過去數年來，隨著金融市場的逐漸開放，帶動了整個金融市場的蓬勃發展。不過，隨之而來的風險，也產生了不少的金融災難。而這些的金融災難，大多是由於金融商品投資避險上的操作不當，或者是風險控管失衡所造成的。為了避免如是情況再度發生，近年來，國際間相繼有許多學者投入風險管理的研究。 而在所有的產業之中，風險管理對於銀行業來說，更為重要。銀行作為產業的金融媒介，一旦發生金融災難，不只產業會受到衝擊，連帶地資金來源的存款戶也受害，影響層面極為廣大。所以，各國政府莫不對銀行業設有相當嚴格的管理規定，以健全整體金融環境的發展。例如，有名的巴塞爾資本協定，即為國際間對於金融環境的風險管理規範。 但是，隨著時空環境的變遷，原有的協定早以不敷需求。終於，在2004年中，巴塞爾銀行監理委員會公佈了定版的新巴塞爾資本協定，並決定於2006年底開始實施。新協定在原有資本準備方面，將作業風險納入風險評估的範圍，並大幅修訂信用風險的衡量方式，允許銀行使用自行開發的內部模型，並採認降低信用風險的工具。而且，更增加了監理審查程序及市場紀律的相關規定，期待以多方面的角度，強化國際金融體系。 本研究將由新巴塞爾資本協定談起，簡介新協定的相關內容，比較新舊協定不同之處，然後針對銀行主要面臨的信用風險部分，探討在新協定所允許使用的信用風險內部模型，以及信用風險抵減技術。分別介紹目前業界常見的四種信用風險模型：專業信用分析公司KMV的KMV模型、CSFP的CreditRisk+模型、J.P. Morgan的CreditMetrics™模型、McKinsey的CreditPortfolioView模型，以及信用衍生性商品與信用風險證券化概念，最後探討未來風險管理發展的可能方向。

Much of the confusion about the effectiveness of information security programs concerns not only how to measure, but also what to measure — an issue of equivocality. Thus, to lower uncertainty for improved decision-making, it is first essential to reduce equivocality by defining, expanding, and clarifying risk factors so that metrics, the "necessary measures," can be unambiguously applied. We formulate a system that (1) allows threats to be accurately measured and tracked, (2) enables the impacts and costs of successful threats to be determined, and (3) aids in evaluating the effectiveness and return on investment of countermeasures. We then examine the quality of controls implemented to mitigate cyber risk and study how effectively they reduce the likelihood of security incidents. Improved control quality was shown to reduce the likelihood of security incidents, yet the results indicate that investing in maximum quality is not necessarily the most efficient use of resources. The next manuscript expands the discussion of cyber risk management beyond single organizations by surveying perceptions and experiences of risk factors related to 3rd parties. To validate and these findings, we undertake in an in-depth investigation of nearly 1000 real-world data breaches occurring over a ten-year period. It provides a robust data model and rich database required by a decision support system for cyber risk in the extended enterprise. To our knowledge, it is the most comprehensive field study ever conducted on the subject. Finally, we incorporate these insights, data, and factors into a simulation model that enables us study the transfer of cyber risk across different supply chain configurations and draw important managerial implications. / Ph. D.

The modern supply chain is long, complex, interconnected and global, and plays a fundamental role in business competitiveness. These conditions, along with various supply chain management trends in recent years have increased risks in supply chains which threaten supply chain performance. Greater impact, especially on cost, from an increased threat of supply disruptions is one area of particular concern. Companies today are struggling to find effective means to manage this increased risk and avoid adverse financial impacts. An approach to managing supply disruption risk in supply chains based on the minimization of the total cost of ownership (TCO) of the supply chain is explored in this thesis. Insights are provided into an appropriate view of supply chain risk and a general four step risk management process to guide the design and evaluation of a new risk management tool based on such an approach. A prototype of the new total cost-based, modeling and simulation tool was created in partnership with ProModel Corporation and a government contractor that requested to remain anonymous. A preliminary assessment of the effectiveness of this tool in minimizing TCO and providing an interface useable by non-modelers is provided. This study also reviews and compares a sample set of current supply chain risk management methods and tools and compares them with the new tool for relevance in aiding users in managing supply disruption risk. Based on literature findings and preliminary feedback from pilot contextual demonstrations of the tool, the total cost approach to risk modeling appears promising, although the execution needs to be improved with further enhancements made to the prototype tool. In this preliminary study and evaluation, sufficient evidence is not available to determine that the new prototype tool is any more effective than other currently available risk management tools to provide necessary information to make supply chain risk management decisions that minimize TCO of a supply chain. Suggestions for further development of the tool, especially for improvement of the total cost approach, are provided as well as a preliminary evaluation procedure and survey instruments for a more robust evaluation of the new tool.

