nÃo hà / Este trabalho analisa durante o perÃodo de 01/2008 a 12/2011 o risco de mercado de seis Ãndices setoriais da Bolsa de Valores de SÃo Paulo (BM&FBovespa): o Ãndice imobiliÃrio (IMOB), o Ãndice de energia elÃtrica (IEE), o Ãndice de consumo (ICON), o Ãndice do setor industrial (INDX), o Ãndice financeiro (IFNC) e o Ãndice setorial de telecomunicaÃÃes (ITEL). AtravÃs da mÃtrica Value-at-Risk (VaR) estimam-se quatro modelos. Dois desses modelos sÃo ditos incondicionais no que se refere à variÃncia: o VaR Gaussiano Incondicional, admitindo que os retornos seguem uma distribuiÃÃo normal, e o VaR Best Fitting Incondicional, construÃdo a partir da distribuiÃÃo de probabilidades que melhor se ajusta Ãs sÃries de retornos. Os outros dois modelos sÃo chamados de condicionais, assumindo que a volatilidade varia ao longo do tempo. Os modelos autoregressivos do tipo GARCH sÃo utilizados para estimar a variÃncia condicional de cada Ãndice, possibilitando a estimaÃÃo do VaR Gaussiano Incondicional e do VaR Best Fitting Incondicional. Em seguida, realizam-se backtestings dos modelos de VaR, revelando a superioridade dos modelos condicionais. Por fim, atravÃs de grÃficos de Balzer, observou-se a performance dos Ãndices por meio de confrontos entre eles. Foi constatado que, para o perÃodo analisado, o IEE vence todos os embates feitos com os demais Ãndices, apresentando a melhor relaÃÃo risco x retorno. O setor imobiliÃrio, representado pelo IMOB, perde todos os confrontos. / This work analyzes during the period between 2008/01 and 2011/12 the market risk of six sectorial indexes from the SÃo PauloÂs Stock Market (BM&FBovespa): the real state index (IMOB), the eletric power index (IEE), the consumption index (ICON), the industrial sector index (INDX), the financial index (IFNC) and the telecommunications sector index (ITEL). Throughout the Value-at-Risk metric (VaR), four models are estimated. Two of those models are called unconditional, due to its variance: the Unconditional Gaussian VaR, that admits that the returns follow a normal distribution, and the Unconditional Best Fitting VaR, built from the distribution of probabilities that better fits to the returns series. The other two models are called conditionals, assuming that the volatility changes along the time. The GARCH autoregressive models are used to estimate the conditional variance of each index, allowing an estimation of the Unconditional Gaussian VaR and the Unconditional Best Fitting VaR. Afterwards, the VaR models backtestings are realized, revealing the conditional models superiority. Finally, throughout the BalzerÂs graphics, the indexes performances were observed over the confrontations between them. It was found that, for the analyzed period, the IEE wins every confrontation against the all other indexes, showing the best relation risk x return. The real state index sector, represented by the IMOB, lost all the confronts.

The importance of sound financial risk management has become increasingly emphasised in recent years, especially with the financial crisis of 2007-08. The Basel Committee sets the international standards and regulations for banks and financial institutions, and in particular under market risk, they prescribe the internal application of the measure Value-at-Risk. However, the most established non-parametric Value-at-Risk model, historical simulation, has been criticised for some of its unrealistic assumptions. This thesis investigates alternative approaches for estimating non-parametric Value-at-Risk, by examining and comparing the capability of three counterbalancing weighting methodologies for historical simulation: an exponentially decreasing time weighting approach, a volatility updating method and, lastly, a more general weighting approach that enables the specification of central moments of a return distribution. With real financial data, the models are evaluated from a performance based perspective, in terms of accuracy and capital efficiency, but also in terms of their regulatory suitability, with a particular focus on the Swedish market. The empirical study shows that the capability of historical simulation is improved significantly, from both performance perspectives, by the implementation of a weighting methodology. Furthermore, the results predominantly indicate that the volatility updating model with a 500-day historical observation window is the most adequate weighting methodology, in all incorporated aspects. The findings of this paper offer significant input both to existing research on Value-at-Risk as well as to the quality of the internal market risk management of banks and financial institutions. / Betydelsen av sund finansiell riskhantering har blivit alltmer betonad på senare år, i synnerhet i och med finanskrisen 2007-08. Baselkommittén fastställer internationella normer och regler för banker och finansiella institutioner, och särskilt under marknadsrisk föreskriver de intern tillämpning av måttet Value-at-Risk. Däremot har den mest etablerade icke-parametriska Value-at-Risk-modellen, historisk simulering, kritiserats för några av dess orealistiska antaganden. Denna avhandling undersöker alternativa metoder för att beräkna icke-parametrisk Value-at‑Risk, genom att granska och jämföra prestationsförmågan hos tre motverkande viktningsmetoder för historisk simulering: en exponentiellt avtagande tidsviktningsteknik, en volatilitetsuppdateringsmetod, och slutligen ett mer generellt tillvägagångssätt för viktning som möjliggör specifikation av en avkastningsfördelnings centralmoment. Modellerna utvärderas med verklig finansiell data ur ett prestationsbaserat perspektiv, utifrån precision och kapitaleffektivitet, men också med avseende på deras lämplighet i förhållande till existerande regelverk, med särskilt fokus på den svenska marknaden. Den empiriska studien visar att prestandan hos historisk simulering förbättras avsevärt, från båda prestationsperspektiven, genom införandet av en viktningsmetod. Dessutom pekar resultaten i huvudsak på att volatilitetsuppdateringsmodellen med ett 500 dagars observationsfönster är den mest användbara viktningsmetoden i alla berörda aspekter. Slutsatserna i denna uppsats bidrar i väsentlig grad både till befintlig forskning om Value-at-Risk, liksom till kvaliteten på bankers och finansiella institutioners interna hantering av marknadsrisk.

