A comercialização da energia elétrica por parte de empresas geradoras que já possuem suas usinas construídas é um tema de grande relevância no setor. A dificuldade em definir a estratégia de venda desta energia, dentre as opções disponíveis no atual modelo regulatório, torna necessário o desenvolvimento de metodologias e ferramentas que auxiliem na tomada de decisão, contabilizando as expectativas e riscos destas decisões. A metodologia com ferramental associado apresentada neste trabalho foi desenvolvida com base em conceitos amplamente utilizados no mercado financeiro para a formação de portfólio de ativos, aliados ao conceito basilar de utilidade das geradoras de energia elétrica, permitindo quantificar a predisposição à tomada de risco e a estruturação de informações públicas dos agentes do setor para a definição de perfis de geradores em relação à estratégia de venda de energia. A contribuição central deste trabalho reside na proposta de uma metodologia que define a melhor estratégia de participação em leilões de energia existente para geradores. / The energy trading activity for Generation Companies (Gencos) owning operating existing power plants is a topic of great relevance in the industry. The difficulty to define the energy selling strategy, given all available options in the actual regulatory model, makes necessary the development of methodologies and tools for decision making support, accounting the expectations and risks embedded in these decisions. The methodology and associated computational tool presented in this work was developed based on concepts widely used in the financial market for assets portfolio composition, the Gencos utility, making feasible to quantify the predisposition of risktaking, and the structuring of public information from the players involved for the definition of generators profiles in relation to the strategy of selling energy. The main contribution of this work lies on the validation of one methodology that defines the Gencos best strategy participation in the auctions designed for trading the existing power plants energy production.

Extensive studies show that risk has a large impact on investment decisions. In addition, studies also show that risk behavior varies between men and women, where women tend to behave more risk averse. The purpose of this study is to examine how private investors make their investment decisions under risk but also to see if there are differences between men and women. The methods used in this research consists of a web survey that has been distributed via social media and interviews, to hear how private investors reason about investment decisions. The result from the web survey show that age and income does not affect investment decisions or their risk behavior. Neither does their own assessed willingness to take risk or knowledge in investments. The women in this research consider themselves to be more risk averse than men. However, when testing for their actual risk behavior, results show that women take more risk than men. Therefore, there are differences in investment decisions between women and men. / Tidigare forskning visar på att risk har en stor betydelse vid investeringsbeslut. Vissa studier menar även på att det föreligger en skillnad i kvinnor och mäns riskbenägenhet, där kvinnor anses vara mer riskaverta än män. Syftet med denna studie är att undersöka hur småsparare fattar sina investeringsbeslut under risk. Med hänsyn till tidigare studier har författarna även ett delsyfte att undersöka om och hur riskbenägenheten skiljer sig mellan kön. Studien har använt enkätundersökning samt intervjuer för att både ha tillgång till stora mängder data, men även för att fördjupa sig i det som enkäten inte kan besvara. Resultatet från enkätundersökningen visar att ålder och inkomst inte påverkar investeringsbeslut, samtidigt som intervjuerna säger det motsatta. Respondenternas självuppskattade riskbenägenhet och ansedda kunskap har heller ingen påverkan på deras investeringsbeslut. Kvinnorna i studien anger en lägre uppskattad riskbenägenhet än män samtidigt som de, i motsats till tidigare studier, agerar mer risksökande. Därmed föreligger det s

A comercialização da energia elétrica por parte de empresas geradoras que já possuem suas usinas construídas é um tema de grande relevância no setor. A dificuldade em definir a estratégia de venda desta energia, dentre as opções disponíveis no atual modelo regulatório, torna necessário o desenvolvimento de metodologias e ferramentas que auxiliem na tomada de decisão, contabilizando as expectativas e riscos destas decisões. A metodologia com ferramental associado apresentada neste trabalho foi desenvolvida com base em conceitos amplamente utilizados no mercado financeiro para a formação de portfólio de ativos, aliados ao conceito basilar de utilidade das geradoras de energia elétrica, permitindo quantificar a predisposição à tomada de risco e a estruturação de informações públicas dos agentes do setor para a definição de perfis de geradores em relação à estratégia de venda de energia. A contribuição central deste trabalho reside na proposta de uma metodologia que define a melhor estratégia de participação em leilões de energia existente para geradores. / The energy trading activity for Generation Companies (Gencos) owning operating existing power plants is a topic of great relevance in the industry. The difficulty to define the energy selling strategy, given all available options in the actual regulatory model, makes necessary the development of methodologies and tools for decision making support, accounting the expectations and risks embedded in these decisions. The methodology and associated computational tool presented in this work was developed based on concepts widely used in the financial market for assets portfolio composition, the Gencos utility, making feasible to quantify the predisposition of risktaking, and the structuring of public information from the players involved for the definition of generators profiles in relation to the strategy of selling energy. The main contribution of this work lies on the validation of one methodology that defines the Gencos best strategy participation in the auctions designed for trading the existing power plants energy production.

