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Submitted by Mariana Dornelles Vargas (marianadv) on 2015-03-19T13:35:22Z No. of bitstreams: 1 classificacao_risco.pdf: 1102040 bytes, checksum: 33359db86cf7431ae0f650e9d54aa9d8 (MD5) / Made available in DSpace on 2015-03-19T13:35:22Z (GMT). No. of bitstreams: 1 classificacao_risco.pdf: 1102040 bytes, checksum: 33359db86cf7431ae0f650e9d54aa9d8 (MD5) Previous issue date: 2011-01-11 / Nenhuma / O risco de crédito empresarial é o risco ao qual a instituição credora está exposta caso alguma de suas contrapartes venha a falhar no cumprimento de suas obrigações contratuais de crédito. No mundo acadêmico, duas metodologias têm sido utilizadas para estimar o risco de crédito das empresas: os modelos estruturais e os modelos não estruturais. Esta pesquisa contempla o modelo estrutural KMV e os modelos não estruturais de Kanitz (1976), Altman, Baidya e Dias (1979), Minussi (2008) e Brito e Assaf Neto (2008), com o objetivo de verificar o nível de convergência entre os resultados estimados pelas referidas metodologias e comparar as classificações obtidas pelas mesmas com os ratings concedidos pelas agências de rating Moody?s e Standard & Poor?s. O estudo, realizado por meio de uma pesquisa explicativa, abrange o período de 2006 a 2009. Estimaram-se regressões lineares simples e múltiplas, a fim de verificar a convergência entre os modelos e realizou-se uma análise comparativa entre as classificações estimadas pelos modelos e os ratings das referidas agências. Concluiu-se que os resultados estimados pelo modelo não estrutural de Altman, Baidya e Dias (1979) são convergentes com os resultados obtidos pelo modelo estrutural KMV. Os resultados estimados pelo modelo de Brito e Assaf Neto (2008) apresentaram-se convergentes com o modelo KMV, quando analisados conjuntamente com os resultados dos demais modelos. Os resultados obtidos pelos modelos de Kanitz (1976) e Minussi (2008) não apresentaram convergência. No que tange à comparação entre os resultados estimados pelos modelos e os ratings das agências, o modelo estrutural KMV e os modelos não estruturais de Minussi (2008) e Altman, Baidya e Dias (1979) destacaram-se por terem os resultados mais semelhantes. / The corporate credit risk is the risk which the lending institution is exposed if some of its counterparties fail in fulfilling the contractual obligations for credit. For academics, a couple of methodologies have been used to estimate the credit risk of companies: the structural and the non structural models. This research focuses the KMV structural model and the non structural models from Kanitz (1976), Altman, Baidya and Dias (1979), Minussi (2008) and Brito & Assaf Neto (2008), in order to verifying the level of convergence between the results estimated through these methodologies and compare the classification obtained for them with the rating given by the Moody?s and Standart & Poor?s rating agencies. The study was developed through an explanatory research within the period of 2006 to 2009. Simple and multiples linear regressions were estimated in order to verify the convergence between the models and a comparative analysis between the classifications obtained through the models and the ratings from those agencies. It was concluded that the results estimated through non structural model of Altman, Baidya and Dias (1979) are convergent with the results obtained through the KMV structural model. The results estimated through the Brito and Assaf Neto (2008) model were convergent with the KMV model when analyzed together with the results from another models. The results obtained through the Kanitz (1976) and Minussi (2008) models did not show convergence. Regarding to the comparisons between the results obtained for the models and the ratings of agencies, the KMV structural model and the non structural models of Minussi (2008) and Altman, Baidya and Dias (1979) stood out for having the most similar results.

