Submitted by Suethene Souza (suethene.souza@ufpe.br) on 2015-03-11T19:46:43Z No. of bitstreams: 2 DISSERTAÇÃO Júlio Pereira de Araújo.pdf: 2035615 bytes, checksum: 24487c14ffd88e76ea5cf5bfea52bfb3 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-11T19:46:43Z (GMT). No. of bitstreams: 2 DISSERTAÇÃO Júlio Pereira de Araújo.pdf: 2035615 bytes, checksum: 24487c14ffd88e76ea5cf5bfea52bfb3 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2014-06-13 / Esta pesquisa teve por objetivo comparar o risco e o retorno das empresas que foram investidas por gestores de private equity e/ou venture capital (PE/VC) em relação às que não receberam esse tipo de investimento. A base de dados utilizada foi os preços e os retornos diários das ações das empresas que realizaram IPO no período de fevereiro/2006 a novembro/2010, bem como do índice Ibovespa. A análise do retorno compreendeu períodos de 21, 126, 252, 504 e 756 dias após o IPO por meio das metodologias do Retorno Anormal Acumulado (RAA) e de modelos de regressão múltipla que tentaram explicar o comportamento dos RAA’s através de seis variáveis: valor de mercado, índice book to market, volume de oferta, parcela primária, presença de organizações gestoras de PE/VC e nível de governança corporativa. Os resultados mostraram que as empresas com PE/VC apresentaram maiores RAA’s em relação às empresas sem PE/VC em todos os períodos. Na análise das regressões chegou-se à conclusão de que além da presença ou não de um fundo de PE/VC, o nível de governança corporativa e o volume de oferta influenciam nos valores dos RAA’s das empresas. Na análise do risco verificou-se aspectos de volatilidade dos preços das ações (normalidade, presença de raízes unitárias, autocorrelação serial dos erros e suporte aos efeitos heterocedásticos) e a metodologia utilizada foi o Value at Risk (VaR) com Simulação de Monte Carlo. Analisou-se os VaR’s individuais das empresas por ano de realização do IPO (2006 a 2010) e no período total. Cada VaR individual foi calculado considerando-se o melhor dentre três modelos de volatilidade: desvio-padrão; volatilidade com modelo GARCH mais parcimonioso e o melhor modelo GARCH encontrado pela metodologia Akaike. Os resultados da análise da volatilidade mostraram principalmente que a maioria das empresas apresenta suporte aos efeitos heterocedásticos. Em relação aos resultados do VaR, tanto no período total quanto na análise por ano do IPO, o grupo de empresas que recebeu investimentos de PE/VC apresentou maiores valores de VaR em relação ao grupo sem PE/VC. Assim, pôde-se concluir que o grupo de empresas com PE/VC possui maiores níveis de retorno e de risco em relação ao conjunto das empresas sem PE/VC.

Sukuk are an important mode of financing in the Islamic financial system. As usury (interest) is prohibited in Islam, conventional bonds are not suitable for investors in Islamic countries. Since their launch in the 1980s, Sukuk have gained recognition and popularity as a substitute for conventional bonds. However, their unique features mean that Sukuk are not always clearly understood. The aim of this study is to analyse the differences and similarities between Sukuk and conventional bonds in terms of their risk and return characteristics.

01 Abstract: This thesis is concerned with the robust methods in portfolio theory. Different risk measures used in portfolio management are introduced and the corresponding robust portfolio optimization problems are formulated. The analytical solutions of the robust portfolio optimization problem with the lower partial moments (LPM), value-at-risk (VaR) or conditional value-at-risk (CVaR), as a risk measure, are presented. The application of the worst-case conditional value-at-risk (WCVaR) to robust portfolio management is proposed. This thesis considers WCVaR in the situation where only partial information on the underlying probability distribution is available. The minimization of WCVaR under mixture distribution uncertainty, box uncertainty, and ellipsoidal uncertainty are investigated. Several numerical examples based on real market data are presented to illustrate the proposed approaches and advantage of the robust formulation over the corresponding nominal approach.