Traditional inventory models focus on risk-neutral decision makers, i.e., characterizing replenishment strategies that maximize expected total profit, or equivalently, minimize expected total cost over a planning horizon. In this paper, we propose a framework for incorporating risk aversion in multi-period inventory models as well as multi-period models that coordinate inventory and pricing strategies. In each case, we characterize the optimal policy for various measures of risk that have been commonly used in the finance literature. In particular, we show that the structure of the optimal policy for a decision maker with exponential utility functions is almost identical to the structure of the optimal risk-neutral inventory (and pricing) policies. Computational results demonstrate the importance of this approach not only to risk-averse decision makers, but also to risk-neutral decision makers with limited information on the demand distribution. / Singapore-MIT Alliance (SMA)

While the market for credit instruments grew continuously in the decade before 2008, its liquidity has dried up significantly in the current crisis, and investors have become aware of the possible consequences of being exposed to credit risk. In this thesis we address these issues by pricing credit instruments using utility indifference pricing, a method that takes into account the investor's personal risk aversion and which is not affected by the lack of liquidity. Through stochastic optimal control methods, we use indifference pricing with exponential utility to determine corporate bond prices and CDS spreads. In the first part we examine how these quantities are affected by risk aversion under different models of default. The emphasis lies on a hybrid model, in which a regime switch of the reference entity is triggered by a creditworthiness index correlated to its stock price. The second part generalizes this setup by introducing uncertainty in the model parameters. Robust optimal control has been used independently in the literature to address model uncertainty for portfolio selection problems. Here, we incorporate this approach with utility indifference and derive some analytical and numerical results on how model uncertainty affects credit spreads.

While the market for credit instruments grew continuously in the decade before 2008, its liquidity has dried up significantly in the current crisis, and investors have become aware of the possible consequences of being exposed to credit risk. In this thesis we address these issues by pricing credit instruments using utility indifference pricing, a method that takes into account the investor's personal risk aversion and which is not affected by the lack of liquidity. Through stochastic optimal control methods, we use indifference pricing with exponential utility to determine corporate bond prices and CDS spreads. In the first part we examine how these quantities are affected by risk aversion under different models of default. The emphasis lies on a hybrid model, in which a regime switch of the reference entity is triggered by a creditworthiness index correlated to its stock price. The second part generalizes this setup by introducing uncertainty in the model parameters. Robust optimal control has been used independently in the literature to address model uncertainty for portfolio selection problems. Here, we incorporate this approach with utility indifference and derive some analytical and numerical results on how model uncertainty affects credit spreads.

The role of public transportation increases as travel demand increases due to the growth in population and economics. The importance of providing a balanced public transportation has increased. In Ontario, Canada, the provincial government investing more than $17B in transit projects by the year of 2020 [28]. Consequently, planners and engineers motivated to pay more attention to mode split (mode choice) models used to estimate transit ridership. In most existing mode choice models, the likelihood of a trip maker using a transit mode (e.g. transit) is based on the generalized cost (GC) of using transit mode relative to the generalized cost of all other available modes. In conventional generalized cost formulations, transit costs are considered deterministic. It is quite evident, however, that great variability exists in the reliability of transit service and, as a result, the actual costs experienced by users. Efforts are ongoing to incorporate the costs of reliability in mode choice models by extending formulations to include penalties for arriving prior to or later than a desired arrival time. Transit operators strive to provide reliable service to retain and attract more users. Unreliable service can adversely affect the user by arriving late or early at their destination, waiting longer at their boarding station, and spending more time than expected in the transit vehicle. Unreliable service will also increase the user's anxiety associated with the uncertainty and discomfort. All these factors should be considered explicitly within the generalized cost (GC) function in order to accurately capture the GC of transit service relative to other modes and to ensure that these factors are not incorporated within the mode specific constant. In this study, a GC model is developed that explicitly represents service reliability. Service reliability is represented in the model as penalties associated with passengers' late arrival, early arrival, departure time shifting, waiting time, and anxiety. Furthermore, a methodology of utilizing field data to capture service reliability is defined. A Monte-Carlo simulation framework has been developed using the proposed GC function to quantify the impact of transit reliability on transit user cost. The proposed framework was applied on the iXpress service in the Regional of Waterloo in Ontario, Canada, utilizing Automated Vehicle Location (AVL) system data from the Regional Municipality of Waterloo to estimate service reliability. All the coefficients included in the proposed GC are assumed based on the relative importance of each penalty to scheduled in vehicle time by considering different passenger classes. In this research, the transit passengers are assumed to belong to one of three passenger classes based on their risk tolerance. From the results, it was found that increasing reliability of arrivals at a station can decrease transit users generalized costs significantly. We further posit that including uncertainty in the calculation of generalized costs may provide better estimates for mode split in travel forecasting models.

