The last decade has witnessed a great deal of research in modelling volatility of financial asset returns, expressed by time-varying variances and covariances. The importance of modelling volatility lies in the dependence of any financial investment decision on the expected risk and return as formalized in classical asset pricing theory. Precise evaluation of volatilities is a compulsory step in order to perform correct options pricing according to recent theories of the term structure of interest rates and for the construction of dynamic hedge portfolios. Models of time varying volatility represent an important ground for the development of new estimation and forecasting techniques for situations not reconcilable with the Gaussian or, more generally, a linear time series framework. This is particularly true for the statistical analysis of time series with long range dependence in a nonlinear framework. The aim of this thesis is to introduce parametric nonlinear time series models with long memory, with particular emphasis on volatility models, and to provide a methodology which yields asymptotically exact inference on the parameters of the models. The importance of these results stems from: (i) rigorous asymptotics was lacking from the stochastic volatility literature; (ii) the statistical literature does not cover the analysis of the asymptotic behaviour of quadratic forms in nonlinear non-Gaussian variates that characterizes our problem.

This paper has four chapters. The first chapter serves as an introduction. The second chapter studies the transaction fees in the bitcoin system. The transaction fees and transaction volume in the bitcoin system increase whenever the network is congested and results from a simple VAR show that it is indeed the case. To account for the empirical findings, we build a model where users and miners together determine the transaction fee and transaction volume endogenously. Even though the fluctuating transaction fee mechanism in bitcoin introduces the extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fix transaction fee rate. The second chapter studies the time-varying price dispersion among different bitcoin exchanges. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility. The results show that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion. The third chapter studies the relationship between connections and returns in the bitcoin investor network. Using transaction data from the bitcoin blockchain, we reach three conclusions. First, on average, the annualized returns of connected addresses in the network are 20.75% above those of their unconnected peers. Second, returns also differ among those connected addresses. By dividing the connected ad- dresses into ten deciles based on their centrality, we find that addresses in the two most-connected deciles earn higher returns than the other connected addresses. Third, eigenvector centrality is more related than degree centrality to higher returns, implying that quality of connections matters. / Doctor of Philosophy / This paper has four chapters. The first chapter serves as an introduction. The second chapter studies the transaction fees in the bitcoin system. The transaction fees in the bitcoin system can fluctuate given the amount of unconfirmed transactions in the bitcoin network. Our results show that the transaction fees and transaction volume in the bitcoin system increase whenever the network is congested. To account for this findings, we build a model and show that users and miners together can determine the transaction fee and transaction volume. Even though the fluctuating transaction fee mechanism in bitcoin introduces the extra cost of uncertainty to users, a back-of- envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fix transaction fee rate. The second chapter studies the price dispersion among different bitcoin exchanges. Our results show that transaction fees and bitcoin price growth can be important explanatory factors for the price dispersion among different bitcoin exchanges. The third chapter studies the relationship between connections and returns in the bitcoin investor network. Using transaction data from the bitcoin blockchain, we reach three conclusions. First, on average, those connected addresses in the network earn higher returns than their unconnected peers. Second, returns also differ among those connected addresses. By dividing the connected addresses into ten groups based on their centrality, we find that addresses in the two most-connected groups earn higher returns than the other connected addresses. Third, eigenvector centrality, which measures the quality of connections, is more related than degree centrality, which measures the quantity of connections, to higher returns, implying that quality of connections matters.

O objetivo deste trabalho foi estudar o comportamento da volatilidade do preço da soja negociada em contratos futuros na BM&FBOVESPA (série SFI). O estudo foi realizado por meio da comparação entre duas abordagens: na primeira, foi utilizada a série de retornos absolutos da série em questão para representar a volatilidade da mesma, que se mostrou persistente ao longo do tempo, comprovando o fato de que a série possui o comportamento de memória longa. Por ter apresentado tal comportamento, fez-se necessária a utilização de modelos ARFIMA (\"Autorregressivos Fracionários Integrados de Médias Móveis\") estes, que são capazes de capturar de maneira efetiva tal comportamento. Ainda dentro desta abordagem, os modelos foram estimados de duas maneiras distintas: a primeira, em que todos os parâmetros foram estimados simultaneamente e a segunda, em que primeiramente foi estimado o parâmetro de memória longa, diferenciada a série e, posteriormente, foram ajustados os modelos ARIMA nos dados diferenciados. Por fim, a segunda abordagem utilizada no trabalho é a mais comum em pesquisas acadêmicas: foi realizada a estimação dos modelos GARCH (\"Autorregressivos Generalizados de Heteroscedasticidade Condicional\") diretamente na série de retornos. Neste estudo, concluímos que a primeira abordagem se mostrou mais eficiente, dados os critérios de comparação utilizados. / The purpose of this article was to study the volatility of the soybean price traded in futures contracts on the BM&FBOVESPA (SFI series). The study was conduct by comparison between two approaches: first, was use the series of absolute returns of the respective series, to represent its volatility, which was persistent over time, proving the fact that the series has a long memory behavior. Because of such behavior, it was necessary to use ARFIMA models (\"Autoregressive Fractional Integrated Moving Average\"), which are able to capture effectively such behavior. Still using this approach, the models were estimate in two different ways: first, which all parameters were estimate simultaneously, and the second one, that was first estimated the long memory parameter, differentiated the series and, later, adjusted the ARIMA models in differentiated data. Finally, the second approach used in this work is the most common in academic research: the estimation of GARCH models (\"Generalized Autoregressive Conditional Heretoscskedasticity\") directly in the returns series of the studied series. In this study, we conclude that the first approach was more effective, given the comparison criteria used.

