Diss. Stockholm : Handelshögsk.

Diss. Stockholm : Handelshögsk.

Roughly all the previous empirical research, focusing on the information effects on volatility, has investigated the volatility dynamics during and after the release of public information. Researchers use ARCH-type or realized volatility models and they proxy public information by market news announcements. So far, studies focusing on the effect of noise or technical trading on volatility have been limited to theoretical results without any empirical evidence. Technical trading is trading based on technical signals. As a consequence, the aim of this dissertation is to answer to the following question: how does foreign exchange volatility behave, in the very short term, around public information and technical signals ? To answer to this question we study the volatility dynamics before, during and after public news announcements and technical chart pattern signals. In order to meet this objective, we implement different methodologies specific to the different chapters of the dissertation. Each chapter tries to answer to a sub-question emerging from the main question of the thesis. This thesis contributes to the empirical finance literature on intradaily exchange rate volatility as follows. First we present evidence that volatility increases in the pre-announcement period of scheduled news. Second, we show that foreign exchange dealers quoting activity reacts to news announcements and it conveys useful information. The third contribution consists in presenting a new approach to recognize technical chart patterns from a time series, and shedding light on the predictive success of the technical chart signals. Finally, the last contribution consists in the finding that technical signals, considered by economists as "noise", have a significant effect on volatility.

The bid-ask spread costs consist of three components, which include order processing costs, inventory-holding costs, and adverse selection costs. In this paper, we model the inventory-holding costs of the bid-ask spread by option hedging. Theinventory-holding costs are hedged by call or put option positions. Since trades deal with the adverse selection traders are unobservable. We treat it as a latent variable, and Expected-Maximization (EM) algorithm are applied to estimate the related parameters of the model. Simulation studies are performed for several different models. Empirical results of NYSE high frequency data show that the proposed model are obtain appropriate parameter estimation when the returns satisfied normality assumption.

This dissertation contains four papers in the field of financial econometrics. In the first paper, A Smooth Transition ARCH Model for Asset Returns, a new class of ARCH models is introduced. The model class allows for non-linearity in the equation for the conditional variance. Two forms of non-linearity are considered: (i) asymmetry regarding the sign of residuals, and (ii) non-linearity regarding the size of residuals. Furthermore, specification tests for the models are presented. The second paper, Specification Tests for Asymmetric GARCH, presents two new Lagrange multiplier test statistics designed for testing the null of GARCH(1,1), against the alternative of asymmetric GARCH. Small sample properties for the statistics are presented and the power of both tests is shown to be superior to that of previously proposed tests. This is true for a large group of asymmetric GARCH models, providing that the proposed tests can detect general GARCH asymmetry. The third paper, Modeling Nordic Stock Returns with Asymmetric GARCH models, investigates the presence of asymmetric GARCH effects in a number of equity return series, and compares the modeling performance of seven different asymmetric GARCH models. The data consists of daily returns for 45 Nordic stocks, for the period July 1991 to July 1996. The paper also introduces three new procedures for asymmetry testing. The proposed LM tests allow for heterokurtosis under the null. The final paper, Discrete Time Hedging of OTC Options in a GARCH Environment: A Simulation Experiment, examines the effect of using the Black and Scholes formula for pricing and hedging options in a discrete time heteroskedastic environment using a simulation procedure. It is shown that the variance of the returns on the hedged position is considerably higher in a GARCH(1,1) environment than in a homoskedastic environment. The variance of returns is heavily dependent on the level of kurtosis in the returns process and on the first-order autocorrelation in centered and squared returns.Each paper is self-contained and can be read in an order chosen by the reader.In an introductory chapter, the reader is given a general summary of the ARCH literature and will gain a clear understanding of how the four essays relate to previous work in the econometrics and finance literature, and to practical considerations of econometric modeling. / Diss. Stockholm : Handelshögsk.

In this thesis, we inquire interdependencies and comovements between CEE capital markets within each other. German market is also included in the analysis as a benchmark to CEE capital markets. We have chosen German capital market as it represents more developed market from the same geographical region. We study a unique high-frequency dataset of 5 minutes, 30 minutes and 1 hour data frequencies covering the the crisis period and post-crisis "tranquil" period. Daily data frequency is also involved in the analysis. Using different econometric techniques, we found no steady long-term relationships among stock market indices. The only strong relationship was detected between the DAX and WIG20 indices during both crisis and "tranquil" periods. The frequency of interactions changed across periods. The strongest interdependencies were recognized in 5 minute data frequency which indicates fast reactions between markets. Information inefficiency was revealed between markets according to cointegration tests in most cases.

