Made available in DSpace on 2010-04-20T21:00:30Z (GMT). No. of bitstreams: 3 marciocecilioturma2004.pdf.jpg: 19602 bytes, checksum: 0772484d1cb46349dfbfb25620b5cdae (MD5) marciocecilioturma2004.pdf: 859203 bytes, checksum: 346a3e7d5751118ff894a182d7512b56 (MD5) marciocecilioturma2004.pdf.txt: 86793 bytes, checksum: e0c91b2715fc569bc6ec29bfce078e69 (MD5) Previous issue date: 2007-01-23T00:00:00Z / Há forte evidência que os retornos das séries financeiras apresentam caudas mais pesadas que as da distribuição normal, principalmente em mercados emergentes. No entanto, muitos modelos de risco utilizados pelas instituições financeiras baseiam-se em normalidade condicional ou não condicional, reduzindo a acurácia das estimativas. Os recentes avanços na Teoria de Valores Extremos permitem sua aplicação na modelagem de risco, como por exemplo, na estimação do Valor em Risco e do requerimento de capital. Este trabalho verifica a adequação de um procedimento proposto por McNeil e Frey [1999] para estimação do Valor em Risco e conseqüente requerimento de capital às principais séries financeiras de retornos do Brasil. Tal procedimento semi-paramétrico combina um modelo GARCH ajustado por pseudo máxima verossimilhança para estimação da volatilidade corrente com a Teoria de Valores Extremos para estimação das caudas da distribuição das inovações do modelo GARCH. O procedimento foi comparado através de backtestings com outros métodos mais comuns de estimação de VaR que desconsideram caudas pesadas das inovações ou a natureza estocástica da volatilidade. Concluiu-se que o procedimento proposto por McNeil e Frey [1999] mostrou melhores resultados, principalmente para eventos relacionados a movimentos negativos nos mercados . Futuros trabalhos consistirão no estudo de uma abordagem multivariada de grandes dimensões para estimação de VaR e requerimento de capital para carteiras de investimentos. / There is a strong evidence that financial return series are heavy-tailed, mostly in emerging markets. However, most of the risk models used by financial institutions are based in conditional or non-conditional normality, which reduces the accuracy of the estimates. The recent advances in Extreme Value Theory permit its application to risk measuring, such as Value at Risk and capital adequacy estimates. This work verifies the adequacy of a procedure proposed by McNeil and Frey [1999] to VaR and consequent capital requirement estimates for the main financial return series in Brazil. This semi parametric procedure combines a pseudo-maximumlikelihood fitting GARCH model to estimate the current volatility and the Extreme Value Theory (EVT) to estimate the tails of the innovations distribution of the GARCH model. Using backtestings the procedure was compared to other common methods of VaR estimation that disregard heavy tails of the innovations or the stochastic nature of the volatility. The procedure proposed by McNeil and Frey [1999] showed better results, mostly for negative events in the financial market2 . Further works will consist of studying a high dimensional multivariate approach to estimate VaR and capital requirements for portfolios of investment instruments.

Ce travail se compose de trois parties consacrées à différents aspects des modèles ARCH (AutoRegressive Conditionally Heteroskedastic) quantiles. Dans ces modèles, l’hétéroscédasticité conditionnelle est à prendre dans un sens très large, et affecte de fa¸ con potentiellement différenciée tous les quantiles conditionnels (et donc la loi conditionnelle elle-même), et non seulement, comme dans les modèles ARCH classiques, l’échelle conditionnelle.<p><p>La première partie étudie les problèmes de Value-at-Risk (VaR) dans les séries financières ainsi modélisées. Les approches traditionnelles présentent une caractéristique discutable, que nous relevons, et à laquelle nous apportons une correction fondée sur les lois résiduelles. Nous pensons que les fondements de cette nouvelle approche sont plus solides, et permettent de prendre en compte le fait que le comportement des processus empiriques résiduels (REP) des processus ARCH, contrairement à celui des REP des processus ARMA, continue à dépendre de certains des paramètres du modèle.<p><p>La seconde partie approfondit l’étude générale des processus empiriques résiduels (REP) des processus ARCH dans l’optique de la régression quantile (QR) au sens de Koenker et Bassett (Econometrica 1978). La représentation de Bahadur des estimateurs QR, et dont découle la propriété de tension asymptotique des REP, est établie.<p><p>Finalement, dans la troisième partie, nous mettons en évidence la nature semi-paramétrique des modèles ARCH quantiles, et l’invariance, sous l’action de certains groupes de transforma-tions, des sous-modèles obtenus en fixant la valeur des paramètres. Cette structure de groupe permet la construction de méthodes d’inférence invariantes qui, dans l’esprit des résultats de Hallin and Werker (Bernoulli 2003) préservent l’optimalité au sens semi-paramétrique. Ces méthodes sont fondées sur les rangs et les signes résiduels. Nous développons en particulier les R-estimateurs des modèles considérés et étudions leurs performances. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