A energia elétrica tem papel fundamental em todos os lugares do mundo e, no Brasil, a importância não poderia ser menor. Com sua implantação no país no final do século XIX, o setor passou por diversos períodos de crescimento com características distintas. A economia nacional, de forma similar ao setor elétrico, ao longo do mesmo período passou por fases de grande expansão e por fases de crise. Nesse contexto, o presente trabalho analisa a evolução da economia e do setor elétrico nacionais desde o final do século XIX até o ano de 2010 e, com isso, busca investigar de que forma se relacionaram os crescimentos de um e de outro. Para isso são conduzidas análises histórica e econométrica. A primeira é feita por meio de revisão da bibliografia pertinente ao tema em que o período do estudo é dividido em sete partes: a primeira aborda o início da indústria elétrica brasileira e vai até o final da Primeira República (1889-1930), a segunda abrange o início do governo Getúlio Vargas até o início da ditadura militar (1930-1964), a terceira trata do período da ditadura (1964-1985), a quarta engloba a fase do início da República Nova até a estruturação do setor elétrico (1985-1994), a quinta analisa o período entre a reestruturação do setor elétrico e a crise energética (1995-2002), a sexta trata da contra-reforma do setor elétrico e também de suas características de mercados (2002-2003) e, por fim a última parte analisa o setor elétrico e economia contemporâneos (2003-2010). Na análise econométrica utilizada nesta dissertação testa-se, de forma quantitativa, as evidências de existência de relação de longo prazo entre a economia e o setor elétrico através de um teste de cointegração. A seguir, é conduzida uma análise econométrica adicional com a qual, por meio de um modelo estrutural de séries de tempo, estima-se a demanda de energia elétrica para os próximos anos no Brasil. Com essa ferramenta de projeção de demanda pretende-se oferecer subsídios para o planejamento da expansão da capacidade instalada de geração de energia elétrica do sistema brasileiro. Por fim, analisa-se a validade dos resultados econométricos à luz do contexto histórico desenvolvido anteriormente e apresentam-se conclusões e limitações para este trabalho. / Electricity plays a fundamental role in all parts of the world and in Brazil the importance could not be lower. With its beginnings in the country in the late nineteenth century, the power sector has gone through several periods of growth with distinct characteristics. The national economy, similarly to the electricity sector, over the same period went through phases of boom and phases of crisis. In this context, this dissertation analyzes the evolution of the economy and of the national power sector since its beginning until the year 2010 and, therefore, seeks to investigate how the increases of one were related to the other. Historical and econometric analyses are conducted for this purpose. The first is through the review of the literature relevant to the topic, where the period of study is divided into seven parts: the first is from the start of the Brazilian electrical industry and runs until the end of the First Republic (1889-1930), the second is from beginning of the Getulio Vargas government until the beginning of the military dictatorship (1930-1964), the third deals with the period of the dictatorship (1964-1985), the fourth phase studies from the beginning of the New Republic to the structuring of the electricity sector (1985-1994), the fifth examines the period between the electricity sector restructuring and the energy crisis (1995-2002), the sixth comes from the electricity sector reform as well as its market characteristics (2002-2003) and finally the last part examines the contemporary electricity sector and economy (2003-2010). In econometric analysis tests used in this dissertation, it is tested, quantitatively, the evidence of the existence of long-term relationship between the economy and energy sector through a cointegration test. Next, an additional econometric analysis is conducted where, through a structural time series model, it is estimated the demand for electricity for the next years in Brazil. With this demand projection tool, it is intended to provide background information for planning the expansion of installed capacity of electric power generation system in Brazil. Finally the validity of the econometric results is analyzed in the light of the historical context previously developed, and conclusions and limitations of this paper are presented.

