the requirements of risk management in this phase. The case has been used to verify the usefulness of the developed framework. The results of this thesis are a new framework for schedule risk analysis during the pre-project phases and an Excel-based model for estimation and quantification of schedule risks in project networks. The method and methodology developed seems to be able to produce schedules with better precision and quite easy to integrate in the offer process. We believe that the model is applicable to many other contexts, including ongoing projects in diverse industries where it is vital to assess uncertainties in project schedules.

The usage of information systems (IS) within organizations has become crucial. Information is one of the most vulnerable resources within an enterprise. Information can be exposed, tampered or made non-accessible, where the integrity, confidentiality or availability becomes affected. The ability to manage risks is therefore a central issue in enterprises today. In order to manage risks, the risks need to be identified and further evaluated. All kind of threats with the possibility to negatively affect the confidentiality, integrity, or availability of the organization need to be reviewed. The process of identifying and estimating risks and possible measures is called risk analysis. There are two main categories of risk analysis, qualitative and quantitative. A quantitative method involves interpreting numbers from data and is based on objective inputs. A qualitative method involves interpreting of subjective inputs such as brainstorming and interviews. A common approach is to apply a qualitative method, however a lot of criticism has been raised against using subjective inputs to assessing risks. Secure State is a consulting company with specialist expertise in the field of information security. They help their customers to build trust in the customers systems and processes, making their customers businesses operate with consideration to information security. One service offered by Secure State is risk analysis, and currently they perform qualitative risk analysis. Given all criticisms against a qualitative approach for assessing risks, this study developed a quantitative risk analysis method for Secure State. According to participants, who attended at a risk analysis where the developed quantitative risk analysis method was used, the quantitative risk analysis method improved the risk assessment. Since risks and their effects are decomposed into smaller components in the proposed quantitative risk analysis method, interpretations of risks and their meaning during assessments less likely differed. Therefore, the common understanding of a risk increases, which makes the quality of the evaluation of risks increase. Furthermore, the usage of statistical data increases in the developed quantitative risk analysis method. Additionally, the quantitative method handles the fact that all data used is imperfect. The data is imperfect since it is used to describe the future, and the future has not happened yet.

The negative effects of hazardous substances and possible measures that can be taken are evaluated in the environmental risk analysis process, consisting of risk assessment, risk communication and risk management. Uncertainty due to lack of knowledge and natural variability are always present in this process. The aim of this thesis is to evaluate some tools as well as discuss the management of uncertainty and variability, as it is necessary to treat them both in a reliable and transparent way to gain regulatory acceptance in decision making. The catalytic effects of various metals on the formation of chlorinated aromatic compounds during the heating of fly ash were investigated (paper I). Copper showed a positive catalytic effect, while cobalt, chromium and vanadium showed a catalytic effect for degradation. Knowledge of the catalytic effects may facilitate the choice and design of combustion processes to decrease emissions, but it also provides valuable information to identify and characterize the hazard. Exposure factors of importance in risk assessment (physiological parameters, time use factors and food consumption) were collected and evaluated (paper II). Interindividual variability was characterized by mean, standard deviation, skewness, kurtosis and multiple percentiles, while uncertainty in these parameters was estimated with confidence intervals. How these statistical parameters can be applied was shown in two exposure assessments (papers III and IV). Probability bounds analysis was used as a probabilistic approach, which enables separate propagation of uncertainty and variability even in cases where the availability of data is limited. In paper III it was determined that the exposure cannot be expected to cause any negative health effects for recreational users of a public bathing place. Paper IV concluded that the uncertainty interval in the estimated exposure increased when accounting for possible changes in climate-sensitive model variables. Risk managers often need to rely on precaution and an increased uncertainty may therefore have implications for risk management decisions. Paper V focuses on risk management and a questionnaire was sent to employees at all Swedish County Administrative Boards working with contaminated land. It was concluded that the gender, age and work experience of the employees, as well as the funding source of the risk assessment, all have an impact on the reviewing of risk assessments. Gender was the most significant factor, and it also affected the perception of knowledge.
Negativa effekter orsakade av skadliga ämnen och möjliga åtgärder bedöms och utvärderas i en miljöriskanalys, som kan delas i riskbedömning, riskkommunikation och riskhantering. Osäkerhet som beror på kunskapsbrist samt naturlig variabilitet finns alltid närvarande i denna process. Syftet med avhandlingen är att utvärdera några tillvägagångssätt samt diskutera hur osäkerhet och variabilitet hanteras då det är nödvändigt att båda hanteras trovärdigt och transparent för att riskbedömningen ska vara användbar för beslutsfattande. Metallers katalytiska effekt på bildning av klorerade aromatiska ämnen under upphettning av flygaska undersöktes (artikel I). Koppar visade en positiv katalytisk effekt medan kobolt, krom och vanadin istället katalyserade nedbrytningen. Kunskap om katalytisk potential för bildning av skadliga ämnen är viktigt vid val och design av förbränningsprocesser för att minska utsläppen, men det är också ett exempel på hur en fara kan identifieras och karaktäriseras. Information om exponeringsfaktorer som är viktiga i riskbedömning (fysiologiska parametrar, tidsanvändning och livsmedelskonsumtion) samlades in och analyserades (artikel II). Interindividuell variabilitet karaktäriserades av medel, standardavvikelse, skevhet, kurtosis (toppighet) och multipla percentiler medan osäkerhet i dessa parametrar skattades med konfidensintervall. Hur dessa statistiska parametrar kan tillämpas i exponeringsbedömningar visas i artikel III och IV. Probability bounds analysis användes som probabilistisk metod, vilket gör det möjligt att separera osäkerhet och variabilitet i bedömningen även när tillgången på data är begränsad. Exponeringsbedömningen i artikel III visade att vid nu rådande föroreningshalter i sediment i en badsjö så medför inte bad någon hälsofara. I artikel IV visades att osäkerhetsintervallet i den skattade exponeringen ökar när hänsyn tas till förändringar i klimatkänsliga modellvariabler. Riskhanterare måste ta hänsyn till försiktighetsprincipen och en ökad osäkerhet kan därmed få konsekvenser för riskhanteringsbesluten. Artikel V fokuserar på riskhantering och en enkät skickades till alla anställda som arbetar med förorenad mark på länsstyrelserna i Sverige. Det konstaterades att anställdas kön, ålder och erfarenhet har en inverkan på granskningsprocessen av riskbedömningar. Kön var den mest signifikanta variabeln, vilken också påverkade perceptionen av kunskap. Skillnader i de anställdas svar kunde också ses beroende på om riskbedömningen finansierades av statliga bidrag eller av en ansvarig verksamhetsutövare.