[pt] A dissertação trata da comparação de dois métodos de estimação para modelos de séries temporais com volatilidade estocástica. Um dos métodos é baseado em inferência Bayesiana e depende de simulações enquanto o outro utiliza máxima verossimilhança para o processo de estimação. A comparação é feita tanto com séries temporais artificialmente geradas como também com séries financeiras reais. O objetivo é mostrar que os dois métodos apresentam resultados semelhantes, sendo que o segundo método é significativamente mais rápido do que o primeiro.

[pt] Nesta tese são desenvolvidos três ensaios que avaliam os riscos relativos a alguns países emergentes. No primeiro ensaio, por meio do uso de modelos da família GARCH, verificou-se que o aumento dos pesos relativos atribuídos às observações mais antigas em função do aumento do horizonte de previsão resulta em melhores estimativas de volatilidade. Por meio da utilização de sete modelos de previsão de volatilidade e séries de retornos de ativos do mercado financeiro brasileiro (ações de Petrobrás e Vale, índice Ibovespa, taxa de câmbio Real/Dólar, taxa de juros de 1 ano e taxa de juros de 3 anos de títulos de dívida do governo brasileiro emitidos em reais) compararam-se as estimativas obtidas na amostra (in-sample) com as observações fora da amostra (out-of-sample). Com base nesta comparação, constatou-se que as melhores estimativas de previsão de volatilidade foram obtidas, predominantemente, por dois modelos que permitem que seus parâmetros variem em função do horizonte de previsão: o modelo modificado EGARCH e o modelo ARLS. Concluiu-se que a utilização de modelos de previsão de volatilidade tradicionais, os quais mantêm inalterados os pesos relativos atribuídos às observações antigas e recentes, independente do horizonte de previsão, mostrou-se inapropriada. No segundo ensaio comparou-se os desempenhos dos modelos de memória longa (FIGARCH) e curta (GARCH) na previsão de value-at-risk (VaR) e expected shortfall (ES) para múltiplos períodos à frente para seis índices de ações de mercados emergentes. Utilizou-se, para dados diários de 1999 a 2014, uma adaptação da simulação de Monte Carlo para estimar previsões de VaR e ES para 1, 10 e 20 dias à frente, usando modelos FIGARCH e GARCH para quatro distribuições de erros. Os resultados sugerem que, em geral, os modelos FIGARCH melhoram a precisão das previsões para horizontes mais longos; que a distribuição dos erros pode influenciar a decisão de escolha do melhor modelo; e que apenas para os modelos FIGARCH houve redução do número de subestimações do VaR verdadeiro com o aumento do horizonte de previsão. Com relação ao terceiro ensaio, percebeu-se que aadministração de riscos é um assunto que há muito tempo já faz parte do dia-adia das instituições financeiras e não financeiras, todavia não é comum a utilização de métricas de risco na Administração Pública. Considerando a existência dessa lacuna e a importância do tema para uma adequada gestão dos recursos públicos, principalmente para países emergentes, esse terceiro ensaio teve como propósitos estimar, em um único valor, o risco de liquidez de um Órgão Público, a Marinha do Brasil, e identificar as fontes desse risco. Para isso, utilizou-se o exposure-based Cash-Flow-at-Risk (CFaR) model, o qual, além de resumir a estimação do risco de liquidez a um único valor, ajuda no gerenciamento desse risco pelo fornecimento de informações adicionais sobre a exposição do fluxo de caixa da organização a diversos fatores