<p>Central to the explosive growth of the Internet has been the desire</p><p>of dispersed buyers and sellers to interact readily and in a manner</p><p>hitherto impossible. Underpinning these interactions, auction</p><p>pricing mechanisms have enabled Internet transactions in novel ways.</p><p>Despite this massive growth and new medium, empirical work in</p><p>marketing and economics on auction use in Internet contexts remains</p><p>relatively nascent. Accordingly, this dissertation investigates the</p><p>role of online auctions; it is composed of three essays.</p><p>The first essay, ``Online Auction Demand,'' investigates seller and</p><p>buyer interactions via online auction websites, such as eBay. Such</p><p>auction sites are among the earliest prominent transaction sites on</p><p>the Internet (eBay started in 1995, the same year Internet Explorer</p><p>was released) and helped pave the way for e-commerce. Hence, online</p><p>auction demand is the first topic considered in my dissertation. The</p><p>second essay, ``A Dynamic Model of Sponsored Search Advertising,''</p><p>investigates sponsored search advertising auctions, a novel approach</p><p>that allocates premium advertising space to advertisers at popular</p><p>websites, such as search engines. Because sponsored search</p><p>advertising targets buyers in active purchase states, such</p><p>advertising venues have grown very rapidly in recent years and have</p><p>become a highly topical research domain. These two essays form the</p><p>foundation of the empirical research in this dissertation. The third</p><p>essay, ``Sponsored Search Auctions: Research Opportunities in</p><p>Marketing,'' outlines areas of future inquiry that I intend to</p><p>pursue in my research.</p><p>Of note, the problems underpinning the two empirical essays exhibits</p><p>a common form, that of a two-sided network wherein two parties</p><p>interact on a common platform (Rochet and Tirole, 2006). Although</p><p>theoretical research on two-sided markets is abundant, this</p><p>dissertation focuses on their use in e-commerce and adopts an</p><p>empirical orientation. I assume an empirical orientation because I</p><p>seek to guide firm behavior with concrete policy recommendations and</p><p>offer new insights into the actual behavior of the agents who</p><p>interact in these contexts. Although the two empirical essays share</p><p>this common feature, they also exhibit notable differences,</p><p>including the nature of the auction mechanism itself, the</p><p>interactions between the agents, and the dynamic frame of the</p><p>problem, thus making the problems distinct. The following abstracts</p><p>for these two essays as well as the chapter that describes my future</p><p>research serve to summarize these contributions, commonalities and</p><p>differences.</p><p>Online Auction Demand</p><p>With $40B in annual gross merchandise volume, electronic auctions</p><p>comprise a substantial and growing sector of the retail economy. For</p><p>example, eBay alone generated a gross merchandise volume of $14.4B</p><p>during the fourth quarter of 2006. Concurrent with this growth has</p><p>been an attendant increase in empirical research on Internet</p><p>auctions. However, this literature focuses primarily on the bidder;</p><p>I extend this research to consider both seller and bidder behavior</p><p>in an integrated system within a two-sided network of the two</p><p>parties. This extension of the existing literature enables an</p><p>exploration of the implications of the auction house's marketing on</p><p>its revenues as well as the nature of bidder and seller interactions</p><p>on this platform. In the first essay, I use a unique data set of</p><p>Celtic coins online auctions. These data were obtained from an</p><p>anonymous firm and include complete bidding and listing histories.</p><p>In contrast, most existing research relies only on the observed</p><p>website bids. The complete bidding and listing histories provided by</p><p>the data afford additional information that illuminates the insights</p><p>into bidder and seller behavior such as bidder valuations and seller</p><p>costs.</p><p>Using these data from the ancient coins category, I estimate a</p><p>structural model that integrates both bidder and seller behavior.</p><p>Bidders choose coins and sellers list them to maximize their</p><p>respective profits. I then develop a Markov Chain Monte Carlo (MCMC)</p><p>estimation approach that enables me, via data augmentation, to infer</p><p>unobserved bidder and seller characteristics and to account for</p><p>heterogeneity in these characteristics. My findings indicate that:</p><p>i) bidder valuations are affected by item characteristics (e.g., the</p><p>attributes of the coin), seller (e.g. reputation), and auction</p><p>characteristics (e.g., the characteristics of the listing); ii)</p><p>bidder costs are affected by bidding behavior, such as the recency</p><p>of the last purchase and the number of concurrent auctions; and iii)</p><p>seller costs are affected by item characteristics and the number of</p><p>concurrent listings from the seller (because acquisition costs</p><p>evidence increasing marginal values).