Neste trabalho realizamos um amplo estudo de simulação com o objetivo principal de avaliar o desempenho de carteiras de mínima variância global construídas com base em modelos de previsão da volatilidade que utilizam dados de alta frequência (em comparação a dados diários). O estudo é baseado em um abrangente conjunto de dados financeiros, compreendendo 41 ações listadas na BM&FBOVESPA entre 2009 e 2017. Nós avaliamos modelos de previsão de volatilidade que são inspirados na literatura ARCH, mas que também incluem medidas realizadas. Eles são os modelos GARCH-X, HEAVY e Realized GARCH. Seu desempenho é comparado com o de carteiras construídas com base na matriz de covariância amostral, métodos de encolhimento e DCC-GARCH, bem como com a carteira igualmente ponderada e o índice Ibovespa. Uma vez que a natureza do trabalho é multivariada, e a fim de possibilitar a estimação de matrizes de covariância de grandes dimensões, recorremos à especificação DCC. Utilizamos três frequências de rebalanceamento (diária, semanal e mensal) e quatro conjuntos diferentes de restrições sobre os pesos das carteiras. A avaliação de desempenho baseia-se em medidas econômicas tais como retornos anualizados, volatilidade anualizada, razão de Sharpe, máximo drawdown, Valor em Risco, Valor em Risco condicional e turnover. Como conclusão, para o nosso conjunto de dados o uso de retornos intradiários (amostrados a cada 5 e 10 minutos) não melhora o desempenho das carteiras de mínima variância global. / In this work we perform an extensive backtesting study targeting as a main goal to assess the performance of global minimum variance (GMV) portfolios built on volatility forecasting models that make use of high frequency (compared to daily) data. The study is based on a broad intradaily financial dataset comprising 41 assets listed on the BM&FBOVESPA from 2009 to 2017. We evaluate volatility forecasting models that are inspired by the ARCH literature, but also include realized measures. They are the GARCH-X, the High-Frequency Based Volatility (HEAVY) and the Realized GARCH models. Their perfomances are benchmarked against portfolios built on the sample covariance matrix, covariance matrix shrinkage methods, DCC-GARCH as well as the naive (equally weighted) portfolio and the Ibovespa index. Since the nature of this work is multivariate and in order to make possible the estimation of large covariance matrices, we resort to the Dynamic Conditional Correlation (DCC) specification. We use three different rebalancing schemes (daily, weekly and monthly) and four different sets of constraints on portfolio weights. The performance assessment relies on economic measures such as annualized portfolio returns, annualized volatility, Sharpe ratio, maximum drawdown, Value at Risk, Expected Shortfall and turnover. We also account for transaction costs. As a conclusion, for our dataset the use of intradaily returns (sampled every 5 and 10 minutes) does not enhance the performance of GMV portfolios.

In this thesis we focus on modelling correlation between selected stock markets using high-frequency data. We use time-series of returns of following indices: FTSE, DAX PX and S&P, and Gold and Oil commodity futures. In the first part of our empirical work we compute daily realized correlations between returns of subject instruments and discuss the dynamics of it. We then compute unconditional correlations based on average daily realized correlations and using feedforward neural network (FFNN) to assess how well the FFNN approximates realized correlations. We also forecast daily realized correlations of FTSE:DAX and S&P:Oil pairs using heterogeneous autoregressive model (HAR), autoregressive model of order p (AR(p)) and nonlinear autoregressive neural network (NARNET) and compare performance of these models.

Submitted by Jadi Castro (jadiana.castro@ucb.br) on 2017-02-21T18:01:09Z No. of bitstreams: 1 AdaoVoneTeixeiradeAssuncaoDissertacao2016.pdf: 1069424 bytes, checksum: 2cec9cf848a40c34902559e8d8f0c95c (MD5) / Approved for entry into archive by Kelson Anthony de Menezes (kelson@ucb.br) on 2017-02-21T18:02:23Z (GMT) No. of bitstreams: 1 AdaoVoneTeixeiradeAssuncaoDissertacao2016.pdf: 1069424 bytes, checksum: 2cec9cf848a40c34902559e8d8f0c95c (MD5) / Made available in DSpace on 2017-02-21T18:02:24Z (GMT). No. of bitstreams: 1 AdaoVoneTeixeiradeAssuncaoDissertacao2016.pdf: 1069424 bytes, checksum: 2cec9cf848a40c34902559e8d8f0c95c (MD5) Previous issue date: 2016-03-31 / Aplicamos o Modelo Autoregressivo de Dura????o Condicional (ACD) Mercado de Futuros de Taxa Interbanc??ria Brasileira. A amostra foi constru??da com base em contratos M??s antes da expira????o para replicar a curva de obriga????es de um m??s eo per??odo estudado Vai de julho de 2013 a setembro de 2015. Utilizamos M??xima Verossimilhan??a Estimativa baseada nas distribui????es de probabilidade mais populares na literatura ACD: Exponencial, gama e Weibull e verificou-se que a estimativa baseada na A distribui????o exponencial foi a melhor op????o para modelar os dados. / We applied the basic Autoregressive Conditional Duration Model (ACD) to the Brazilian Interbank Rate Futures Market. The sample was built using contracts in the month prior to expiration to replicate a one month bond curve and the period studied goes from july of 2013 to september of 2015. We used Maximum Likelihood Estimation based on the most popular probability distributions in the ACD literature: exponential, gamma and Weibull and we found that the estimation based on the exponential distributional was the best option to model the data.

