Made available in DSpace on 2014-06-12T17:17:13Z (GMT). No. of bitstreams: 2 arquivo3573_1.pdf: 2183173 bytes, checksum: b0ac8e62ddb36a1892ad1922d6ad45b7 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2008 / Os empreendimentos de transmissão de energia elétrica são afetados significativamente pelo financiamento que contratam, pois a repercussão do serviço da dívida afeta o lucro da empresa e sua disponibilidade de caixa. Atualmente, a fonte de recurso mais barata existente no mercado são os empréstimos concedidos pelo Banco Nacional de desenvolvimento Econômico e Social BNDES. Ocorre que as empresas dos setor elétrico têm recorrido bastante a fonte de recursos provenientes de Valores Mobiliários, como as debêntures. A grande vantagem das debêntures é sua flexibilidade no que tange às características como prazo de amortização, taxa de juros, periodicidade da amortização, sistema de amotização, etc, que são escolhidas pelo emissor deste título. A taxa de juros é um importante fator, visto que impacta diretamente o resultado do projeto e sua disponibilidade de caixa. Contudo, qual seria a taxa de juros ideal, considerando o risco do debenturista? Neste estudo, é calculado o risco do acionista e do debenturista, utilizando-se Modelos de Volatilidade Condicional da família ARCH e o Método de Simulação de Monte Carlo. O risco do acionista é comparado à seu retorno, através da utilização do Índice de Sharpe (IS). O valor do IS obtido para o acionista foi aplicado às debêntures, admitindo-se o pressuposto que o Índice de Sharpe do acionista deve ser igual ao do debenturista (a relação retorno versus risco deve ser equivalente para os dois títulos). Foi atribuída às debêntures uma taxa de juros de 7% a.a. + IPCA e observou-se que para o IS das debêntures ser equivalente ao IS das ações, a remunação do primeiro deveria ser 4,29% a.a. + IPCA, uma vez que seu risco (calculado neste trabalho) é inferior ao do acionista

O objetivo principal deste trabalho foi o de explorar a possibilidade de usar uma metodologia capaz de decompor uma série temporal via ondaletas, conjuntamente com os modelos econométricos e de redes neurais já existentes de previsão e comparar a qualidade de previsões obtidas para sucessões cronológicas não lineares simuladas. A proposta foi alcançada principalmente pela elaboração de um fluxograma para tratamento das previsões de sucessões cronológicas para colocar um rigor quantitativo mais adequado. O diferencial deste trabalho esteve na realização das previsões dentro das sub-séries decompostas por uma ondaleta em até dois níveis, e obtendo-se a previsão da série original via reconstrução da série para modelos construídos por processos geradores de dados de sucessões cronológicas não-lineares. Foram simulados séries de um processo ARIMA-GARCH, um processo ARIMA, um processo bilinear e uma série de um movimento browniano. O trabalho principal constituiu-se na elaboração da fase de pré-processamento e das previsões estática em separado para cada uma das sub-séries encontradas sendo feitas para 10 e 200 observações futuras. Além das previsões pontuais foi verificada também o envelopamento dos dados, que consiste em comparar o modelo de previsão através de um intervalo de confiança para os valores previstos em mil séries simuladas pela mesma semente. Os resultados apontaram que para um modelo ARIMA(1,0,0)-GARCH(1,1), pode-se observar que o pré-processamento pela ondaleta foi melhor para apenas uma etapa de separação de altas e baixas freqüências tanto pela correlação quanto pelos critérios do TIC sendo este reduzido e pelo MAPE menor para as previsões de curto prazo. Já para os modelos de redes neurais uma diferença importante que deve ser ressaltada entre as redes neurais recorrentes e as redes com algoritmo de retropropagação é a capacidade de previsão das redes recorrentes para dados não-lineares com 2 níveis de pré-processamento e para previsões de curto prazo. Todavia, já para o critério do envelopamento, os melhores resultados foram para as redes recorrentes na previsão do processo ARIMA-GARCH e bilinear e pré-processamento com 1 nível. Todos os dados também foram comparados com as previsões feitas sem pré-processamento, as quais se mostraram impróprias com MAPE perto de 100% para previsões de longo prazo. Também checou-se neste trabalho as alterações que a mudança da escolha de uma ondaleta por outra, poderia impactar nos resultados das previsões futuras. Constatou-se que a troca da forma de onda no pré-processamento que se pareça mais visualmente com a forma dos dados da série, reduz as medidas de acurácia em 48%, deixando evidências que possa haver melhoras nos resultados. Na análise prática para o IBOVESPA, os resultados não foram satisfatórios, visto que os melhores resultados ficam para redes recorrentes com 1 nível de pré-processamento. Outrossim, da análise deste trabalho, emerge a importância dada ao fluxograma implementado para as previsões e o papel das previsões em separado por ondaletas como redutores dos erros nos processos estocásticos, e da implementação das bandas de previsões para redes recorrentes para sucessões cronológicas não-lineares. / The main objective of this work was it of exploring the possibility to use a methodology capable to decompose a temporary series through ondaletas, jointly with the econometrics models and of neural network already existent of forecast and to compare the quality of forecasts obtained for chronological successions no lineal simulated. The proposal was reached mainly by the elaboration of a flowchart for treatment of the forecasts of chronological successions to put a more appropriate quantitative rigidity. The differential of this work was in the accomplishment of the forecasts inside of the sub-series decomposed by an ondaleta in up to two levels, and being obtained the forecast of the original series through reconstruction of the series for models built by generating processes of data of no-lineal chronological successions. They were simulated series of a process ARIMA-GARCH, a process ARIMA, a process bilinear and a series of a movement browniano. The main work was constituted in the elaboration of the pré-processing phase and of the forecasts static in separate for each one of the found sub-series being done for 10 and 200 future observations. Besides the punctual forecasts it was also verified the envelopamento of the data, that it consists of comparing the forecast model through a trust interval for the values foreseen in a thousand simulated series by the same seed. The results appeared that for a model ARIMA(1,0,0)-GARCH(1,1), it can be observed that the pré-processing for the ondaleta went better so much for just a stage of separation of discharges and low frequencies for the correlation as for the criteria of TIC being this reduced and for smaller MAPE for the forecasts of short period. Already for the models of nets neurais an important difference that it should be emphasized between the nets appealing neurais and the nets with retropropagação algorithm is the capacity of forecast of the appealing nets for no-lineal data with 2 pré-processing levels and for forecasts of short period. Though, already for the criterion of the envelopamento, the best results went to the appealing nets in the forecast of the process ARIMA-GARCH and bilinear and pré-processing with 1 level. All the data were also compared with the forecasts done without pré-processing, which were shown inappropriate with MAPE close to 100% for forecasts of long period. It was also checked in this work the alterations that the change of the choice of an ondaleta for other, was able to impactar in the results of the future forecasts. It was verified that the change in the wave way in the pré-processing that if it seems more visually with the form of the data of the series, it reduces the acurácia measures in 48%, leaving evidences that it can have improvements in the results. In the practical analysis for IBOVESPA, the results were not satisfactory, because the best results are for appealing nets with 1 pré-processing level. Likewise, of the analysis of this work, the importance emerges given to the flowchart implemented for the forecasts and the paper of the forecasts in separate for ondaletas as reducers of the mistakes in the processes estocásticos, and of the implementation of the bands of forecasts for appealing nets for no-lineal chronological successions.