An important objective in statistical risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches for BMD analysis typically involve one-sided, frequentist confidence limits, leading in practice to what are called Benchmark Dose Lower Limits (BMDLs). With this context, a quantitative methodology is developed to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk- analytic BMDs are then estimated from this modeling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new, translational adaptation of the risk-benchmark approach, including its ability to account for geospatial autocorrelation, is seen to operate quite flexibly in this socio-geographic setting. Further, alternative definitions for neighborhoods are considered to extend the autologistic benchmark paradigm to non-spatial settings. All 3108 counties in the contiguous 48 U.S. states are studied to identify a benchmark dose variable as the number of hazards. This is employed to benchmark billion-dollar losses across each county. County-level resilience is used as a potential characteristic for defining the neighborhood structure within the autologistic model.

Uncertainty in situations involving risk is frequently modelled by assuming a plausible form of probability distribution for the uncertain quantities involved, and estimating the relevant parameters of that distribution based on the knowledge and judgement of informed experts or decision makers. The distributions assumed are usually uni-modal (and often bell-shaped) around some most likely value, with the Normal, Beta, Gamma and Triangular distributions being popular choices.

Thesis (M.S.)--West Virginia University, 2007.
Title from document title page. Document formatted into pages; contains vii, 66 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 55-57).

In order to model the binary process of precipitation and the dichotomized temperature process, we use the conditional probability of the present given the past. We find necessary and sufficient conditions for a collection of functions to correspond to the conditional probabilities of a discrete-time categorical stochastic process X₁,X₂,···. Moreover we find parametric representations for such processes and in particular rth-order Markov chains. To dichotomize the temperature process, quantiles are often used in the literature. We propose using a two-state definition of the quantiles by considering the "left quantile" and "right quantile" functions instead of the traditional definition. This has various advantages such as a symmetry relation between the quantiles of random variables X and -X. We show that the left (right) sample quantile tends to the left (right) distribution quantile at p ∈[0,1], if and only if the left and right distribution quantiles are identical at p and diverge almost surely otherwise. In order to measure the loss of estimating (or approximating) a quantile, we introduce a loss function that is invariant under strictly monotonic transformations and call it the "probability loss function". Using this loss function, we introduce measures of distance among random variables that are invariant under continuous strictly monotonic transformations. We use this distance measures to show optimal overall fits to a random variable are not necessarily optimal in the tails. This loss function is also used to find equivariant estimators of the parameters of distribution functions. We develop an algorithm to approximate quantiles of large datasets which works by partitioning the data or use existing partitions (possibly of non-equal size). We show the deterministic precision of this algorithm and how it can be adjusted to get customized precisions. Then we develop a framework to optimally summarize very large datasets using quantiles and combining such summaries in order to infer about the original dataset. Finally we show how these higher order Markov models can be used to construct confidence intervals for the probability of frost-free periods.