de risco. Usando dados trimestrais do período compreendido entre o primeiro trimestre de 1999 ao quarto trimestre de 2013, identificaram-se as taxas de câmbio real/dólar, dólar/libra, a taxa SELIC, a Necessidade de Financiamento do Setor Público e a taxa de inflação dos Estados Unidos como os fatores de risco macroeconômicos e de mercado que impactam o fluxo de caixa da Marinha, bem como se calculou seu CFaR com 95 por cento de nível de confiança para o período de um trimestre à frente. / [en] In this thesis we develop three essays on risk management in some emerging countries. On the first one, using models of the GARCH family, we verified that the increase in relative weights assigned to the earlier observations due to the increase of the forecast horizon results in better estimates of volatility. Through the use of seven forecasting models of volatility and return series of financial markets assets (shares of Petrobras and Vale, Bovespa index, exchange rate Real/Dollar, 1-year and 3 years interest rates of Brazilian Government bonds issued in Reais) the estimates obtained in the sample (in-sample) were compared with observations outside the sample (out-of-sample). Based on this comparison, it was found that the best estimates of expected volatility were obtained predominantly by two models that allow its parameters to vary depending on the forecast horizon: the modified EGARCH model (exponential generalized autoregressive conditional heteroskedastic) and the ARLS model proposed by Ederington and Guan (2005). We conclude that the use of traditional forecasting models of volatility, which keeps unchanged relative weights assigned to both old and new observations, regardless of the forecast horizon, was inappropriate. On the second essay we compared the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock índices. We used daily data from 1999 to 2014 and an adaptation of the Monte Carlo simulation to estimate VaR and ES forecasts for multiple steps ahead (1, 10 and 20 days ), using FIGARCH and GARCH models for four errors distributions. The results suggest that, in general, the FIGARCH models improve the accuracy of forecasts for longer horizons; that the error distribution used may influence the decision about the best model; and that only for FIGARCH models the occurrence of underestimation of the true VaR is less frequent with increasing time horizon. Regarding the third essay, we realized that risk management is a subject that has long been part of the day-to-day activities of financial and nonfinancial institutions, yet the use of risk metrics is not common among public agencies. Considering this gap, and the importance of the issue for the proper management of public resources, the purpose of this third essay is to estimate, in a single value, the liquidity risk of a public agency, in this case, the Brazilian Navy, and to identify the sources of risk. To do this, the exposure-based Cash-Flow-at- Risk (CFaR) model has been developed, which, in addition to summarizing the liquidity risk estimation in a single value, helps in managing risk by providing additional information about the exposure of the organization s cash flow to various risk factors. Using quarterly data for the period between the first quarter of 1999 and the fourth quarter of 2013, the macroeconomics and market risk factors that impact the Navy s cash flow were identified. Moreover, the CFaR was calculated at a 95 percent confidence level for a period of one quarter ahead.