Reservoir economic performance is based upon future cash flows which can be generated from a reservoir. Future cash flows are a function of hydrocarbon volumetric flow rates which a reservoir can produce, and the market conditions. Both of these functions of future cash flows are associated with uncertainties. There is uncertainty associated in estimates of future hydrocarbon flow rates due to uncertainty in geological model, limited availability and type of data, and the complexities involved in the reservoir modeling process. The second source of uncertainty associated with future cash flows come from changing oil prices, rate of return etc., which are all functions of market dynamics. Robust integration of these two sources of uncertainty, i.e. future hydrocarbon flow rates and market dynamics, in a model to predict cash flows from a reservoir is an essential part of risk assessment, but a difficult task. Current practices to assess a reservoir’s economic performance by using Deterministic Cash Flow (DCF) methods have been unsuccessful in their predictions because of lack in parametric capability to robustly and completely incorporate these both types of uncertainties. This thesis presents a procedure which accounts for uncertainty in hydrocarbon production forecasts due to incomplete geologic information, and a novel real options methodology to assess the project economics for upstream petroleum industry. The modeling approach entails determining future hydrocarbon production rates due to incomplete geologic information with and without secondary information. The price of hydrocarbons is modeled separately, and the costs to produce them are determined based on market dynamics. A real options methodology is used to assess the effective cash flows from the reservoir, and hence, to determine the project economics. This methodology associates realistic probabilities, which are quantified using the method’s parameters, with benefits and costs. The results from this methodology are compared against the results from DCF methodology to examine if the real options methodology can identify some hidden potential of a reservoir’s performance which DCF might not be able to uncover. This methodology is then applied to various case studies and strategies for planning and decision making. / text

The demand for sustainable investments has seen an increase in recent years. There is considerable literature covering backtesting of the performance and risk of socially responsible investments (SRI) compared to conventional investments. However, literature that models and examines the risk characteristics of SRI compared to conventional investments is limited. This thesis seeks to model and compare the risk of mutual funds scoring in the top 10% in terms of sustainability, based on Morningstar Portfolio Sustainability Score, to those scoring in the bottom 10%. We create one portfolio consisting of the top 10% funds and one portfolio consisting of the bottom 10%, for European and global mutual funds separately, thus in total creating 4 portfolios. The analysis is based on data of the funds' returns and Morningstar Portfolio Sustainability Scores during December 2015 to August 2019. Investigating several GARCH models, we find an ARMA-GARCH model with skewed Student's t-distribution as innovation distribution to give the best fit to the daily log-returns of each portfolio. Based on the fitted ARMA-GARCH models with skewed Student's t-distribution, we use a parametric bootstrap method to compute 95% confidence intervals for the difference in long-run volatility and value at risk (VaR) between the portfolios with high and low Morningstar Portfolio Sustainability Scores. This is performed on the portfolios of European and global funds separately. We conclude that, for global and European funds respectively, no significant difference in terms of long-run volatility and VaR is found between the funds in each of the 10% ends of the Morningstar Portfolio Sustainability Score. / Efterfrågan av hållbara investeringar har ökat kraftigt de senaste åren. Det finns många studier som genomför backtesting av hållbara investeringars avkastning och risk jämfört med konventionella investeringar. Färre studier har däremot gjorts för att modellera och jämföra investeringarnas riskegenskaper. Denna uppsats syftar till att modellera risken av hållbara investeringar genom att jämföra de 10% fonder med högst Morningstar Portfolio Sustainability Score mot de 10% fonder med lägst score. Jämförelsen görs separat för globala fonder och europeiska fonder, vilket resulterar i totalt 4 portföljer. Analysen baseras på data på fondernas avkasting och Morningstar Portfolio Sustainability Score under tidsperioden december 2015 till augusti 2019. Genom att undersöka flera olika GARCH-modeller, kommer vi fram till att en ARMA-GARCH-modell med skev t-fördelning bäst beskriver den dagliga logaritmerade avkastningen för varje portfölj. Baserat på de anpassade ARMA-GARCH-modellerna, används en "parametric bootstrap"-metod för att beräkna 95%-iga konfidensintervall för skillnaden i långsiktig volatilitet och value at risk (VaR) mellan portföljerna med högt och lågt Morningstar Portfolio Sustainability Score. Detta görs separat för de europeiska och globala fonderna. Vår slutsats är att det, för globala och europeiska fonder, inte råder en signifikant skillnad i långsiktig volatilitet eller VaR mellan fonder med högt och lågt Morningstar Portfolio Sustainability Score.

The fundamental purpose of drinking water distribution systems is to provide safe drinking water at sufficient volumes and optimal pressure with the lowest lifecycle costs from the source (treatment plants, raw water source) to the customers (residences, industries). Most of the distribution systems in the US were laid out during the development phase after World War II. As the drinking water infrastructure is aging, water utilities are battling the increasing break rates in their water distribution system and struggling to bear the associated economic costs. However, with the growth in sensory technologies and data science, water utilities are seeing economic value in collecting data and analyzing it to monitor and predict the performance of their distribution systems. Many mathematical models have been developed to guide repair and rehabilitation decisions in the past but remain largely unused because of low reliability. This is because any effort to build a decision support framework based on a model should rest its foundations on a robust knowledge base of the critical factors influencing the system, which varies from utility to utility. Mathematical models built on a strong understanding of the theory, current practices and the trends in data can prove to be more reliable. This study presents a framework to support repair and rehabilitation decisions for water utilities using water pipeline field performance data. / Master of Science / The fundamental purpose of drinking water distribution systems is to provide a safe and sufficient volume of drinking water at optimal pressure with the lowest costs to the water utilities. Most of the distribution systems in the US were established during the development phase after World War II. The problem of aging drinking water infrastructure is an increasing financial burden on water utilities due to increasing water main breaks. The growth in data collection by water utilities has proven to be a useful tool to monitor and predict the performance of the water distribution systems and support asset management decisions. However, the mathematical models developed in the past suffer from low reliability due to limited data used to create models. Also, any effort to build sophisticated mathematical models should be supported with a comprehensive review of the existing recommendations from research and current practices. This study presents a framework to support repair and rehabilitation decisions for water utilities using water pipeline field performance data.