The supervising institutions do not give to commercial banks indications what models have to be used for stress testing. This research was done in order to find out which mathematical/statistical models are and can be used in credit risk stress testing. Credit risk is one of the biggest financial risks that every bank faces. Stress testing is a tool of credit risk assessment that helps to estimate the consequences of the events that have really small probability to happen but if they occur, banks can have significant losses. This study determined that the most plausible event is adverse macroeconomic conditions. For this reason, models that include macroeconomic impact were presented. Vector autoregression and vector error correction model were tested using the empirical data received from Swedish central bank, Swedish statistics and Eurostat. For financial stability it is worth using vector autoregression or vector error correction model as they describe the macroeconomic environment in the most suitable way and they are appropriate for shock analysis by showing how the impact of any factor can change the whole system. Structure: introduction, main part (credit risk, methods and empirical analysis), publication, conclusions, references. Thesis consists of: 50 p. text without appendices, 13 pictures, 11 tables, 26 bibliographical entries. Appendices included.
Kredito įstaigų priežiūros institucijos nepateikia komerciniams bankams kokius metodus jie turėtų naudoti testavime nepalankiomis sąlygomis. Tiriamasis darbas buvo atliktas tuo tikslu, kad būtų išsiaiškinta kokie matematiniai ir statistiniai metodai yra ir gali būti naudojami kredito rizikos vertinime testuojant nepalankiomis sąlygomis. Kredito rizika yra viena iš didžiausių finansinių rizikų su kuria bankai susiduria. Testavimas nepalankiomis sąlygomis yra kredito rizikos vertinimo įrankis, padedantis nustatyti įvykių, kurių realizavimosi tikimybės yra mažos, tačiau jiems įvykus, bankai patirtų reikšmingus nuostolius, pasekmes. Šis tyrimas nustatė, jog labiausiai tikėtinas įvykis gali būti ypatingai nepalankios ekonominės sąlygos. Dėl šios priežasties darbe yra pristatyti metodai, kurie įvertina makroekonominių veiksnių įtaką. Vektorinė autoregresija ir vektorinis paklaidų korekcijos modelis buvo patikrinti naudojant Švedijos centrinio banko, Švedijos statistikos departamento ir Eurostat empirinius duomenis. Finansinio stabilumo įvertinimui vertėtų naudoti vektorinį autoregresijos ar vektorinį paklaidų korekcijos modelius, nes šie modeliai geriausiai aprašo ekonominę aplinką bei yra labai tinkami šokų analizei, kadangi įvertina bet kurio veiksnio įtaką visai sistemai. Struktūra: įvadas, pagrindinė dalis (kredito rizika, metodai ir empirinė analizė), publikacija, išvados, literatūros sąrašas. Tiriamasis darbas sudarytas iš: 50 psl. teksto be priedų, 13 paveikslų, 11... [toliau žr. visą tekstą]

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Com o aumento da popularidade e acessibilidade de sistemas de realidade virtual (RV) de alta qualidade, tem-se levantado preocupações com relação a tendência dos sistemas de realidade virtual em provocar perda de equilíbrio. O equilíbrio é essencial para o uso seguro da realidade virtual e a perda do mesmo pode causar lesões graves. O objetivo deste trabalho é criar um sistema para avaliar o impacto da realidade virtual no equilíbrio humano. Neste trabalho, propomos e conduzimos um experimento usando o Oculus Rift e o MS Kinect Sensor. Nesse experimento, foi possível observar, quantificar e comparar o efeito de diferentes cenas de RV no equilíbrio dos usuários, bem como o efeito de avisos visuais e sonoros sobre perda de equilíbrio.
With the increasing popularity and accessibility of high-quality Virtual Reality (VR) systems, concerns have been raised about the propensity of VR to induce balance loss. Balance is essential for safe use of VR experience and its loss can result in severe injury. This project is set to create a system able to measure the impact of VR in the human balance system. In this work, we design and conduct an experiment making use of the Oculus Rift VR headset and MS Kinect Sensor. In this experiment, we are able to visualize, quantify, and compare the effect of different VR scenes on the balance of the experiment subjects as well as the effect of visual and auditory warnings of balance loss.

This thesis identifies and demonstrates the methodological challenges of meta-analysing risk prediction models using either aggregate data or individual patient data (IPD). Firstly, a systematic review of published breast cancer models is performed, to summarise their content and performance using aggregate data. It is found that models were not available for comparison. To address this issue, a systematic review is performed to examine articles that develop and/or validate a risk prediction model using IPD from multiple studies. This identifies that most articles only use the IPD for model development, and thus ignore external validation, and also ignore clustering of patients within studies. In response to these issues, IPD is obtained from an article which uses parathyroid hormone (PTH) assay (a continuous variable) to predict postoperative hypocalcaemia after thyroidectomy. It is shown that ignoring clustering is inappropriate, as it ignores potential between-study heterogeneity in discrimination and calibration performance. This dataset was also used to evaluate an imputation method for dealing with missing thresholds when IPD are unavailable, and the simulation results indicate the approach performs well, though further research is required. This thesis therefore makes a positive contribution towards meta-analysis of risk prediction models to improve clinical practice.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1998.
Includes bibliographical references (leaves 80-85).
The first part of this thesis presents an analysis on the factors contributing to groundings when ships transit in and out of ports. The study has been part of a three-year project on "Ship Transit Risk". By verifying the grounding location database generated during the first two years of the project against the United States Coast Guard's grounding accident data, an updated database was established in this research. Within the frame of this new database, two factors were analyzed-tide and time of day. The results suggest that tide forecast error (predicted tide water level minus observed tide water level) had no significant effect as a risk factor, and that night navigation was far more risky than day navigation. The second part of this thesis presents a complementary point of view for risk analysis human factors. Many papers indicate that human beings are the most significant factors contributing to groundings. Nevertheless, few or no historical data sets recorded the information required for human factors analysis. Hence this part focuses mostly on literature review, introduces briefly the concept of the International Safety Management (ISM) Code, and some maritime regulations and final rules of OSHA (Occupational Safety and Health Administration). OSHA contributes to much research in human factors, yet few maritime related papers pay attention to it. Thus, we suggest that for further research on human factors, the collaboration of OSHA and maritime related research organizations such as USCG is necessary. The cooperative research would greatly contribute to the success of a risk model for groundings that may be also applied to other accident analysis. In addition, the exploration of a risk model for groundings may have a positive effect on the ISM code and OSHA's final rules in the future.
by Shu-chiang Lin.
S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaves 59-60).
In this thesis we quantify the risk arbitrage investment process and create trading strategies that generate positive risk-adjusted returns. We use a sample of 895 stock swap mergers, cash mergers, and cash tender offers during 1998-2004Q2. We test the market efficiency hypothesis, and after accounting for transaction costs, we find that our risk arbitrage strategies generate annual risk-adjusted returns in excess of 4.5%. The research also obtains various other merger statistics, and relates them to a variety of economic indicators and merger timing models, as described in past work. We also estimate conditional probabilities of a merger's success, using a deal characteristic-driven prediction model, and combine it with market-implied probabilities. Our analysis suggests that the probability of success of a merger depends on a deal's characteristics. Further, it implies that one can improve on the market-implied estimates thereby creating trading opportunities. The analytical results achieved in this thesis can be used as the foundation for building an effective risk arbitrage trading platform.
by Akshay Naheta.
S.M.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 69-70).
In this paper, we apply spectral analysis tools to portfolio management. Recognizing volatility and factor beta as major risk sources, we analyze the short-term and longterm components of risk for any given portfolio. We model the portfolio weights as an LTI system filter and describe how the risk metrics behave as one holes the portfolio over increasing horizon. Then, we propose dynamic portfolios to shift frequency-specific risks without changing the investment period or net dollar exposure.
by Dongyoung Kim.
M. Eng.