Cette thèse porte sur l'étude de la modélisation et estimation de la dépendance des portefeuilles de risques et l'estimation du risque agrégé. Dans le Chapitre 2, nous proposons une nouvelle méthode pour estimer les quantiles de haut niveau pour une somme de risques. Elle est basée sur l'estimation du rapport entre la VaR de la somme et la VaR du maximum des risques. Nous utilisons des résultats sur les fonctions à variation régulière. Nous comparons l'efficacité de notre méthode avec quelques estimations basées sur la théorie des valeurs extrêmes, sur plusieurs modèles. Notre méthode donne de bons résultats lors de l'approximation de la VaR à des niveaux élevés lorsque les risques sont fortement dépendants et au moins l'un des risques est à queue épaisse. Dans le Chapitre 3, nous proposons une procédure d'estimation pour la distribution d'un risque agrégé basée sur la copule échiquier. Elle permet d'obtenir de bonnes estimations à partir d'un petit échantillon de la loi multivariée et une connaissance complète des lois marginales. Cette situation est réaliste pour de nombreuses applications. Les estimations peuvent être améliorées en incluant dans la copule échiquier des informations supplémentaires (sur la loi d'un sous-vecteur ou sur des probabilités extrêmes). Notre approche est illustrée par des exemples numériques. Finalement, dans le Chapitre 4, nous proposons un estimateur de la mesure spectrale basé sur l'estimation à noyau de la densité de la mesure spectrale d'une distribution à variation régulière bivariée. Une extension de notre méthode permet d'estimer la mesure spectrale discrète. Certaines propriétés de convergence sont obtenues / This thesis comprises three essays on estimation methods for the dependence between risks and its aggregation. In the first essay we propose a new method to estimate high level quantiles of sums of risks. It is based on the estimation of the ratio between the VaR (or TVaR) of the sum and the VaR (or TVaR) of the maximum of the risks. We use results on regularly varying functions. We compare the efficiency of our method with classical ones, on several models. Our method gives good results when approximating the VaR or TVaR in high levels on strongly dependent risks where at least one of the risks is heavy tailed. In the second essay we propose an estimation procedure for the distribution of an aggregated risk based on the checkerboard copula. It allows to get good estimations from a (quite) small sample of the multivariate law and a full knowledge of the marginal laws. This situation is realistic for many applications. Estimations may be improved by including in the checkerboard copula some additional information (on the law of a sub-vector or on extreme probabilities). Our approach is illustrated by numerical examples. In the third essay we propose a kernel based estimator for the spectral measure density of a bivariate distribution with regular variation. An extension of our method allows to estimate discrete spectral measures. Some convergence properties are obtained

The aim of the thesis was to develop an effective hedge strategy for a Czech importing pharmaceutical company. To this goal, I used both theoretical knowledge from the first and second chapter, and internal data of the company. Particularly, this thesis is dedicated to its management, however, it may also inspire those who are interested in this issue.

Diploma thesis Business risks in insurance and their quantification describes the business risks to which insurance companies are exposed in their activities. Thesis is focused on market risk and quantification of market risk in insurance companies. It includes determination of the specifications for the activities of insurance companies, regulation and characteric of business risks in insurance. Large part of the thesis deals with the method of Value at Risk as a tool to measure market risk as well as individual methods to calculate it. In the conclusion, thesis describes the processes of quantification of market risk in Generali PPF Holding and in Česká poisťovňa, which gives a practical insight into the issues of market risk in insurance companies.

We examine the impact of Mergers &amp; Acquisitions on credit- and investment risk using a sample of 402 acquisitions by 215 Swedish firms from 2000 to 2020. We find significant evidence that, on average, M&amp;A increases the credit risk and inversely decreases the investment risk of the acquiring firm. Our results indicate that firm credit risk however is positively correlated with investment risk. After controlling for specific deal- and firm characteristics, our findings suggest that managerial hubris decreases the level of credit risk and increases the level of investment risk in acquiring firms. Our results are consistent with the asymmetric information hypothesis that managers may exploit the volatility of their stock price to hide risk-increasing activities. We also observe that acquirers with high pre-deal credit risk undertake acquisitions that decrease credit risk and increase investment risk. We find no significant impact from neither method of payment nor valuation errors.