</p><p>Of special interest, the model enables me to compute fee</p><p>elasticities, even though no variation in historical fees exists in</p><p>these data. I compute fee elasticities by inferring the role of</p><p>seller costs in their historical listing decision and then imputing</p><p>how an increase in these costs (which arises from more fees) would</p><p>affect the seller's subsequent listing behavior. I find that these</p><p>implied commission elasticities exceed per-item fee elasticities</p><p>because commissions target high value sellers, and hence, commission</p><p>reductions enhance their listing likelihood. By targeting commission</p><p>reductions to high value sellers, auction house revenues can be</p><p>increased by 3.9%. Computing customer value, I find that attrition</p><p>of the largest seller would decrease fees paid to the auction house</p><p>by $97. Given that the seller paid $127 in fees, competition</p><p>offsets only 24% of the fees paid by the seller. In contrast,</p><p>competition largely in the form of other bidders offsets 81% of the</p><p>$26 loss from buyer attrition. In both events, the auction house</p><p>would overvalue its customers by neglecting the effects of</p><p>competition.</p><p>A Dynamic Model of Sponsored Search Advertising</p><p>Sponsored search advertising is ascendant. Jupiter Research reports</p><p>that expenditures rose 28% in 2007 to $8.9B and will continue to</p><p>rise at a 26% Compound Annual Growth Rate (CAGR), approaching half</p><p>the level of television advertising and making sponsored search</p><p>advertising one of the major advertising trends affecting the</p><p>marketing landscape. Although empirical studies of sponsored search</p><p>advertising are ascending, little research exists that explores how</p><p>the interactions of various agents (searchers,</p><p>advertisers, and the search engine) in keyword</p><p>markets affect searcher and advertiser behavior, welfare and search</p><p>engine profits. As in the first essay, sponsored search constitutes</p><p>a two-sided network. In this case, bidders (advertisers) and</p><p>searchers interact on a common platform, the search engine. The</p><p>bidder seeks to maximize profits, and the searcher seeks to maximize</p><p>utility.</p><p>The structural model I propose serves as a foundation to explore</p><p>these outcomes and, to my knowledge, is the first structural model</p><p>for keyword search. Not only does the model integrate the behavior</p><p>of advertisers and searchers, it also accounts for advertisers</p><p>competition in a dynamic setting. Prior theoretical research has</p><p>assumed a static orientation to the problem whereas prior empirical</p><p>research, although dynamic, has focused solely on estimating the</p><p>dynamic sales response to a single firm's keyword advertising</p><p>expenditures.</p><p>To estimate the proposed model, I have developed a two-step Bayesian</p><p>estimator for dynamic games. This approach does not rely on</p><p>asymptotics and also facilitates a more flexible model</p><p>specification.</p><p>I fit this model to a proprietary data set provided by an anonymous</p><p>search engine. These data include a complete history of consumer</p><p>search behavior from the site's web log files and a complete history</p><p>of advertiser bidding behavior across all advertisers. In addition,</p><p>the data include search engine information, such as keyword pricing</p><p>and website design.</p><p>With respect to advertisers, I find evidence of dynamic</p><p>bidding behavior. Advertiser valuation for clicks on their sponsored</p><p>links averages about $0.27. Given the typical $22 retail price of</p><p>the software products advertised on the considered search engine,</p><p>this figure implies a conversion rate (sales per click) of about</p><p>1.2%, well within common estimates of 1-2% (gamedaily.com). With</p><p>respect to consumers, I find that frequent clickers place a</p><p>greater emphasis on the position of the sponsored advertising link.</p><p>I further find that 10% of consumers perform 90% of the clicks.</p><p>I then conduct several policy simulations to illustrate the effects</p><p>of change in search engine policy. First, I find that the</p><p>search engine obtains revenue gains of nearly 1.4% by sharing</p><p>individual level information with advertisers and enabling them to</p><p>vary their bids by consumer segment. This strategy also improves</p><p>advertiser profits by 11% and consumer welfare by 2.9%. Second, I</p><p>find that a switch from a first to second price auction results in</p><p>truth telling (advertiser bids rise to advertiser valuations), which</p><p>is consistent with economic theory. However, the second price</p><p>auction has little impact on search engine profits. Third, consumer</p><p>search tools lead to a platform revenue increase of 3.7% and an</p><p>increase of consumer welfare of 5.6%. However, these tools, by</p><p>reducing advertising exposure, lower advertiser profits by 4.1%.