The imminent Franchise Break, the latest step in the opening up to competition of the UK Electricity Supply market, has highlighted the issues that will affect the prices which customers pay for electricity. In particular, the fluctuating wholesale price of electricity has exposed the industry to new risks and an ever-increasing need for better information and analysis. At present, Suppliers purchase electricity wholesale from the UK market for electricity, the Pool, and sell it on to end-users, hoping to achieve a profit: there are many companies eager to become involved in this business. Since most electricity contracts between Supplier and end-user are agreed on the basis of fixed tariffs the Suppliers are exposed to significant risk. In order to quote a customer a tariff, or set of tariffs, for his future use of electricity, the Supplier needs to forecast both the customer's demand and the price of electricity in the Pool. The amount of revenue the Suppler receives from a customer depends on the actual amount and pattern of electricity that the customer consumes and the tariffs that the Supplier has agreed to charge. The cost to the Supplier of supplying this electricity depends, in part, on the actual prices of electricity in the Pool. If the Supplier's forecasts are significantly different from the actual behaviour of the customer's demand and the Pool price, the contract can perform in a very unexpected manner. This may lead to the Supplier making no profit on a contract. In the worst case, the Supplier can lose money. This thesis discusses these risks and describes a decision support system, the Electricity Sales Integrated Price and Risk Analysis System (ESIPRAS), which has been developed to provide a useful analytical tool for electricity Suppliers. The system helps a Supplier to price contracts with clients and aims to provide him with a clearer understanding of the level of risk each contract might represent.

Nowadays, the time and resources needed to get an accurate estima-tion of a client’s ability to pay back a loan has gone up. With theamount of data complexity it involves to do the credit risk analysis, the machine learning technique has been used to ease the process.To help a bank institute get a better insight into their client’s eco-nomic state. The thesis is to present a model that could help themfind interesting information using machine learning.With many clients having nonlinear income and expenses, it madethe machine learning algorithm of choosing, in this case, Linear Re-gression, very hard to predict an accurate output, the next month’ssalary. However, interesting relations between trends and the datahave been found.
\noindent Numera har tiden och resurserna som behövs för att få en exakt uppskattning av en kunds förmåga att betala tillbaka ett lån ökat. Med mängden datakomplexitet som det innebär att göra kredit riskanalys har maskininlärnings tekniken använts för att underlätta processen. För att hjälpa ett bank institutet att få en bättre inblick i kundens ekonomiska tillstånd. Avhandlingen är att presentera en modell som kan hjälpa dem att hitta intressant information med hjälp av maskininlärning. Med många kunder som har icke-linjära inkomster och kostnader gör det maskininlärnings algoritmen att välja, i detta fall linjär regression, mycket svårt att förutsäga en exakt produktion, nästa månadslön. Intressanta relationer mellan trender och data har dock hittats.

Flood risk management seeks to reduce flood consequences and probability by considering a wide range of options that include non-structural measures such as flood event management. Quantitative flood risk analysis has provided a powerful tool to support appraisal and investment in engineered flood defence. However, analysing the risks and benefits of non-structural measures have been limited making it difficult to compare the benefits of a wide range of options on a shared assessment platform. A major challenge to understand the performance of non-structural measures during a flood event is the complexity of analysing the human responses in the system that determines the successful operation of flood event management. Here presents a risk analysis approach that couples a multi-agent simulation of individual and organizational behaviour with a hydrodynamic model. The model integrates remotely sensed information on topography, buildings and road networks with empirical survey data and information on local flood event management strategies to fit characteristics of specific communities. The model has been tested in Towyn, North Wales, and subsequently used to analyse the effectiveness of flood event management procedures, including flood warning and evacuation procedures in terms of potential loss of life , economic damages and the identification of roads susceptible to congestion. The potential loss of life increases according to the magnitude of a storm surge (e.g. 11 for 1 in 100 years surges as opposed to 94 for 1 in 1000 surges). Providing 3 hours flood warning can reduce this by 67% if individuals take appropriate action. A global sensitivity analysis shows that hydrodynamic processes are only responsible for 50% of the variance in expected loss of life because actions taken by individuals and society can greatly influence the outcome. The model can be used for emergency planners to improve flood response in a flood event.

