Made available in DSpace on 2016-03-15T19:26:13Z (GMT). No. of bitstreams: 1 Rodrigo Vogliotti.pdf: 868389 bytes, checksum: e1b90368c40778893d720b9e7a0fe869 (MD5) Previous issue date: 2012-08-17 / The difficulty in developing a statistical model that includes random variables and the need for intensive data processing capacity are the main challenges for the measurement of counterparty credit risk. The need to know the exposure value at the time of default (EAD) on a derivative instrument is a decisive factor for pricing, portfolio management and capital allocation. Recent events such as the creation of innovative products, coming from the new Basel Accord (Basel II) and the credit crisis of 2007/08 reinforce the importance of knowing what the actual credit risk exposure in a particular transaction. The aim of this study was to develop models for measuring credit risk of the counterparty from the estimation of counterparty exposure to bonds, equities and forward contract through the use of Monte Carlo simulation. The results of the sensitivity analysis indicate that certain parameters such as the interest rate, the mean and standard deviation show strong linear correlation with exposure (EAD) and this issue can be an important driver for the decision-making process. In the model of forward contract was found that correlated random variables can potentiate the exposure value. / A dificuldade em desenvolver um modelo estatístico que contemple variáveis aleatórias e a necessidade de intensa capacidade para processamento de dados são os principais desafios para a mensuração do risco de crédito de contraparte. A necessidade em conhecer o valor da exposição no momento do default (EAD) em um instrumento derivativo é fator decisivo para a precificação, gestão do portfólio e alocação de capital. Recentes acontecimentos como a criação de produtos inovadores, o advindo do novo acordo de Basileia (Basileia II) e a crise de crédito de 2007/08 reforçaram a importância de se saber qual o risco de crédito efetivo que cada contraparte está exposta em uma determinada transação. O objetivo deste estudo foi desenvolver modelos para mensuração do risco de crédito da contraparte a partir da estimação da exposição da contraparte para títulos, ações e contrato a termo de ações através da utilização da simulação de Monte Carlo. Os resultados da análise de sensibilidade indicam que certos parâmetros como a taxa de juro, a média e o desvio padrão apresentam forte correlação linear com a exposição (EAD) calculada e podem ser importantes direcionadores para o processo decisório. No modelo de contrato a termo de ações foi verificado que variáveis aleatórias correlacionadas potencializam o valor da exposição.

This thesis project is an experimental study on how to approach quantitative portfolio credit risk modelling in Venture Debt portfolios. Facing a lack of applicable default data from ArK and publicly available sets, as well as seeking to capture companies that fail to service debt obligations before defaulting per se, we present an approach to risk modeling based on trends in revenue. The main framework revolves around driving a Monte Carlo simulation with Copluas to predict future revenue scenarios across a portfolio of early-stage technology companies. Three models for a random Gaussian walk, a Linear Dynamic System and an Autoregressive Integrated Moving Average (ARIMA) time series are implemented and evaluated in terms of their portfolio Value-at-Risk influence. The model performance confirms that modeling portfolio risk in Venture Debt is challenging, especially due to lack of sufficient data and thus a heavy reliance on assumptions. However, the empirical results for Value-at-Risk and Expected Shortfall are in line with expectations. The evaluated portfolio is still in an early stage with a majority of assets not yet in their repayment period and consequently the spread of potential losses within one year is very tight. It should further be recognized that the scope in terms of explanatory variables for sales and model complexities has been narrowed and simplified for computational benefits, transparency and communicability. The main conclusion drawn is that alternative approaches to model Venture Debt risk is fully possible, and should improve in reliability and accuracy with more data feeding the model. For future research it is recommended to incorporate macroeconomic variables as well as similar company analysis to better capture macro, funding and sector conditions. Furthermore, it is suggested to extend the set of financial and operational explanatory variables for sales through machine learning or neural networks. / Detta examensarbete är en experimentell studie för kvantitativ modellering av kreditrisk i Venture Debt-portföljer. Givet en brist på tillgänlig konkurs-data från ArK samt från offentligt tillgängliga databaser i kombination med ambitionen att inkludera företag som misslyckas med skuldförpliktelser innan konkurs per se, presenterar vi en metod för riskmodellering baserad på trender i intäkter. Ramverket för modellen kretsar kring Monte Carlo-simulering med Copluas för att estimera framtida intäktsscenarier över en portfölj med tillväxtbolag inom tekniksektorn. Tre modeller för en random walk, ett linjärt dynamiskt system och ARIMA- tidsserier implementeras och utvärderas i termer av deras inflytande på portföljens Value-at- Risk. Modellens prestationer bekräftar att modellering av portföljrisk inom Venture Debt är utmanande, särskilt på grund av bristen på tillräckliga data och därmed ett stort beroende av antaganden. Dock är de empiriska resultaten för Value-at-Risk och Expected Shortfall i linje med förväntningarna. Den utvärderade portföljen är fortfarande i ett tidigt skede där en majoritet av tillgångarna fortfarande befinner sig i en amorteringsfri period och följaktligen är spridningen av potentiella förluster inom ett år mycket snäv. Det bör vidare tillkännages att omfattningen i termer av förklarande variabler för intäkter och modellkomplexitet har förenklats för beräkningsfördelar, transparens och kommunicerbarhet. Den främsta slutsatsen som dras är att alternativa metoder för att modellera risker inom Venture Debt är fullt möjliga och bör förbättras i tillförlitlighet och precision när mer data kan matas in i modellen. För framtida arbete rekommenderas det att inkorporera makroekonomiska variabler samt analys av liknande bolag för att bättre fånga makro-, finansierings- och sektorsförhållanden. Vidare föreslås det att utöka uppsättningen av finansiella och operationella förklarande variabler för intäkter genom maskininlärning eller neurala nätverk.

The thesis "Credit risk from Basel II point of view" deals with new capital concept with main focus on the credit risk. The particular emphasis is laid on the chief issue of Basel II concept i.e. internal models. The thesis quite in detail describes the usage of basel parameters - LGD particularly - in various day-to-day business processes of credit institutions. An individual part of the thesis is devoted to credit risk mitigants and their impacts on the amount of capital requirements. The analysis carried out precedent Basel II implementation indicated the launching of Basel II should imply risk weighted assests to credit risk decline. This documents the last chapter.