This thesis analyzes the mean variance optimization problem with respect to cardinalityconstraints. The aim of this thesis is to figure out how much of an impact transactionchanges has on the profit and risk of a portfolio. We solve the problem by implementingmixed integer programming (MIP) and solving the problem by using the Gurobi solver.In doing this, we create a mathematical model that enforces the amount of transactionchanges from the initial portfolio. Our results is later showed in an Efficient Frontier,to see how the profit and risk are changing depending on the transaction changes.Overall, this thesis demonstrates that the application of MIP is an effective approachto solve the mean variance optimization problem and can lead to improved investmentoutcomes.

The All-Electric Vehicle (AEV) industry development has intensified and is connected to governmentefforts to minimize greenhouse gas emissions and encourage people to buy electric vehicles. This hasled to all the lights turning on newly established all-electric vehicle makers and some older players. Thegrowth of these companies is depicted in their market capitalization, which has seen an unprecedentedrun. However, one can notice a knowledge gap in the analysis of factors affecting such companies'expected rate of return. This research focuses on analyzing the factors from three of the most knownasset pricing models - CAPM, Fama-French 3 Factor, and Fama-French 5 Factor models. It shows whichof these factors are significant in estimating the expected return rate for nine chosen companies and theimpact of each considerable factor on the return rate.Additionally, we calculate the expected return rate using the beforementioned models to verify whetherthere is an uptrend or not in the electric vehicle market. The current research is limited to companieslisted on the US stock market, with only all-electric vehicle production lines. We make an introductionto the AEV theoretical aspects and related market structure. We also present theoretical concepts behindthe expected rate of return perception.The analysis showed that the market risk premium impacts 100% of the companies. The SMB factorinfluences 55% of the companies while the HML factor only 11%. Finally, RMW affects 66% of thechosen dataset and CMA 77%. For all companies, there is a positive expected return rate. Looking atthe significant coefficients for each model, the results are the following: we can observe that for CAPMand all the companies, 100% of the coefficients are positive. For FF3FM, 93% of the significant factorsare positive, while only 7% are negative. Finally, for FF5FM, out of the 28 significant factors, 65% ofthe coefficients are positive, and 35% are negative.

A systematic customer value assessment process (CVAP) was developed to give system engineering teams the capability to qualitatively and quantitatively assess customer values. It also provides processes and techniques used to create and identify alternatives, evaluate alternatives in terms of effectiveness, cost, and risk. The ultimate goal is to provide customers (or decision makers) with objective and traceable procurement recommendations. The creation of CVAP was driven by an industry need to provide ballistic missile defense (BMD) customers with a value proposition of contractors’ BMD systems. The information that outputs from CVAP can be used to guide BMD contractors in formulating a value proposition, which is used to steer customers to procure their BMD system(s) instead of competing system(s). The outputs from CVAP also illuminate areas where systems can be improved to stay relevant with customer values by identifying capability gaps. CVAP incorporates proven approaches and techniques appropriate for military applications. However, CVAP is adaptable and may be applied to business, engineering, and even personal every-day decision problems and opportunities. CVAP is based on the systems decision process (SDP) developed by Gregory S. Parnell and other systems engineering faculty at the Unites States Military Academy (USMA). SDP combines Value-Focused Thinking (VFT) decision analysis philosophy with Multi-Objective Decision Analysis (MODA) quantitative analysis of alternatives. CVAP improves SDP’s qualitative value model by implementing Quality Function Deployment (QFD), solution design implements creative problem solving techniques, and the qualitative value model by adding cost analysis and risk assessment processes practiced by the U.S DoD and industry. CVAP and SDP fundamentally differ from other decision making approaches, like the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), by distinctly separating the value/utility function assessment process with the ranking of alternatives. This explicit value assessment allows for straightforward traceability of the specific factors that influence decisions, which illuminates the tradeoffs involved in making decisions with multiple objectives. CVAP is intended to be a decision support tool with the ultimate purpose of helping decision makers attain the best solution and understanding the differences between the alternatives. CVAP does not include any processes for implementation of the alternative that the customer selects. CVAP is applied to ballistic missile defense (BMD) to give contractors ideas on how to use it. An introduction of BMD, unique BMD challenges, and how CVAP can improve the BMD decision making process is presented. Each phase of CVAP is applied to the BMD decision environment. CVAP is applied to a fictitious BMD example.