The dissertation will at first analyze the major trends and technologies adopted in the business context nowadays, analysing the main advantages and disavantages with a focus on the cyber risks that emerge in adopting such technologies. In additon, will be discuss how these risks really impact a corporation, analyzing the main economìc consequences that cyber-attacks and data breaches have. Moreover, in the dissertation will be introduced some key controls to minimize the possibility of a data breach, also presenting the NIST Framework as tool to be adopted in order to increase the overall cybersecurity of an organization. To conclude, a litterature review on how the cyber risks can be evaluate in the business field will be presented, also analysing some issues that companies face in this evaluation.

European Critical Infrastructures, as define in Green book (COM(2005) 576, November 2005), could include those physical resources, services, information technology facilities, networks and infrastructure assets, which, if disrupted or destroyed would have serious impacts on the health, safety, security, economic or social well-being of either Member States. Critical infrastructure can be damaged, destroyed or disrupted by deliberate acts of terrorism, natural disasters, negligence, accidents or computer hacking, criminal activity and malicious behavior. To save the lives and property of people at risk in the EU from terrorism, natural disasters and accidents, any disruptions or manipulations of CI should, to the extent possible, be brief, infrequent, manageable, geographically isolated and minimally detrimental to the welfare of the Member States, their citizens and the European Union. The gas distribution network is a critical infrastructure and the damage or an accident can cause damage to structures and people. Similarly, the network is vulnerable because the lack of gas supply due to social and political problems, such as the Ukraine-Russia crisis or the recent political changes in Libya, may create inefficiencies. The aim of this thesis is to analyze and then estimate the risk assessment of this infrastructure, in particular the study will focus on the quantitative risk analysis (QRA). In this thesis critical infrastructures studied are two: the Italian natural gas distribution network at high pressure and the hypothetic UK pipeline network of CO2 from Carbon Capture and Storage(CCS) processes. The work of CO2 network was conducted at Imperial College of London, in collaboration with Professor Sandro. Macchietto. The networks studied are different because the transported substances produce different consequences: natural gas is a flammable, while CO2 is toxic material, at high concentrations leads to asphyxiation. For natural gas network it was necessary to create a database with the data from different company that operate in this sector. The network was simulated with process simulator Aspen Plus ®, because data of pressure and flow rate of each section of network were missing and they required for the software of consequences calculation, PHAST. Thanks to the simulator it has been possible to study the vulnerability of the network in case of interruption of gas supply from other exporting countries, as ex Soviet countries and countries of North Africa, highlighting the dependence Italian by importing countries. The simulations have shown the important role of regasification terminals for liquefied natural gas (LNG), as they help to make independent a country . Quantitative risk analysis due to accident was conducted following the methodology proposed in the literature, described in Chapter 1 and Chapter 4.Failure frequency and the consequences were assessed by literature data and methodologies that are based on the techniques of event tree. The consequences estimation was perform with the software PHAST of DNV company. The results of risk analysis is the determination of local risk and social risk. the social risk was calculated for a section of network, because the data of population density available were for Friuli Venezia Giulia, Veneto and Trentino Alto Adige regions. Another case study of risk analysis of natural gas distribution network was conducted to off-shore LNG terminal. The LNG terminal analyzed is Floating Storage and Regasification Unit terminal (FSRU). For the CO2 network the consequences of release were evaluated. The results show that the proposed safety improvement are different for the two network types. The natural gas network is a network that is structured and then consolidated then the actions arising from risk will be mitigation and prevention actions. Considering the results obtained from the analysis of consequences of CO2 network, proximity of the network to population centers can produce injuries. Being a network proposal, the actions, that it can take, are to verify from technical and economic point of view, the shift of one or more parts of the network outside the areas whit high or medium density population. afterwards it is necessary to analyze the consequences associated with a release to see if the actions had improvements the safety.
Le infrastrutture critiche dell’UE, come definite nel Libro verde (COM (2005) 576 Libro verde), consistono nelle infrastrutture materiali e di tecnologia dell'informazione, reti, servizi e beni il cui danneggiamento o distruzione avrebbero gravi ripercussioni sulla salute, la sicurezza e il benessere economico o sociale dei cittadini degli Stati membri. Le infrastrutture critiche (IC) possono essere danneggiate, distrutte o manomesse a causa di atti deliberati di terrorismo, calamità naturali, negligenza, incidenti, pirateria informatica, attività criminose e comportamenti dolosi. Per tutelare la vita e i beni dei cittadini dell’UE dai rischi legati al terrorismo, alle calamità naturali e agli incidenti, bisogna fare in modo che gli eventuali danni alle infrastrutture critiche o la loro manomissione siano, nella misura del possibile, di breve durata, poco frequenti, gestibili, geograficamente isolati e il meno nocivi possibile per il benessere degli Stati membri, dei loro cittadini e dell’Unione europea. La rete di distribuzione gas è un’infrastruttura critica e il danneggiamento o un incidente può provocare danni a strutture e persone. Inoltre la rete è vulnerabile in quanto la mancanza di fornitura di gas a causa di problemi socio politici, come la crisi Ucraina-Russia o i recenti cambiamenti politici in Libia, possono creare dei disservizi. Lo scopo della tesi è analizzare e quindi effettuare una valutazione del rischio di tale infrastruttura, in particolare lo studio si focalizzerà sull’analisi del rischio quantitativa (QRA). Si sono studiate due tipologie di rete di distribuzione: la rete di distribuzione gas naturale Italiana ad alta pressione e una proposta di rete di distribuzione trasportante CO2 derivante dal sistema di Carbon Capture and Storage (CCS). L’analisi della rete di CO2 è stata condotta durante il soggiorno all’estero presso Imperial College of London, con la supervisione del Prof. Sandro Macchietto. Le reti studiate sono diverse in quanto le sostanze trasportate producono differenti conseguenze: il gas naturale è una sostanza infiammabile mentre la CO2 è una sostanza tossica, ad alte concentrazioni porta ad asfissia. A causa della frammentarietà dei dati della rete di distribuzione NG la rete è stata ricostruita e simulata con il simulatore di processo Aspen Plus®. Le simulazioni effettuate hanno valutato i dati mancanti, pressione e portata di ogni tratto della rete, richiesti dal software per il calcolo delle conseguenze PHAST. Inoltre grazie al simulatore è stato possibile studiare la vulnerabilità della rete nei casi di interruzione di fornitura di gas da altri Paesi esportatori, Paesi Sovietici e Paesi del Nord Africa, evidenziando la dipendenza italiana ai paesi importatori. Nelle simulazioni è emerso il ruolo importante dei terminali di rigassificazione di gas naturale liquefatto (LNG), in quanto contribuiscono a rendere il Paese più autonomo. Per quanto riguarda l’analisi quantitativa del rischio da incidente è stata effettuata seguendo la metodologia proposta in letteratura, descritta nel capitolo 1 e capitolo 4. Sono state valutate le frequenze di rilascio e di conseguenze attraverso dati di letteratura e metodologie che si basano sulle tecniche di albero degli eventi. Le conseguenze sono state stimate attraverso l’utilizzo del codice di calcolo PHAST. I risultati dell’analisi del rischio sono la determinazione del rischio locale per la rete nazionale di distribuzione del gas naturale e il rischio sociale per una sezione di rete, in quanto erano disponibili i dati di densità di popolazione delle regioni Friuli Venezia Giulia, Veneto e Trentino Alto Adige. La rete di distribuzione gas comprende anche i terminali di rigassificazione di gas naturale liquefatto. Per tali strutture è stata condotta un l’analisi del rischio e quindi la determinazione del rischio locale per un terminale off-shore del tipo Floating Storage and Regasification Unit terminal (FSRU). Per la rete di trasporto di CO2 sono state valutate le conseguenze del rilascio in un ipotetica rete in Gran Bretagna che coinvolge diversi tipi di impianti di CCS. Dai risultati si nota che le proposte di miglioramento della sicurezza sono diverse. La rete gas naturale è una rete già strutturata e consolidata quindi le azioni derivanti dall’analisi del rischio saranno di mitigazione e prevenzione. Mentre la rete di CO2 è una proposta quindi l’analisi del rischio mette in evidenziare i tratti che possono generare danni a persone. Le azioni che si possono fare per questa rete sono spostare i tratti di pipeline e valutare dal punto di vista tecnica economica la nuova soluzione. Dopo lo spostamento sarà necessario verificare se le azioni intraprese portano miglioramenti dal punto di vista della sicurezza.