This diploma thesis studies extreme value theory and its application in finan- cial risk management, when focusing on computation of well-known risk measure - Value at Risk (VaR). The first part of the thesis reviews theoretical background. In particular, it rigorously discusses the extreme value theory when emphasi- zing fundamentals theorems and their consequences followed by the summary of methods based on this theory, specifically, Block Maxima method, Hill met- hod and Peaks over Threshold method. Moreover, specific issues that may arise in such applications and ways how to deal with these problems are described. The second part of the thesis contains extensive empirical study, which together with theoretical foundings applies each of the examined method to real market data of the closing prices of Dow Jones Industrial Average stock index, stocks of JPMorgan and stock index Russell 2000 in order to compare methods based on extreme value theory together with the classic methodology RiskMetrics. 1

Le modèle RiskMetrics développé par la Banque JP Morgan suite à l'amendement des accords de Bâle de 1988 a été érigé comme mesure de risque financier pour faire face aux importantes perturbations ayant affecté les marchés bancaires internationaux. Communément appelé Value at Risk, il a été admis par l'ensemble des organes et institutions financiers comme une mesure de risque cohérente. Malgré sa popularité, elle est le sujet de beaucoup de controverses. En effet, les paramètres d'estimation du système RiskMetrics sont supposés fixes au cours du temps ce qui est contraire aux caractéristiques des marchés financiers. Deux raisons valables permettent de justifier cette instabilité temporelle : * la présence d'agents hétérogènes fait qu'on n'analyse plus la VaR en se focalisant sur une seule dimension temporelle mais plutôt sur des fréquences de trading (nous recourons pour cela à la méthode Wavelet). * la structure des séries financières qui d'habitude est affectée par les phénomènes de crash, bulle etc. Ceux-ci peuvent être considérés comme des variables cachées qu'on doit prendre en compte dans l'évaluation du risque. Pour cela, nous recourons à la modélisation espace d'états et au filtre de Kalman. Nous savons d'emblée que les performances de la VaR s'évaluent en recourant au test de backtesting. Celui-ci repose sur la technique de régression roulante qui montre une faille évidente : Nous ne pouvons pas connaitre le processus gouvernant la variation des paramètres, il n'y a pas endogénéisation de la dynamique de ceux-ci. Pour apporter une solution à ce problème, nous proposons une application du filtre de Kalman sur les modèles VaR et WVaR. Ce filtre, par ses fonctions corrige de manière récursive les paramètres dans le temps. En ces termes nous définissons une mesure de risque dit SVaR qui en réalité est la VaR obtenue par une actualisation des paramètres d'estimation. Elle permet une estimation précise de la volatilité qui règne sur le marché financier. Elle donne ainsi la voie à toute institution financière de disposer de suffisamment de fonds propres pour affronter le risque de marché. / The RiskMetrics model developed by the bank JP Morgan following the amendment of Basel accords 1988 was erected as a measure of financial risk to deal with important disturbances affecting international banking markets. Commonly known as Value at Risk, it was accepted by all bodies and financial institutions to be a coherent risk measure. Despite its popularity, it is the subject of many controversies. Indeed, the estimation parameters of RiskMetrics are assumed to be fixed over time, which is contrary to the characteristics of financial markets. Two valid reasons are used to justify temporal instability : *Due to the presence of heterogenous agents the VaR is not analysed by focusing on a single temporal dimension but rather on trading frequencies (we use Wavelet method for it). *The structure of financial time series wich is usually affected by the crash bubble phenomenons and so on. These can be considered as hidden variables that we must take into account in the risk assessment. For this, we use state space modeling and kalman filter. We immediately know that performances of the VaR are evaluated using backtesting test. This is based on the technique of rolling regression wich shows an obvious break : We can not know the processes governing the variation of parameters; there is no endogeneisation dynamics thereof. To provide a solution to this problem, we propose an application of the kalman filter on VaR and WVaR models. This filter recursively corrects by its functions the parameters of time. In these terms we define a risk measure called SVaR wich in realitity is the VaR obtained by updating estimation parameters. It provides an accurate estimate of the volatility existing in the financial market. It thus gives way to any financial institution to have enough capital to face market risk.

This master thesis aims to describe and apply in practice solutions of basic tasks in portfolio management- portfolio optimization, portfolio modelling and risk management. As value of financial assets in future is a random variable, it is necessary to use mathematic tools resulting from probability theory and statistics. Basic terms from this area are for example stochastic Wiener process or geometric Brownian motion, which are described in first part of this thesis. Next parts of thesis describe the Markowitz model or method Value at Risk. In the last part of thesis is application of calculation VaR using Monte Carlo simulation for stock portfolio constructed as optimal portfolio according to Markowitz model from real data.