The number of statistical and mathematical credit risk models that financial institutions use and manage due to international and domestic regulatory pressures in recent years has steadily increased. This thesis examines the evolution of model risk management and provides some guidance on how to effectively build and manage different bagging and boosting machine learning techniques for estimating expected credit losses. It examines the pros and cons of these machine learning models and benchmarks them against more conventional models used in practice. It also examines methods for improving their interpretability in order to gain comfort and acceptance from auditors and regulators. To the best of this author’s knowledge, there are no academic publications which review, compare, and provide effective model risk management guidance on these machine learning techniques with the purpose of estimating expected credit losses. This thesis is intended for academics, practitioners, auditors, and regulators working in the model risk management and expected credit loss forecasting space. / Dissertation / Doctor of Philosophy (PhD)

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No description available.

The North European power system (Sweden, Finland, Norway, Denmark, Estonia, Latvia and Lithuania) is facing changes in its electricity production. The increasing share of intermittent power sources, such as wind power, makes the production less predictable. The decommissioning of large plants, for environmental or market reasons, leads to a decrease of production capacity while the demand can increase, which is detrimental to the power system reliability. Investments in interconnections and new power plants can be made to strengthen the system. Evaluating the reliability becomes essential to determine the investments that have to be made. For this purpose, a model of the power system is built. The power system is divided into areas, where the demand, interconnections between areas, and intermittent generation are represented by Cumulative Distribution Functions (CDF); while conventional generation plants follow a two-state behaviour. Imports from outside the system are set equal to their installed capacity, with considering that the neighbouring countries can always provide enough power. The model is set up by using only publicly available data. The model is used for generating numerous possible states of the system in a Monte Carlo simulation, to estimate two reliability indices: the risk (LOLP) and the size (EPNS) of a power deficit. As a power deficit is a rare event, an excessively large number of samples is required to estimate the reliability of the system with a sufficient confidence level. Hence, a pre-simulation, called importance sampling, is run beforehand in order to improve the efficiency of the simulation. Four simulations are run on the colder months (January, February, March, November, December) to test the reliability of the current system (2015) and of three future scenarios (2020, 2025 and 2030). The tests point out that the current weakest areas (Finland and Southern Sweden) are also the ones that will face nuclear decommissioning in years to come, and highlight that the investments in interconnections and wind power considered in the scenarios are not sufficient to maintain the current reliability levels. If today’s reliability levels are considered necessary, then possible solutions include more flexible demand, higher production and/or more interconnections. / Det nordeuropeiska elsystemet (Sverige, Finland, Norge, Danmark, Estland, Lettland och Litauen) står inför förändringar i sin elproduktion. Den ökande andelen intermittenta kraftkällor, såsom vindkraft, gör produktionen mindre förutsägbar. Avvecklingen av stora anläggningar, av miljö- eller marknadsskäl, leder till en minskning av produktionskapaciteten, medan efterfrågan kan öka, vilket är till nackdel för kraftsystemets tillförlitlighet. Investeringar i sammankopplingar och i nya kraftverk kan göras för att stärka systemet. Utvärdering av tillförlitligheten blir nödvändigt för att bestämma vilka investeringar som behövs. För detta ändamål byggs en modell av kraftsystemet. Kraftsystemet är uppdelat i områden, där efterfrågan, sammankopplingar mellan områden, och intermittent produktion representeras av fördelningsfunktioner; medan konventionella kraftverk antas ha ett två-tillståndsbeteende. Import från länder utanför systemet antas lika med deras installerade kapaciteter, med tanke på att grannländerna alltid kan ge tillräckligt med ström. Modellen bygger på allmänt tillgängliga uppgifter. Modellen används för att generera ett stort antal möjliga tillstånd av systemet i en Monte Carlo-simulering för att uppskatta två tillförlitlighetsindex: risken (LOLP) och storleken (EPNS) av en effektbrist. Eftersom effektbrist är en sällsynt händelse, krävs ett mycket stort antal tester av olika tillstånd i systemet för att uppskatta tillförlitligheten med en tillräcklig konfidensnivå. Därför utnyttjas en för-simulering, kallad ”Importance Sampling”, vilken körs i förväg i syfte att förbättra effektiviteten i simuleringen. Fyra simuleringar körs för de kallare månaderna (januari, februari, mars, november, december) för att testa tillförlitligheten i nuvarande systemet (2015) samt för tre framtidsscenarier (2020, 2025 och 2030). Testerna visar att de nuvarande svagaste områdena (Finland och södra Sverige) också är de som kommer att ställas inför en kärnkraftsavveckling under de kommande åren. De indikerar även att planerade investeringar i sammankopplingar och vindkraft i scenarierna inte är tillräckliga för att bibehålla de nuvarande tillförlitlighetsnivåerna. Om dagens tillförlitlighetsnivåer antas nödvändiga, så inkluderar möjliga lösningar mer flexibel efterfrågan, ökad produktion och/eller fler sammankopplingar.