A competing risks situation arises when a unit can fail due to several distinct failure types. In a competing risk situation, standard techniques from survival analysis may lead to incorrect and biased results. In this thesis, the theory of competing risks is used to identify possible risk factors for breast, uterine and ovarian cancer. This has been done by regression on the cause specific hazard functions, the subdistribution hazard functions and two approximate methods. Cox regression is used for a complete analysis of the medical data.By following 61457 women over approximately 50 years, it has been observed 3407 cases of breast cancer, 934 of uterine cancer and 843 of ovarian cancer. Summarized, it has been found that several births decrease the risk of breast, uterine and ovarian cancer. Obesity is associated with increasing risk of ovarian cancer for postmenopausal women, but not premenopausal. A long reproductive period (early menarche and/or late menopause) and high BMI increases the risk of breast and uterine cancer. Late first and last birth decreases the risk of uterine cancer, while it increases the risk of breast cancer. The data used in the analysis is selected from a screening program organized by the Norwegian Cancer Society for early diagnosis of breast cancer. postmenopausale women, but not premenopausale. A long reproductive period (early menarche and/or late menopause) and high BMI increases the risk of breast and uterine cancer. Late first and last birth decreases the risk of uterine cancer, while it increases the risk of breast cancer. The data used in the analysis is selected from a screening program organized by the Norwegian Cancer Society for early diagnosis of breast cancer.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2006.
Includes bibliographical references (p. 50-52).
Disruptions in the bulk power grid can result in very diverse consequences that include economic, social, physical, and psychological impacts. In addition, power outages do not affect all end-users of the system in the same manner. For these reasons, a risk analysis of bulk power systems requires more than determining the likelihood and magnitude of power outages; it must also include the diverse impacts power outages have on the users of the system. We propose a methodology for performing a risk analysis on the bulk power system. A power flow simulation model is used to determine the likelihood and extent of power outages when components within the system fail to perform their designed function. The consequences associated with these failures are determined by looking at the type and number of customers affected. Stakeholder input is used to evaluate the relative importance of these consequences. The methodology culminates with a ranking of each system component by its risk significance to the stakeholders. The analysis is performed for failures of infrastructure elements due to both random causes and malevolent acts.
by Anthony M. Koonce.
S.M.