</p><p>Sponsored Search Auctions: Research Opportunities in Marketing</p><p>In the final chapter, I systematically review the literature on</p><p>keyword search and propose several promising research directions.</p><p>The chapter is organized according to each agent in the search</p><p>process, i.e., searchers, advertisers and the search engine, and</p><p>reviews the key research issues for each. For each group, I outline</p><p>the decision process involved in keyword search. For searchers, this</p><p>process involves what to search, where to search, which results to</p><p>click, and when to exit the search. For advertisers, this process</p><p>involves where to bid, which word or words to bid on, how much to</p><p>bid, and how searchers and auction mechanisms moderate these</p><p>behaviors. The search engine faces choices on mechanism design,</p><p>website design, and how much information to share with its</p><p>advertisers and searchers. These choices have implications for</p><p>customer lifetime value and the nature of competition among</p><p>advertisers. Overall, I provide a number of potential areas of</p><p>future research that arise from the decision processes of these</p><p>various agents.</p><p>Foremost among these potential areas of future research are i) the</p><p>role of alternative consumer search strategies for information</p><p>acquisition and clicking behavior, ii) the effect of advertiser</p><p>placement alternatives on long-term profits, and iii) the measure of</p><p>customer lifetime value for search engines. Regarding the first</p><p>area, a consumer's search strategy (i.e., sequential search and</p><p>non-sequential search) affects which sponsored links are more likely</p><p>to be clicked. The search pattern of a consumer is likely to be</p><p>affected by the nature of the product (experience product vs. search</p><p>product), the design of the website, the dynamic orientation of the</p><p>consumer (e.g., myopic or forward-looking), and so on. This search</p><p>pattern will, in turn, affect advertisers payments, online traffic,</p><p>sales, as well as the search engine's revenue. With respect to the</p><p>second area, advertisers must ascertain the economic value of</p><p>advertising, conditioned on the slot in which it appears, before</p><p>making decisions such as which keywords to bid on and how much to</p><p>bid. This area of possible research suggests opportunities to</p><p>examine how advertising click-through and the number of impressions</p><p>differentially affect the value of appearing in a particular</p><p>sponsored slot on a webpage, and how this value is moderated by an</p><p>appearance in a non-sponsored slot (i.e., a slot in the organic</p><p>search results section). With respect to the third area of future</p><p>research, customer value is central to the profitability and</p><p>long-term growth of a search engine and affects how the firm should</p><p>allocate resources for customer acquisition and retention.</p><p>Organization</p><p>This dissertation is organized as follows. After this brief</p><p>introduction, the essay, ``Online Auction Demand,'' serves as a</p><p>basis that introduces some concepts of auctions as two-sided</p><p>markets. Next, the second essay, ``A Dynamic Model of Sponsored</p><p>Search Advertising,'' extends the first essay by considering a</p><p>richer context of bidder competition and consumer choice behavior.</p><p>Finally, the concluding chapter, which outlines my future research</p><p>interests, considers potential extensions that pertain especially to</p><p>sponsored search advertising.</p> / Dissertation

In this work, pricing models of corporate coupon-bonds and credit default swaptions are derivedand analyzed. Corporate coupon-bonds are priced incorporating both intensity models and structural models, and also jumps introduced by seasonal effects. In deriving the models, we form portfolios to hedge the risk incurred by the instruments, then derive PDE equations using the arbitrage principle and the Ito Lemma for jump processes. The mathematical models are the parabolic-type PDE equations with terminal conditions and boundary conditions. These PDE problems are analyzed and solved by various transformations and incorporation with probabilistic properties. Either a unique solution in the exponential form is obtained, or a particular solution in the separation formis acquired. Further, the pricing model of credit default swaptions is derived using the pricing of corporate coupon-bonds in the similar manner. The main idea of deriving the price of credit default swaptions is to use the price of existing products, i.e., corporate bonds, as opposed to the existing models, which use non-existing forward credit default swap price of the reference entity. The prices of corporate coupon-bonds and credit default swaptions with unexpected default, obtained from these models, are compared to the actual market prices and analyzed.