Depuis le début de la décennie 2000, le marché du maïs connaît un changement profond. D'une part, le prix enregistre une volatilité extrême sans précédent. D'autre part, ce marché bénéficie d'un déferlement massif des investisseurs financiers. Il offre des opportunités d'investissements financiers rentables en raison des crises récurrentes sur le marché boursier. Il est intéressant pour des investisseurs (spéculateurs, fondamentalistes, arbitragistes) d'avoir connaissance des résultats d'analyse des variations extrêmes du prix du marché du maïs. La maîtrise des variations extrêmes du prix permet une meilleure gestion du risque. Des études ont déjà été menées dans cette direction en utilisant des techniques du type « VaR ». Cependant, les différents modèles de gestion du risque par la VaR souffrent de certaines limites. Ils supposent l'hypothèse de la normalité des distributions. Or, la distribution des rendements du maïs montre des valeurs extrêmes. Cela ne permet pas une bonne appréciation du risque. Afin de contribuer à l'analyse des variations extrêmes de prix sur le marché du maïs, nous faisons appel aux modèles GARCH et à la théorie des valeurs extrêmes. Puis, dans un cadre multi-varié, le lien entre rendements spots et futures exprime le degré de la dépendance. Il permet ainsi d'analyser l'effet de la spéculation. Pour cela, nous utilisons la théorie des valeurs extrêmes couplée à la mesure de la dépendance qu'on appelle « copule » pour cerner les mouvements extrêmes des variations du prix au delà d'un seuil. En effet, la théorie des copules propose toute une gamme de fonctions capable de mesurer la dépendance asymétrique aux queues de la distribution des rendements spots et futures du maïs. / Since the early 2000s, the corn market is undergoing a profound change. On the one hand, the price has experienced unprecedented extreme volatility. Moreover, this market has a massive outpouring of financial investors. The corn market offers profitable financial investments due to recurrent crises in the stock market opportunities. It is interesting for investors (speculators, fundamentalists, arbitrageurs) to be aware of the analysis of extreme price changes in corn results. The mastery of extreme price changes provides better risk management. Studies have already been conducted in this direction by using techniques such as "VaR". However, the different models of risk management VaR suffer from certain limitations. They assume the assumption of normality of distributions. However, the distribution of return corn shows extreme values. This does not allow a proper assessment of risk. To contribute to the analysis of extreme price changes in the corn market, we use the GARCH models and the theory of extreme values. Then, in a multi-varied context, the link between returns and future spots expresses the degree of dependence. It allows analyzing the effect of speculation. We use extreme value theory coupled to the measure of dependence called "copula" to identify extreme movements of price changes beyond a threshold. Indeed, copula theory offers a range of features that can measure the asymmetric dependence tails of the distribution of spot return and futures of corn.

This thesis is focused on a theoretical explication of the basic methods of the calculation Value at Risk for the market and credit risk. For the market risk there is in detail developed the variance -- covariance method, historical simulation and Monte Carlo simulation, above all for the nonlinear portfolio. For all methods the assumptions of their applications are highlighted and the comparation of these methods is made too. For the credit risk there is made a theoretical description of CreditMetrics, CreditRisk+ and KMV models. Analytical part is concerned in the quantification of Value at Risk on two portfolios, namely nonlinear currency portfolio, which particular assumptions of the variance -- covariance method a Monte Carlo simulation are tested on. Then by these methods the calculation of Value at Risk is realized. The calculation of Credit Value at Risk is made on the portfolio of the US corporate bonds by the help of CreditMetrics model.

The main focus of this thesis lies on description of Risk Management in context of Energy Trading. The paper will predominantly discuss Value at Risk and its modifications as a main overall indicator of Energy Risk.

The Master's thesis deals with management of the foreign exchange risk in international trade. Initially the essential theoretical framework is introduced and then applied on the real situation of a Czech internationally trading company. The first chapter explains individual types of foreign exchange exposure, principal foreign exchange forecasting methods and foreign exchange risk evaluation methods. The second chapter deals with internal techniques of lowering foreign exchange exposure and external hedging tools. In the third chapter the improvement recommendations are stated based on an analysis of company's foreign currency denominated cash flow, subsequent evaluation of its foreign exchange position and assessment of its current approach to foreign exchange risk.