Purpose: The aim of this thesis is to investigate risk management strategies for international logistics operations that can minimise the occurrence and/or the impact of risks in order to achieve a desirable logistics network. For this purpose, international logistics risks were analysed to find out critical risk areas, and then strategies to mitigate those risks were developed and validated in relation to organisational orientations and outcomes. Methodology: Risk identification, risk clustering and risk analysis were conducted by using focus group research and Interpretive Structural Modelling (ISM) to investigate risk areas that should be mitigated. A risk management strategy model was developed using Information Processing Theory, a review of extant supply chain risk management studies and interviews with logistics practitioners. The model was empirically tested with questionnaire survey data using descriptive statistics, ANOVA and Partial Least Square Structural Equation Modelling (PLS-SEM). Findings: International logistics risks consists of value streams; information and relationships; logistics activities; and the external environments. Among these, information and relationships risks were found to generate self-enhancing risk loops, thereby creating subsequent risk impacts after disruptions. To mitigate these risks, firms involved in international logistics implemented strategies, such as building a stable logistics network, leveraging logistics information, leveraging outsourcing contracts and developing logistics collaboration, although the level of implementation depends on the business context. Among the four strategies, building a stable logistics network and developing logistics collaboration strategies were most effective in strengthening both robustness and resilience in the logistics network. Customer orientation had positive impacts on all four strategies, but disruption orientation and quality orientation influenced certain types of strategies. Research Implications: This is the first study which has applied a three-phase risk management process to international logistics operations, thereby highlighting distinctive features of international logistics risks. This thesis empirically develops and validates a risk management strategy model which embraces both strategies and relevant tactical/operational initiatives. The antecedents and outcomes of risk management strategies were also investigated and conceptualised for future research. Practical Implications: The profile of risks, risk sources, loss types and risk levels provide a guideline for logistics managers to anticipate and proactively deal with potential risks. Also, they can evaluate the current status of risk management efforts and can benchmark suggested strategies and practices in consideration of the strategic fit to their organisations.

My thesis will focus on risk management practically in developing countries. In this research study, first a literature review about the concept of risk management and its concerns is provided which is the output of extensive review of resources such as journal papers, books and research studies. Then a case study will be reviewed in Iran which is considered as a developing country. In this step, the researcher attempts to extract needed information for her research based on the theory of frames. After gathering needed information the findings which are extracted from practical environment are compared to the academics material to answer the questions which are defined in the primary steps. Meanwhile, the main purpose of this study is reviewing the conditions of work environment in developing countries and extract main concerns and compare them to the standards, methodologies and tools and techniques of risk management to improve the efficiency and effectiveness of using these tools in performing risk management in developing countries.

In this thesis, I study the performance behaviour of hedge funds and mutual funds. I study a basket of various risk statistics that are widely used to measure the fluctuation of asset prices. Those risk statistics are used to rank the performance of the assets. The linear dependence relation of these risk measures in ranking assets is investigated and the set of risk measures is reduced by excluding risk measures that produce linearly dependent ranking vectors to other risk measures. The ranks within each of the selected remaining risk statistics are standardised and then linearly transformed into a new set of linearly independent factors where principal component analysis is carried out as a variable reduction technique to remove the noise while preserve the main variation of the original data. The transformed factors are sorted in descending order according to their contribution to the variation of the original data. The factor loadings of the first two principal components PC1 and PC2 are reviewed and interpreted as styles (PC1 as consistency and PC2 as aggression). The universe of a set of hedge funds is classified according to these styles as BL=(low consistency, low aggression), BR=(high consistency, low aggression), TL=(low consistency, high aggression) and TR=(high consistency, high aggression). I examine the performance behaviour of the four different classified classes whereby this classification method provides an indication on returns and management styles of hedge funds. A three-factor prediction model for asset returns is introduced by regressing 12 weeks' forward rank of return on the historical ranks of risk statistics. The first few principal components, which explain the main variation of information captured by risk statistics, are used in the prediction model. The robustness of the model is tested by applying the model to the following 12-week period using the set of independent factors. An investment strategy is constructed based on the prediction model using the set of independent factors. I discover high evidence of predictability and I test for out-of-sample forecasting performance. I then examine the use of subsets of risk statistics from the basket rather than using the set of all risk statistics. I further study the use of the so-called σ2/μ risk measure in predicting the market “turning point” of performance of a portfolio of hedge funds. Risk measure quantity σ2/μ replaces the traditional variance σ2 in the Black-Scholes option valuation formula when it is evaluated for hedge funds.

Thesis (Ph. D.) -- University of Maryland, College Park, 2004
Thesis research directed by: Reliability Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

In our daily life we are surrounded by different kind of risks and we constantly strive for better methods to quantify and in the prolongation manage these risks. Every activity involves some risks and there are some kinds of risks and some level of risks that we are unwilling to accept. We all like to live a life that is free from risks, but that is impossible.

The word risk has a lot of different interpretations. In this thesis we shall let risk stand for the combination of random or uncertain events with negative consequences for human health, life and welfare and for the environment together with some measures of the likelihood of such events. We believe this is the prevailing concept or understanding of risk; as the probability of an event followed by some negative consequences or activities of that event.

In risk analysis one tries to recognize the nature of various risks and to assess the magnitude of the risks. In the risk analysis it is very important to know what system to consider and this is not self evident in many cases. The situation is clearly different for planning and/or building a system compared with running the same system in a real time state. The system that is going to be the subject to the risk analysis must be clearly defined and the limitations and the boundaries of the system must be set. It is very important to ensure that all persons involved in a risk analysis have a common understanding of the system being considered, including relevant operations.