In traditional investment decision making, one tool commonly used is the mean variance model, also known as an expected-value variance (EV) model, which evaluates the anticipated payout of different assets with respect to uncertainty where portfolios with higher risk demand higher expected returns from an individual. This thesis adapts this framework to a cost setting where decision makers are evaluating alternative physical assets that carry lifetime cost uncertainty for maintenance. Specifically, this paper examines homeowner choices for their home plumbing systems in the event of a pinhole leak, a tiny pin-sized hole that forms in copper, drinking-water pipes. These leaks can cause substantial damage and cost homeowners thousands of dollars in repairs. Since pinhole leaks are not related to the age of pipe material, a homeowner is subject to the risk of additional costs if a pinhole leak occurs again despite their repair efforts. The EV cost model in this paper defines two discrete choices for the homeowner in the event of a leak; to apply a simple repair at lower cost and higher future cost uncertainty, or to replace their plumbing with new pipe material, usually made of plastic, at a higher upfront cost but lower likelihood of future expenses. The risk preference of homeowners are demonstrated by their repair strategy selection, as well as the level of cost they incur to reduce uncertainty. Risk neutral individuals will select the repair strategy with the lowest lifetime expected cost and high variance, while risk averse homeowners will prefer to replace their plumbing with higher cost but lower variance. Risk averse individuals are also exposed to indirect costs, which is an additional unobserved cost in the form of a risk premium the homeowner is willing to pay to remove all uncertainty of future pinhole leak expense. Expected costs and variances are also higher for regions in the U.S. that experience elevated leak incident rates, known as hotspots. Using this mean variance cost framework, indirect cost can be quantified for homeowners in hotspot regions and compared to the rest of the U.S. to evaluate the magnitude of pinhole leak risk. The EV cost model estimates risk premiums on pinhole leaks to be $442 for homeowners in hotspots and $305 for those in the rest of the U.S. Finally, this paper examines the impact of pinhole leak cost uncertainty on the U.S. economy. Of an estimated $692 million in annual pinhole leak costs to homeowners, this study estimates a lower bound cost of $54 million per year (7.8% of estimated national annual cost) in risk premium that homeowners would be willing to pay to avoid pinhole leak cost uncertainty. Information in this study on the role of risk in home plumbing decisions and indirect costs would be helpful to policymakers and water utility managers as they deal with infrastructure management decisions. Furthermore, the EV cost methodology established in this paper demonstrates an effective use of mean variance modeling under cost uncertainty. / Master of Science / This paper examines homeowner choices for their home plumbing systems in the event of a pinhole leak, a tiny pin-sized hole that forms in copper, drinking-water pipes. These leaks can cause substantial damage and cost homeowners thousands of dollars in repairs. Since pinhole leaks are not related to the age of pipe material, a homeowner is subject to the risk of additional costs if a pinhole leak occurs again despite their repair efforts. This paper also examined costs in regions of the U.S. that experience elevated leak incident rates, known as hotspots. There were two primary choices assessed in this study for homeowners facing pinhole leaks: to either apply a simple repair today at lower cost but take on a higher chance of more pinhole leaks; or to replace their plumbing with new pipe material, usually made of plastic, at a higher overall cost but lower risk of another leak. Using a cost focused investment analysis, it was estimated that homeowners selecting the ‘safer’ replacement strategy would be willing to pay a minimum of $305 in additional cost if able to eliminate all possibility of another leak compared to those who opted for the more ‘riskier’ repair choice. Additionally, homeowners who live in hotspot regions who selected the replacement strategy were estimated to be willing to pay a minimum of $442 in additional cost to avoid pinhole leaks. At a national level, these pinhole leak-avoiding premiums equate to $54 million, about 7.8% of the estimated $692 million in costs spent on fixing pinhole leaks by U.S. homeowners each year. Information in this study on homeowner preferences and pinhole leak would be helpful to policymakers and water utility managers as they deal with infrastructure management decisions.