The role of public transportation increases as travel demand increases due to the growth in population and economics. The importance of providing a balanced public transportation has increased. In Ontario, Canada, the provincial government investing more than $17B in transit projects by the year of 2020 [28]. Consequently, planners and engineers motivated to pay more attention to mode split (mode choice) models used to estimate transit ridership. In most existing mode choice models, the likelihood of a trip maker using a transit mode (e.g. transit) is based on the generalized cost (GC) of using transit mode relative to the generalized cost of all other available modes. In conventional generalized cost formulations, transit costs are considered deterministic. It is quite evident, however, that great variability exists in the reliability of transit service and, as a result, the actual costs experienced by users. Efforts are ongoing to incorporate the costs of reliability in mode choice models by extending formulations to include penalties for arriving prior to or later than a desired arrival time. Transit operators strive to provide reliable service to retain and attract more users. Unreliable service can adversely affect the user by arriving late or early at their destination, waiting longer at their boarding station, and spending more time than expected in the transit vehicle. Unreliable service will also increase the user's anxiety associated with the uncertainty and discomfort. All these factors should be considered explicitly within the generalized cost (GC) function in order to accurately capture the GC of transit service relative to other modes and to ensure that these factors are not incorporated within the mode specific constant. In this study, a GC model is developed that explicitly represents service reliability. Service reliability is represented in the model as penalties associated with passengers' late arrival, early arrival, departure time shifting, waiting time, and anxiety. Furthermore, a methodology of utilizing field data to capture service reliability is defined. A Monte-Carlo simulation framework has been developed using the proposed GC function to quantify the impact of transit reliability on transit user cost. The proposed framework was applied on the iXpress service in the Regional of Waterloo in Ontario, Canada, utilizing Automated Vehicle Location (AVL) system data from the Regional Municipality of Waterloo to estimate service reliability. All the coefficients included in the proposed GC are assumed based on the relative importance of each penalty to scheduled in vehicle time by considering different passenger classes. In this research, the transit passengers are assumed to belong to one of three passenger classes based on their risk tolerance. From the results, it was found that increasing reliability of arrivals at a station can decrease transit users generalized costs significantly. We further posit that including uncertainty in the calculation of generalized costs may provide better estimates for mode split in travel forecasting models.

Studies in newsvendor decision-making have shown that human decisions systematically deviate from analytical solutions found in many utility models of the single period problem (SPP). Yet for the most part the impacts of this human decision bias in systems of newsvendor type products have not been investigated. We study bias in human decision-making to determine how different factors affect the performance of systems of newsvendor type products. We extended the state of the arts utility models of SPP to analyze the effects of individuals wealth on individual decision-making. Our theoretical and empirical results proved that individuals wealth significantly affected individual decision-making. Specifically, our analysis concluded that wealthier individual ordered more than poorer individual did when presented with the same investment opportunity. We created a human decision bias (HDB) model to include different newsvendor ordering policies that individuals could use to determine their order quantities. This model is set up to investigate individuals reliance on different ordering policies under different experimental conditions. We designed multi period newsvendor experiments to study effects of factors such as item profit margin, wealth, value of learning, and salvage value on decision-maker's order quantity. We found that wealth and profit margin factors significantly affected individual newsvendor decision-making. Learning, gender, and salvage value factor did not exhibit significant effects in our empirical studies. We designed multi period multi echelon newsvendor experiments to study effects of factors such as the relationship between newsvendors, item profit margin, and newsvendors' wealth on the performance of two-echelon newsvendors system. We found item profit margin, wealth, and relationship between supplier and retailer to significantly affect newsvendor decision-making. Finally, we present a case study of US fresh produce industry to illustrate the impacts of human decision bias on the performance of a supply chain system.