The topic of the work for Master’s degree is: “The formation of fuzzy logic links and structural models”. The purpose of this work is to design a calculator, which would make arithmetic operations with the elements of fuzzy logic. This programme would allow to make such operations with fussy logic elements: addition (+), substraction (–), multiplication (×), division ( : ). Five main parts are presented in the work: analytical one, designed one, the documentation of the consumer, the evaluation of fuzzy logic calculator, the conclusions. In the first part the analysis of fussy arithmetic operation is made, the operations which can be applied in designing are given. In the second part the requirements of the consumer are analyzed and functional requirements are given. The specification of the system is made. The schedule of the project performance is set. The measures of project hazard and prevention are set. The testing, which has been made is described. The plan of system developing is presented. In the third part the documentation of consumer is written. In the fourth part a thorough analysis of calculator working is made, its advantages and disadvantages, fields of application are singled out. The fifth part is the conclusions. Fussy logic makes it possible to achieve much better management characteristics than by traditional methods. One may use the designed calculator by teaching the course of “Calculating methods with indefinite sets” and will help students to master... [to full text]

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This work empirically examines six structural models of the term structure of credit risk spreads: Merton (1974), Longstaff & Schwartz (1995) (with and without stochastic interest rates), Leland & Toft (1996), Collin-Dufresne & Goldstein (2001), and a constant elasticity of variance model. The conventional approach to testing structural models has involved the use of observable data to proxy the latent capital structure process, which may introduce additional specification error. This study extends Jones, Mason & Rosenfeld (1983) and Eom, Helwege & Huang (2004) by using implicit estimation of key model parameters resulting in an improved level of model fit. Unlike prior studies, the models are fitted from the observed dynamic term structure of firm-specific credit spreads, thereby providing a pure test of model specification. The models are implemented by adapting the method of Duffee (1999) to structural credit models, thereby treating the capital structure process is truly latent, and simultaneously enforcing cross-sectional and time-series model constraints. Quasi-maximum likelihood parameter estimates of the capital structure process are obtained via the extended Kalman filter applied to actual market trade prices on 32 firms and 200 bonds for the period 1994 to 2000. / We find that including an allowance for time-variation in the market liquidity premium improves model specification. A simple extension of the Merton (1974) model is found to have the greatest prediction accuracy, although all models performed with similar prediction errors. At between 28.8 to 34.4 percent, the root mean squared error of the credit spread prediction is comparable with reduced-form models. Unlike Eom, Helwege & Huang (2004) we do not find a wide dispersion in model prediction errors, as evidenced by an across model average mean absolute percentage error of 22 percent. However, in support of prior studies we find an overall tendency for slight underprediction, with the mean percentage prediction error of between -6.2 and -8.7 percent. Underprediction is greatest with short remaining bond tenor and low rating. Credit spread prediction errors across all models are non-normal, and fatter tailed than expected, with autocorrelation evident in their time series. / More complex models did not outperform the extended Merton (1974) model; in particular stochastic interest-rate and early default accompanied by an exogenous write-down rate appear to add little to model accuracy. However, the inclusion of solvency ratio mean-reversion in the Collin-Dufresne & Goldstein (2001) model results in the most realistic latent solvency dynamics as measured by its implied levels of asset volatility, default boundary level, and mean-reversion rate. The extended Merton (1974) is found to imply asset volatility levels that are too high on average when compared to observed firm equity volatility. / We find that the extended Merton (1974) and the Collin-Dufresne & Goldstein (2001) models account for approximately 43 percent of the credit spread on average. For BB rated trades, the explained proportion rises to 55 to 60 percent. For investment grade trades, our results suggest that the amount of the credit spread that is default related is approximately double the previous estimate of Huang & Huang (2003). / Finally, we find evidence that the prediction errors are related to market-wide factors exogenous to the models. The percentage prediction errors are positively related to the VIX and change in GDP, and negatively related to the Refcorp-Treasury spread.

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