O objetivo deste trabalho foi estudar o comportamento da volatilidade do preço da soja negociada em contratos futuros na BM&FBOVESPA (série SFI). O estudo foi realizado por meio da comparação entre duas abordagens: na primeira, foi utilizada a série de retornos absolutos da série em questão para representar a volatilidade da mesma, que se mostrou persistente ao longo do tempo, comprovando o fato de que a série possui o comportamento de memória longa. Por ter apresentado tal comportamento, fez-se necessária a utilização de modelos ARFIMA (\"Autorregressivos Fracionários Integrados de Médias Móveis\") estes, que são capazes de capturar de maneira efetiva tal comportamento. Ainda dentro desta abordagem, os modelos foram estimados de duas maneiras distintas: a primeira, em que todos os parâmetros foram estimados simultaneamente e a segunda, em que primeiramente foi estimado o parâmetro de memória longa, diferenciada a série e, posteriormente, foram ajustados os modelos ARIMA nos dados diferenciados. Por fim, a segunda abordagem utilizada no trabalho é a mais comum em pesquisas acadêmicas: foi realizada a estimação dos modelos GARCH (\"Autorregressivos Generalizados de Heteroscedasticidade Condicional\") diretamente na série de retornos. Neste estudo, concluímos que a primeira abordagem se mostrou mais eficiente, dados os critérios de comparação utilizados. / The purpose of this article was to study the volatility of the soybean price traded in futures contracts on the BM&FBOVESPA (SFI series). The study was conduct by comparison between two approaches: first, was use the series of absolute returns of the respective series, to represent its volatility, which was persistent over time, proving the fact that the series has a long memory behavior. Because of such behavior, it was necessary to use ARFIMA models (\"Autoregressive Fractional Integrated Moving Average\"), which are able to capture effectively such behavior. Still using this approach, the models were estimate in two different ways: first, which all parameters were estimate simultaneously, and the second one, that was first estimated the long memory parameter, differentiated the series and, later, adjusted the ARIMA models in differentiated data. Finally, the second approach used in this work is the most common in academic research: the estimation of GARCH models (\"Generalized Autoregressive Conditional Heretoscskedasticity\") directly in the returns series of the studied series. In this study, we conclude that the first approach was more effective, given the comparison criteria used.

There has been an increasing interest in the finance literature regarding the impact of transactions costs on US equity markets. The US empirical evidence indicates that transactions costs influence both trading volume (Atkins and Dyl (1997)) and asset returns (Amihud and Mendelson (1986)). Additionally, the theoretical finance literature also indicates that transactions costs affect equilibrium asset returns (Fisher (1994)). In this thesis we assess the impact of transactions costs on the UK equity markets, from four aspects. Firstly, we provide empirical support to the hypothesis that transactions costs affect the "holding period" of an asset in the portfolio of an investor. Secondly, we provide robust results showing that transactions costs affect equilibrium asset returns. Thirdly, we explain the variability of transactions costs with the use of information asymmetry, proxied by the variance of analysts' forecasts, in the spirit of Kim and Verrecchia (1994, 2001). Finally, we find that stock price and trading volume reaction to changes in the FTSE 100 list can be explained by liquidity effects, as proxied by the bid-ask spread. We provide overwhelming evidence, suggesting that transactions costs are important in UK equity markets.

O objetivo deste trabalho é apresentar e comparar diferentes métodos de modelagem da volatilidade (variância condicional) de séries temporais financeiras. O modelo ARFIMA é empregado para capturar o comportamento de memória longa observado na volatilidade de séries financeiras. Por sua vez, o modelo GARCH é utilizado para modelar a volatilidade variando no tempo destas séries. Finalmente, o modelo FIGARCH é utilizado para modelar a dinâmica dos retornos de séries temporais financeiras juntamente com sua volatilidade. Serão apresentados alguns estimadores para os parâmetros dos modelos estudados. Foram realizadas simulações dos três tipos de modelos com o objetivo de comparar o comportamento dos estimadores para diferentes valores dos parâmetros. Por fim, serão apresentadas aplicações em séries reais. / The goal of this project is to present and compare differents methods of modeling volatility (conditional variance) in financial time series. ARFIMA model is applied to capture long memory behavior of volatility in financial time series. GARCH model is used to model the temporal variation in financial volatility. Finally, FIGARCH model is used to model dynamic of financial time series returns as well as its volatility behavior. We present some estimators for the studied models. Estimators behavior of the three types of models for different parameters is assessed through a simulation study. At last, applications to real data are presented.