The major internet auction sites eBay and Yahoo have developed innovative hybrid auction designs that incorporate buy prices. My dissertation focuses on the Buy It Now (BIN, hereafter) version of the auctions on eBay, the largest online auction site. The BIN hybrid auction combines a standard ascending bid auction with a posted-price offer. A seller in a BIN auction lists his auction with a "buy price". A bidder may purchase the item immediately at the buy price and end the auction. If he places a bid instead, the option to purchase the item at the buy price disappears and the subsequent bidders participate in the standard eBay auction. This auction format has been very popular with both buyers and sellers. In 2005 eBay's sales in fixed price platform (BIN and Half.com) totaled $13.8 billion, which was 33.1% of eBay's total sales.The dissertation explores the BIN auctions using theory, experiments and field data. Chapter 1 theoretically analyzes BIN auctions within the common values framework. An equilibrium is characterized, shown to exist, and the revenues generated by BIN and standard eBay auctions are compared. Chapter 2 compares the bidding behavior and the revenue implications of BIN auctions in lab experiments under common and private value assumptions. The third develops an "incomplete" theoretical model of BIN auctions within the private values framework. An "incomplete" empirical specification is derived and then field data collected from eBay's BIN auctions are used to estimate the primitives of the model, including the bidders' risk aversion and time preference. I then explore how heterogeneity of sellers and items influence these primitives. Chapter 1 (Common Values Auctions with a Buy Price: the case of eBay): Several explanations for the popularity of buy price have been provided for independent private value auctions. Risk aversion and impatience of either the bidders or the seller have mainly been used to explain the popularity of buy prices in IPV models. This paper, using a pure common value framework, models auctions with eBay-style "temporary" buy prices, when the bidders and the seller are either risk neutral or risk averse. It characterizes equilibrium bidding strategies in a general setup and then analyzes a seller's incentive to post a buy price when there are two bidders. When bidders are either risk neutral or risk averse there is no incentive to post a buy price for a risk neutral seller. But when the seller is risk averse, a suitably chosen buy price can raise the seller's expected utility when the bidders are either risk neutral or risk averse. Chapter 2 (An Experimental Study of Auctions with a Buy Price Under Private and Common Values): We use experiments to examine several predictions from the theoretical studies of buy prices. The theoretical predictions from Wooders and Reynolds (2003) and Chapter 1 show that the introduction of a buy price causes the seller's revenue to move in opposite directions in private value and common value settings. Meanwhile, Mathews and Katzman (2006) find that risk averse sellers might find buy prices advantageous because they reduce the variance in seller revenue in eBay auctions with risk-neutral bidders. The lab experiments are used to answer three key questions. (a) Can a buy price raise seller revenue and lower the variance of seller revenue in an independent private value auction? (b) Does a buy price lower seller revenue in common value auctions? (c) If the theoretical predictions do not hold, can a behavioral model explain the patterns observed in the data? Using a between-subjects design the results show that the use of a buy price has a positive and statistically significant effect on seller revenue in private value auctions. The buyers are risk averse. The estimate of the Constant Absolute Risk Aversion (CARA) index of 1.11 for the bidders is equivalent to a Constant Relative Risk Aversion (CRRA) index of 0.62 which is centered within the range of other estimates of relative risk aversion. As predicted by the theory when buyers are risk averse, the use of a buy price yields a statistically significant reduction in the variance of seller revenue. Hence, as predicted, the use of a buy price is advantageous to the seller when either the bidders or the seller are risk averse. The results for common value auctions are inconsistent with the theoretical predictions. Use of a buy price did not lower seller revenue, and the bidders' behavior departed from theoretical predictions in several respects. As a result, we develop and estimate a behavioral model of common value BIN auctions based on the winner's curse and overweighting of a bidder's private information. We find statistically significant evidence of overweighting of the bidder's own signal and estimated a CARA index of 0.001. This behavioral model explains all the departures from the rational model we found in the common values experiments. Chapter 3 (The Buy-it-now Option, Risk Aversion, and Impatience in an Empirical Model of eBay Bidding): Haile and Tamer (2003) first used an incomplete econometric model in an auction context, assuming that bidders bid up to their values and do not allow an opponent to win at a price they are willing to beat. Canals-Cerda and Pearcy (2004) used a similar incomplete econometric model to study eBay auctions while adding the assumption that the maximum of all the bids placed by the bidder with the second highest value is exactly equal to his value. Chapter 3 extends these incomplete models to eBay's BIN auctions. We develop and estimate an equilibrium model for BIN independent private value auctions with a stochastic and unknown number of potential bidders who enter the auction sequentially. In the model risk averse and time impatient bidders buy at the BIN price because it allows them to avoid the uncertainties and delay of the ascending bid auction that takes place if no one chooses the BIN option. As a result, the bidders' decisions to choose the BIN option in BIN auctions of different lengths can be used to identify the bidders' risk aversion and time preference parameters. Our model is "incomplete" in the sense that we do not impose any stylized structure on bidding in the ascending bid auction and, although bid revision is allowed, the process is not explicitly described. Our "incomplete" econometric model uses a partial likelihood approach proposed by Cox (1975) that allows the analysis to bypass modeling bidding and the bid revision process. The model is estimated using a new data set of 3245 eBay auctions of Pentium-3 laptops that ran between 22 July to 10 August 2005.