Commodities futures trading went through unparalleled structural transformation during the first decade of the 2000s, which ultimately resulted in long lasting impacts on the volume and open interest levels as well as on the access to these markets and inclusion of new participants. Benefiting from the new sets of high frequency data made available due to these transformations, this dissertation is composed of three papers that investigate different market microstructure aspects of the commodities futures markets at BM&F-Bovespa. The first paper analyzes the modelling and forecasting of realized volatility in the corn and live cattle markets. For this purpose, the heterogeneous autoregressive model (HAR-RV) proposed by Corsi (2009) was used, as well as its extensions adapted to include jump components (Andersen et al., 2007) and leverage components (Corsi and Reno, 2012). Using measurements to compare both analysis, results show that modelling in-sample realized volatility is best performed if jumps and leverage components are included in the model. Out-of-sample forecasts results for the live cattle market show that there was no statistically significant difference between the models. For the corn markets, difference in the models\' forecast performance was found at the daily horizon only. The second paper delves into the relationship between volatility, volume and bid-ask spread in the corn and live cattle markets. Considering that these are emerging agricultural markets, concentration measures were also included. A three-equation structural model was used to capture the relationship between volatility, volume and bid-ask spread and the estimation was performed using the IV-GMM approach. Findings show that bid-ask spread levels are higher for live cattle markets than it is for corn markets. In addition, bid-ask spread is negatively related to volume and positively related to volatility. The significance and magnitude of the responses depend on the level of liquidity in each market. Further, results point out that concentration impacts corn and live cattle differently. The third paper examines the dynamic relationship between dealers activity and market structure in the live cattle inter-dealer market at BM&F-Bovespa. First, a descriptive analysis of the live cattle inter-dealer market structure is carried out and then follows an investigation of the dynamic of dealers\' activity and its determinants. Results indicate that the live cattle inter-dealer market is not competitive and that dealers\' activity is positively related to market concentration, quoted bid-ask spread, number of active dealers and the dealer\'s traded quantity. / Negociações nos mercados futuros de commodities passaram por transformações estruturais significativas durante a primeira década dos anos 2000, resultando em uma elevação dos níveis de volume e open interest, e também em uma maior facilidade de acesso a esses mercados e inclusão de novos participantes. Beneficiando-se da divulgação de dados de alta frequência possibilitada por estas transformações, esta tese, composta por três artigos, tem por objetivo investigar diferentes aspectos da microestrutura dos mercados de commodities da BM&F-Bovespa. O primeiro artigo analisa a modelagem e previsão de volatilidade realizada nos mercados futuros de milho e boi gordo. Para este fim, utilizou-se o modelo heterogêneo auto regressivo proposto por Corsi (2009), bem como suas extensões adaptadas para a inclusão dos componentes de saltos (jumps) (Andersen et al., 2007) e alavancagem (Corsi e Reno, 2012). Utilizando diferentes métricas de comparação, os resultados encontrados mostram que os modelos que incluem os componentes de saltos e os de alavancagem tem melhor desempenho que os demais em análises in-sample (modelagem). Por outro lado, a análise das previsões out-of-sample mostra que, para o mercado de boi gordo, não há diferença entre os modelos empregados, enquanto que para o mercado de milho verificou-se uma diferenciação preditiva no horizonte diário, porém para os horizontes semanal e mensal, os quatro modelos tiveram performance indistinta. O segundo artigo explora a relação entre volatilidade, volume e bid-ask spread nos mercados de milho e boi gordo. Levando em conta que se trata de mercados emergentes, métricas de concentração de mercado foram incluídas na análise. Para capturar a relação entre volatilidade, volume e bid-ask spread, um modelo estrutural de três equações simultâneas foi utilizado e a estimação foi feita através do modelo GMM com variáveis instrumentais. Os resultados indicam que os níveis de bid-ask spread encontrados para o mercado de boi gordo são maiores que os encontrados para o mercado de milho. Além disso, o bid-ask spread é negativamente relacionado ao volume e positivamente relacionado à volatilidade. Entretanto, a intensidade e magnitude da relação entre as variáveis depende dos níveis de liquidez dos mercados analisados. A concentração impacta o mercado de milho e boi gordo de forma diferente. O terceiro artigo investiga a dinâmica da relação entre a atividade dos dealers e estrutura do mercado de boi gordo na BM&F-Bovespa. Primeiramente, faz-se uma análise descritiva deste mercado e posteriormente estuda-se o comportamento dos dealers e seus determinantes. Os resultados indicam que os dealers no mercado de boi gordo não operam em uma estrutura competitiva e que a atividade destes é positivamente relacionada à concentração de mercado, ao bid-ask spread, ao número de dealers ativos e à quantidade de contratos transacionada pelos dealers.