In this dissertation, I explore the impact of idiosyncratic risk on asset returns. The first essay examines how idiosyncratic risk affects the cross-section of stock returns. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size effect, value premium, return momentum and short-term reversal to measure relative mispricing. I find that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or firm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these findings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost. The second essay studies the cross-sectional determinants of delta-hedged stock option returns with an emphasis on the pricing of volatility risk. We find that the average delta-hedged option returns are significantly negative for most stocks, and they decrease monotonically with both total and idiosyncratic volatility of the underlying stock. Our results are robust and cannot be explained by the Fama-French factors, market volatility risk, jump risk, or the effect of past stock return and volatility-related option mispricing. Our results strongly support a negative market price of volatility risk specification that is proportional to the volatility level. Reflecting this volatility risk premium, writing covered calls on high volatility stocks on average earns about 2% more per month than selling covered calls on low volatility stocks. This spread is higher when it is more difficult to arbitrage between stock and option. / text

O objetivo deste trabalho é apresentar e comparar diferentes métodos de modelagem da volatilidade (variância condicional) de séries temporais financeiras. O modelo ARFIMA é empregado para capturar o comportamento de memória longa observado na volatilidade de séries financeiras. Por sua vez, o modelo GARCH é utilizado para modelar a volatilidade variando no tempo destas séries. Finalmente, o modelo FIGARCH é utilizado para modelar a dinâmica dos retornos de séries temporais financeiras juntamente com sua volatilidade. Serão apresentados alguns estimadores para os parâmetros dos modelos estudados. Foram realizadas simulações dos três tipos de modelos com o objetivo de comparar o comportamento dos estimadores para diferentes valores dos parâmetros. Por fim, serão apresentadas aplicações em séries reais. / The goal of this project is to present and compare differents methods of modeling volatility (conditional variance) in financial time series. ARFIMA model is applied to capture long memory behavior of volatility in financial time series. GARCH model is used to model the temporal variation in financial volatility. Finally, FIGARCH model is used to model dynamic of financial time series returns as well as its volatility behavior. We present some estimators for the studied models. Estimators behavior of the three types of models for different parameters is assessed through a simulation study. At last, applications to real data are presented.

My thesis will focus on optimal investment decisions, especially those that are planned for longer investment horizon. I will review the literature, showing that changes in investment opportunities can alter the risk-return tradeoff over time and that asset return predictability has an important effect on the variance and correlation structure of returns on bonds, stocks and T bills across investment horizons. The main attention will be given to pension funds, which are institutional investors with relatively long investment horizon. I will find the term structure of risk-return tradeoff in the empirical part of this paper. Later on I will add some variables into the model and investigate whether it can improve the results. Finally the optimal investment strategies will be constructed for various levels of risk tolerance and the results will be compared with strategies of Czech pension funds. I am going to use data from Thomson Reuters Datastream, Wharton Research Data Services and additionally from some other sources.

Face à l’instabilité des cours agricoles et à ses conséquences notamment pour les pays en développement, la première partie de cette thèse est consacrée à la présentation des déterminants des cours des matières premières alimentaires, incluant les évolutions récentes en matière d’offre, en tenant compte des conséquences du réchauffement climatique, et de demande, considérant notamment les biocarburants. Il est également question de présenter la financiarisation en cours des économies, et les doutes qui planent sur le rôle que peuvent avoir la spéculation sur les marchés à terme ou encore la mise en œuvre des politiques monétaires, sur les cours au comptant observés sur les marchés physiques des produits agricoles. Suite aux réflexions et éléments de littérature avancés, la seconde partie procède de deux études empiriques. La première est axée sur l’impact de la spéculation sur les marchés financiers à terme sur le cours des sous-jacents (agricoles), alors que la seconde questionne le rôle des marchés monétaires, abordé à travers la capacité du banquier central à stabiliser les taux d’intérêt à court terme. Sur cette base, des conclusions mais également des pistes de recherche sont établies, du fait du prolongement en cours du processus de financiarisation des économies. / Faced with instability of agricultural commodities’ prices and its consequences especially for developing countries, the first part of this thesis is devoted to the presentation of food commodities’ prices, including recent developments with respect to the offering, taking into account the consequences of global warming and demand, as well as the importance of biofuels. It is also question to present the financialization of economies, and the doubts that take over the role of speculation on the futures markets or the implementation of monetary policies, on the spot prices observed on physical agricultural commodities markets. Following the advanced literature reflections and elements, the second part proceeds of two empirical studies, the first one focused on the impact of speculation about the financial futures markets on the underlying asset’s price (agricultural), while the second one examines the role of money markets through the capacities of the central banker to stabilize short-term interest rates. On this basis, conclusions but also future research are established due to the continuation of the economies financialization process.