The thesis compares GARCH volatility models and Stochastic Volatility (SV) models with Student's t distributed errors and its empirical forecasting per- formance of Value at Risk on five stock price indices: S&P, NASDAQ Com- posite, CAC, DAX and FTSE. It introduces in details the problem of SV models Maximum Likelihood examinations and suggests the newly devel- oped approach of Efficient Importance Sampling (EIS). EIS is a procedure that provides an accurate Monte Carlo evaluation of likelihood function which depends upon high-dimensional numerical integrals. Comparison analysis is divided into in-sample and out-of-sample forecast- ing performance and evaluated using standard statistical probability back- testig methods as conditional and unconditional coverage. Based on empirical analysis thesis shows that SV models can perform at least as good as GARCH models if not superior in forecasting volatility and parametric VaR. 1

In this paper we will be studying and backtesting a particular investment strategy by buying and holding dividend stocks. We think dividend stock is an important type of investment to investors and portfolio managers because of its cash implications, especially in a high volatility equity market. Furthermore, we think that consistency in a company’s ability and willingness in distributing dividends to its shareholders is a strong indicator of its financial strength and operational success. How portfolio managers should pick the best performing dividend stocks would then become an important issue. In this paper, we will be testing the historical performance of a portfolio of dividend stocks that we construct and adjust based on a list of parameters associated with companies’ operational performance, cash position, and dividend yield. Hence, the main way we select stocks in the portfolio is based on fundamental analysis. Our research is conducted relying exclusively on the Wharton Research Data Services database (WRDS). In addition to evaluating the investment attractiveness of our portfolio, the strategy may also have implications regarding several other topics including the semi-strong form market efficiency and active portfolio management. Therefore, this paper covers also potential benefits to be gained from the strategy other than its investment payoff.

The diploma thesis Risks of using VaR models for portfolio management is focused on estimation of the portfolio VaR using basic and modified methods. The goal of this thesis is to point out some weakness of the basic methods and to demonstrate the estimation of VaR using improved methods to overcome these problems. The analysis will be perform theoretically and in practice. Only market risk will be the subject of the study. Several simulation and parametric methods will be introduced.

Neste trabalho, apresentamos alguns métodos para avaliação das previsões do Valor em Risco (VaR). Estes métodos testam um tipo de eficiência, denominada cobertura condicional correta. O poder empírico e a probabilidade do erro de tipo I são comparados através de simulações de Monte Carlo. Além disso, avaliamos um novo método de previsão do VaR, o qual é aplicado nos retornos diários do Ibovespa. Os resultados obtidos mostram que a nova classe de testes, baseados em uma regressão Weibull discreta, em muitos casos, tem poder empírico maior comparando com outros métodos apresentados neste trabalho. / In this paper, we present some procedures for assessing forecasts for the Value at Risk (VaR). These procedures test a type of efficiency, referred as correct conditional coverage. The empirical power and type I error probability are compared through a Monte Carlo simulation. The results show that a new class of tests based on a discrete Weibull regression in most cases has greater power empirical to other methods available in this paper.

The subject of my thesis is technical analysis - creation of an investment strategies. The theoretical part describes the theoretical background relating to technical analysis and indicators. The practical part map the current situation in the environment of investing in forex - comparing brokers, choice of platform, etc. The solution contains a description of the investment strategies that have been programmed in Meta Quotes Language 4 and tested and optimized using genetic algorithms in platform MetaTrader 4. In my thesis is also calculated the interdependence of investment strategies.