During the past decades many studies have been carried out on risk related topics and the society has showed a significant interest in the field of risk analysis. Risk analysis is the interdisciplinary field of science that combines results and knowledge of probability theory, mathematical statistics, engineering, medicine, philosophy, psychology, economics and other applied disciplines.

In this thesis we will give some examples of different risk analyses carried out basically within two areas. The first part of the thesis (paper 1- paper V) describes different risk analyses carried out in the area of transportation. This is an area with large differences between the different modes of transportation in respect to, for example number of users, number of accidents, magnitude of the accidents and accessible data. The latter part of the thesis (paper VI and paper VII) describes two risk analyses carried out in the field of medicine. Medicine is a science, which has used methods from the area of risk analysis for a long time. The different papers will be used to discuss risk analysis as a tool in decision-making.

QC 20100616

The analysis of decision making under risk involves (i) risk assessment - the preparation of appropriate probability assessments of stochastic events; (ii) risk management – the application of quantifiable measures of the impacts of the stochastic events under alternative strategies; and (iii) risk communication – through the governance of structured decision making processes, and tracking and monitoring of the event. This work involves the application of the steps of the risk analysis process on coastal communities facing short-term operational decisions and long-term strategic decisions to deal with the pending impacts of climate change. These impacts include the immediate impacts from the increased frequency of severe storms and storm surge, and the long-term impacts of rising sea levels. The analysis of risk is in support of coastal communities’ decision support systems. This work is a part of the C-Change International Community-University Research Alliance (ICURA) program that is examining the adaptation of selected coastal communities in Canada and the Caribbean. In all these cases, the foundation to support community-based decision making for adaptation is required in the event of mounting evidence that coastal communities are especially susceptible to the changing climate, that coastal communities are under-resourced with respect to their abilities to respond to the climate threats, and that coastal communities are in need of defensible structures on which to make critical decisions on adaptation that will ensure community sustainability. The work draws on: (1) statistical, time-series analysis for predicting the event of storms; (2) the profiling of community threats and vulnerabilities, as well as community environmental, economic, social, and cultural priorities; (3) the calibration and interpretation of storm impacts through utility curve analysis; and (4) the application of the risk analysis to decision making in complex, multi-criteria environments. The significance of determining an appropriate time horizon for adaptation decision making impacts communities’ outcomes. If strategic planning periods are too short, evidence from this research demonstrates that the often used strategy of Status Quo or ‘Do Nothing’ may be justified. However, a more strategic planning period clearly indicates that an active adaptation strategy to Accommodate or Protect the community from severe storms is much preferred to the ‘Do Nothing’ strategy or the unwelcome Retreat strategy in the face of more frequent severe storms. The community of Charlottetown, PEI, is considered as the application of this study.

No
The surprise advent of the peer-to-peer payment system Bitcoin in 2009 has raised various concerns regarding its relationship to established economic market ideologies. Unlike fiat currencies, Bitcoin is based on open-source software; it is a secure cryptocurrency, traded as an investment between two individuals over the internet, with no bank involvement. Computationally, this is a very innovative solution, but Bitcoin’s popularity has raised a number of security and trust concerns among mainstream economists. With cities and countries, including San Francisco and Germany, using Bitcoin as a unit of account in their financial systems, there is still a lack of understanding and a paucity of models for studying its use, and the role Bitcoin might play in real physical economies. This project tackles these issues by analysing the ramifications of Bitcoin within economic models, by building a computational model of the currency to test its performance in financial market models. The project uses established agent-based modelling techniques to build a decentralised Bitcoin model, which can be ‘plugged into’ existing agent-based models of key economic and financial markets. This allows various metrics to be subjected to critical analysis, gauging the progress of digital economies equipped with Bitcoin usage. This project contributes to the themes of privacy, consent, security and trust in the digital economy and digital technologies, enabling new business models of direct relevance to NEMODE. As computer scientists, we consider Bitcoin from a technical perspective; this contrasts with and complements other current Bitcoin research, and helps document the realizable risks Bitcoin and similar currencies bring to our current economic world. This report outlines a comprehensive collection of risks raised by Bitcoin. Risk management is a discipline that can be used to address the possibility of future threats which may cause harm to the existing systems. Although there has been considerable work on analysing Bitcoin in terms of the potential issues it brings to the economic landscape, this report performs a first ever attempt of identifying the threats and risks posed by the use of Bitcoin from the perspective of computational modeling and engineering. In this project we consider risk at all levels of interaction when Bitcoin is introduced and transferred across the systems. We look at the infrastructure and the computational working of the digital currency to identify the potential risks it brings. Additional information can be seen in our forthcoming companion report on the detailed modeling of Bitcoin.

The main purpose of this thesis is to analyze some common risk factors and to propose several useful analytical models for cost overrun risk analysis in transportation infrastructure investments. Probability models and regression models are proposed and partially (due to data insufficiency) applied using the VIHP (Vancouver Island Highway Project) data. The VIHP case study shows that cost overrun ratio increases as project sizes increase for small road/highway projects (budget < $0.25 million) and bridge/tunnel projects (budget >$0.85 million). However for road/highway projects with budgets over $0.25 million, cost overrun ratio decreases as project size increases. Using the VIHP database, results of a distribution fitting model and a Monte Carlo simulation model are compared. Compared with the distribution fitting model, the Monte Carlo simulation model is shown to underestimate both the upper bound value of project cost overrun ratio and slightly the probabilities of cost overrun. The distribution fitting model and regression model are shown to have close estimates of project costs and cost ranges at each confidence level.
Business, Sauder School of
Graduate