Multiobjective Stochastic Linear Programming is a relevant topic. As a matter of fact, many real life problems ranging from portfolio selection to water resource management may be cast into this framework. There are severe limitations in objectivity in this field due to the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice does not hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this thesis, we resort to the bounded rationality and chance-constrained principles to define satisficing solutions for Multiobjective Stochastic Linear Programming problems. These solutions are then characterized for the cases of normal, exponential, chi-squared and gamma distributions. Ways for singling out such solutions are discussed and numerical examples provided for the sake of illustration. Extension to the case of fuzzy random coefficients is also carried out. / Decision Sciences

Quantifier et mesurer le risque dans un environnement partiellement ou totalement incertain est probablement l'un des enjeux majeurs de la recherche appliquée en mathématiques financières. Cela concerne l'économie, la finance, mais d'autres domaines comme la santé via les assurances par exemple. L'une des difficultés fondamentales de ce processus de gestion des risques est de modéliser les actifs sous-jacents, puis d'approcher le risque à partir des observations ou des simulations. Comme dans ce domaine, l'aléa ou l'incertitude joue un rôle fondamental dans l'évolution des actifs, le recours aux processus stochastiques et aux méthodes statistiques devient crucial. Dans la pratique l'approche paramétrique est largement utilisée. Elle consiste à choisir le modèle dans une famille paramétrique, de quantifier le risque en fonction des paramètres, et d'estimer le risque en remplaçant les paramètres par leurs estimations. Cette approche présente un risque majeur, celui de mal spécifier le modèle, et donc de sous-estimer ou sur-estimer le risque. Partant de ce constat et dans une perspective de minimiser le risque de modèle, nous avons choisi d'aborder la question de la quantification du risque avec une approche non-paramétrique qui s'applique à des modèles aussi généraux que possible. Nous nous sommes concentrés sur deux mesures de risque largement utilisées dans la pratique et qui sont parfois imposées par les réglementations nationales ou internationales. Il s'agit de la Value at Risk (VaR) qui quantifie le niveau de perte maximum avec un niveau de confiance élevé (95% ou 99%). La seconde mesure est l'Expected Shortfall (ES) qui nous renseigne sur la perte moyenne au delà de la VaR. / To quantify and measure the risk in an environment partially or completely uncertain is probably one of the major issues of the applied research in financial mathematics. That relates to the economy, finance, but many other fields like health via the insurances for example. One of the fundamental difficulties of this process of management of risks is to model the under lying credits, then approach the risk from observations or simulations. As in this field, the risk or uncertainty plays a fundamental role in the evolution of the credits; the recourse to the stochastic processes and with the statistical methods becomes crucial. In practice the parametric approach is largely used.It consists in choosing the model in a parametric family, to quantify the risk according to the parameters, and to estimate its risk by replacing the parameters by their estimates. This approach presents a main risk, that badly to specify the model, and thus to underestimate or over-estimate the risk. Based within and with a view to minimizing the risk model, we choose to tackle the question of the quantification of the risk with a nonparametric approach which applies to models as general as possible. We concentrate to two measures of risk largely used in practice and which are sometimes imposed by the national or international regulations. They are the Value at Risk (VaR) which quantifies the maximum level of loss with a high degree of confidence (95% or 99%). The second measure is the Expected Shortfall (ES) which informs about the average loss beyond the VaR.