This master thesis deals with design of automated trading system for currency trading. The thesis includes testing of this system on historical data and its optimization for achieving stability and profit. Thesis is divided into theoretical, analytical and practical part. The goal of the first part is to provide theoretical knowledge of the currency market, methods for analysis of the currency market and to define fundamental terminology. Second part describes properties of technical analysis indicators and introduces optimization and testing methods for automated trading systems. The last part is focused on design, implementation, optimization and testing of the automated trading systems.

This thesis includes four essays on risk assessment with financial econometrics models. The first chapter provides Monte Carlo evidence on the efficiency gains obtained in GARCH-base estimations of VaR and ES by incorporating dependence information through copulas and subsequently using full maximum likelihood (FML) estimates. First, individual returns series are considered; in this case, the efficiency gain stems from exploiting the relationship with another returns series using a copula model. Second, portfolio returns series obtained as a linear combination of returns series related with a copula model, are considered; in this case, the efficiency gain stems from using FML estimates instead of two-stage maximum likelihood estimates. Our results show that, in these situations, using copula models and FML leads to a substantial reduction in the mean squared error of the VaR and ES estimates (around 50\% when there is a medium degree of dependence between returns) and a notable improvement in the performance of backtesting procedures. Then, chapter 2 analyzes the impact of the COVID-19 pandemic on the conditional variance of stock returns. In this work, we look at this effect from a global perspective, employing series of major stock market and sector indices. We use the Hansen’s Skewed-t distribution with EGARCH extended to control for sudden changes in volatility. We oversee the COVID-19 effect on the VaR. Our results show that there is a significant sudden shift up in the return distribution variance post the announcement of the pandemic, which must be explained properly to obtain reliable measures for financial risk management. In chapter 3, we assess VaR and ES estimates assuming different models for standardised returns such as Cornish-Fisher and Gram-Charlier polynomial expansions, and well-known parametric densities such as normal, skewed Student-t family of Zhu and Galbraith (2010), and Johnson. This paper aims to check whether models based on polynomial expansions outperform the parametric ones. We carry out the model performance comparison in two stages. First, a backtesting analysis for VaR and ES, and second, using the loss function approach. Our backtesting results in our empirical exercise suggest that all distributions, but the normal, perform quite well in VaR and ES estimations. Regarding the loss function analysis, we conclude that the Cornish-Fisher expansion usually outperforms the others in VaR estimation, but Johnson distribution is the one that provides the best ES estimates in most cases. Although the differences among all distributions (excluding the normal) are not great. Finally, chapter 4 assess whether accounting for asymmetry and tail-dependence in returns distributions may help to identify more profitable investment strategies in asset portfolios. Three copula models are used to parameterize the multivariate distribution of returns: Gaussian, C-Vine and R-Vine copulas. Using data from equities and ETFs from the US market, we find evidence that, for portfolios of 48 constituents or less, the R-Vine copula is able to produce more profitable portfolios with respect to both, the C-Vine and Gaussian copulas. However, for portfolios of 100 assets, performance of R- and C-Vine copulas is quite similar, being both better than the Gaussian copula.

Ce travail de recherche propose de nouveaux outils pour améliorer la prise en compte des caractéristiques spécifiques des hedge funds, dans l'évaluation de leur risque et de leur performance. Tout d'abord, nous mettons en évidence l'intérêt des développements basés sur la théorie des valeurs extrêmes pour analyser et quantifier le risque extrême des hedge funds. Une procédure de backtesting démontre que la valeur en risque, estimée à partir de la distribution de Pareto généralisée s'ajustant aux pertes extrêmes (VaREVT), est plus fiable que les mesures de risque usuelles. Puis, nous suggérons un nouvel indicateur de performance, lequel permet de prendre en compte la non normalité des distributions de rentabilités des hedge funds ainsi que, le niveau de rentabilité minimum acceptable de l'investisseur. Enfin, quatre modèles ont été construits en vue de déterminer les principaux facteurs explicatifs de l'évolution de la rentabilité journalière des stratégies alternatives. Ce dernier point donne l'occasion de mettre en évidence les avantages de la méthode de régression PLS pour identifier les facteurs pertinents. Cette recherche offre, non seulement, des résultats intéressants pour mieux comprendre le monde des hedge funds mais également, de nouvelles perspectives pour l'évaluation du risque et de la performance des autres actifs financiers ayant une distribution de rentabilités leptokurtique et asymétrique.