Financial institutions manage numerous portfolios whose risk must be managed continuously, and the large amounts of data that has to be processed renders this a considerable effort. As such, a system that autonomously detects anomalies in the risk measures of financial portfolios, would be of great value. To this end, the two econometric models ARMA-GARCH and EWMA, and the two machine learning based algorithms LSTM and HTM, were evaluated for the task of performing unsupervised anomaly detection on the streaming time series of portfolio risk measures. Three datasets of returns and Value-at-Risk series were synthesized and one dataset of real-world Value-at-Risk series had labels handcrafted for the experiments in this thesis. The results revealed that the LSTM has great potential in this domain, due to an ability to adapt to different types of time series and for being effective at finding a wide range of anomalies. However, the EWMA had the benefit of being faster and more interpretable, but lacked the ability to capture anomalous trends. The ARMA-GARCH was found to have difficulties in finding a good fit to the time series of risk measures, resulting in poor performance, and the HTM was outperformed by the other algorithms in every regard, due to an inability to learn the autoregressive behaviour of the time series. / Finansiella institutioner hanterar otaliga portföljer vars risk måste hanteras kontinuerligt, och den stora mängden data som måste processeras gör detta till ett omfattande uppgift. Därför skulle ett system som autonomt kan upptäcka avvikelser i de finansiella portföljernas riskmått, vara av stort värde. I detta syftet undersöks två ekonometriska modeller, ARMA-GARCH och EWMA, samt två maskininlärningsmodeller, LSTM och HTM, för ändamålet att kunna utföra så kallad oövervakad avvikelsedetektering på den strömande tidsseriedata av portföljriskmått. Tre dataset syntetiserades med avkastningar och Value-at-Risk serier, och ett dataset med verkliga Value-at-Risk serier fick handgjorda etiketter till experimenten i denna avhandling. Resultaten visade att LSTM har stor potential i denna domänen, tack vare sin förmåga att anpassa sig till olika typer av tidsserier och för att effektivt lyckas finna varierade sorters anomalier. Däremot så hade EWMA fördelen av att vara den snabbaste och enklaste att tolka, men den saknade förmågan att finna avvikande trender. ARMA-GARCH hade svårigheter med att modellera tidsserier utav riskmått, vilket resulterade i att den preseterade dåligt. HTM blev utpresterad utav de andra algoritmerna i samtliga hänseenden, på grund utav dess oförmåga att lära sig tidsserierna autoregressiva beteende.

With the substantial size of the interest rate markets, the importance of accurate pricing, risk measurement and performance attribution can not be understated. However, the models used on the markets often have underlying issues with capturing the market's fundamental behavior. With this thesis, we aim to improve the pricing, risk measurement, and performance attribution of interest rate swap portfolios. The paper is divided into six main parts, by subject, to aid in achieving these goals. To begin with, we validate all cash flows with SEB to increase the validity of the results. Next, we implement an optimization-based model developed by Jörgen Blomvall to estimate multiple yield curves.  By considering innovations of the daily in-sample curves, risk factors are computed with principal component analysis. These risk factors are then used to simulate one-day and ten-day ahead scenarios for the multiple yield curves using a Monte Carlo method. Given these simulated scenarios, risk measures are then computed. When backtested, these risk measurements give an indication on the overall accuracy of the methodology, including the estimated curves, the derived risk factors, and the simulation methodology. Along with the simulation, on each out-of-sample day, monetary performance attribution for the portfolios is also performed. The performance attribution indicates what drives the value change in the portfolio. This can be used in order to evaluate the estimated yield curves and derived risk factors. The risk measurement and performance attribution is done for three different portfolios of interest rate swaps on the EUR, USD, and SEK markets. However, the risk factors are only estimated for EUR data and used for all portfolios.  The main difference to previous work in this area is that, for all implementations, a multiple yield curve environment is studied. Different PCA algorithms are evaluated to increase the precision and speed of the risk factor calculation. Mean reverting risk factors are developed in the simulation framework, along with a Latin hypercube sampling method accounting for dependence in the random variables to reduce variance. We also study the EUR and SEK markets, while the focus in previous literature is on the USD market. Lastly, we calculate and backtest the risk measures value-at-risk and expected shortfall for one-day and ten-day horizons. Four different PCA methods are implemented, a bidiagonal divide and conquer SVD algorithm, a randomized SVD method, an Arnoldi method, and an optimization-based PCA algorithm. We opt to use the first one due to high accuracy and the ability to calculate all eigenpairs. However, we recommend to use the Arnoldi method in future implementations and to further study the optimization-based method. The Latin hypercube sampling with dependence method is able to produce random variables with the same correlation as the input variables. In the simulation, we are able to produce results that pass all backtests for the risk measures considering the USD portfolio. For the EUR and SEK portfolios, it is shown that the risk measures are too conservative. The results of the mean reversion method indicate that it produces slightly less conservative estimates for the ten-day horizon. In the performance attribution, we show that we are able to produce results with small error terms, therefore indicating accurately estimated term structures, risk factors, and pricing. We conclude that we are partly able to fulfill the stated purpose of this thesis due to having produced accurate pricing and satisfactory performance attribution results for all portfolios, and stable risk measures for the USD portfolio. However, it is not possible to state with certainty that improved risk measurements have been achieved for the EUR and SEK portfolios. Although, we present several alternative approaches to remedy this in future implementations.