This thesis consists of three studies which cover topics in the trading volume-market return volatility linkage, stock market return-aggregate mutual fund flow relationship as well as market return volatility-aggregate mutual fund flow interaction. Chapter 2 investigates the issue of volume-volatility linkage in the US market for the period 1990-2012 (S&P 500) and 1992-2012 (Dow Jones). We construct four sub-samples depending on three different structural points (the Asian Financial Crisis, the Dot-Com Bubble and the 2007 Financial Crisis). By employing univariate and bivariate GARCH processes, we find positive (negative) bidirectional linkages between these two aforementioned variables in various cases of the estimation, while a mixed one is observed in the remainder of these cases. Chapter 3 examines the issue of temporal ordering of the range-based stock market return (S&P 500 index) and aggregate mutual fund flow in the U.S. market for the period 1998-2012. We construct nine sub-samples represented by three fundamental cases of the whole data set. In addition, we take into consideration three essential indicators when splitting the whole data set, which are the 2000 Dot-Com Bubble, the 2007 Financial Crisis as well as the 2009 European Sovereign Debt Crisis. We examine the dynamics of the return-flow interaction by employing bivariate VAR model with various specifications of GARCH approach. Our principal findings display a bidirectional mixed feedback between stock market return and aggregate mutual fund flow for the majority of the sub-samples obtained. Nevertheless, we provide limited evidence of a positive bi-directional causality between return and flow. Chapter 4 investigates the dynamic relation between S&P 500 return volatility and U.S. aggregate mutual fund flow for the period spanning between 1998 and 2012. We assess the dynamics of the volatility-flow linkage by employing a bivariate VAR model with the GARCH approach which allows for long memory in the mean and the variance equations. In addition to the sub-samples obtained in chapter 3, we generate two measurements of volatility. Our baseline results indicate a variety of bidirectional mixed causalities between market return volatility and aggregate mutual fund flow in several sub-samples. In addition, we observe a negative/positive bi-directional relationship between volatility and flow in the rest of the sub-periods. Summarizing, a range of our findings are in line with the empirical underpinnings that most likely predict a significant linkage between the aforementioned variables. Finally, most of the bidirectional effects are found to be quite robust to the dynamics of the various GARCH processes employed in this thesis.

The purpose of this study is to investigate changes in dynamic conditional correlations between Swedish equity sector indices and commodities using oil, gold, copper and a general commodity index. Additionally the purpose is to evaluate which of the two methods, DCC- GARCH or GO-GARCH that is more efficient in estimating correlation for hedge ratio calculation. Daily data on the FTSE30 index of Sweden and its sector indices have been studied between the years 1994 and 2017. A DCC-GARCH (1,1) and GO-GARCH (1,1) model with one autoregressive term AR(1) using multivariate Student t- and Multivariate Affine Negative Inverse Gaussian distribution were used to estimate conditional correlations. Correlations between Swedish FTSE30, its sector indices and commodities are considerably lower than previous research has found American or emerging markets correlation with commodities to be. This suggests better diversification opportunities with commodities for the Swedish market. Optimal hedge ratios (OHR) was calculated and back tested using a rolling window analysis with 1000 days forecast length and 20 days re-estimation window and evaluated using a calculated hedge effectiveness index (HE). Determined by HE, copper is the best hedge for the Swedish composite FTSE30 and sector indices using conditional correlation from the GO-GARCH during the data period. Gold is considered as a semi-strong safe haven due to its negative correlation with all sectors. Additionally, this study identifies a temporarily large increase in the correlation between the Swedish equities sectors and composite index with commodities around the years 2015/2016. This study also emphasizes the difference between stressful and calm periods in the market.

My dissertation investigated business cycle effects on US sectoral stock returns. The first chapter examined the relationship between the business cycle and sectoral stock returns. First, I calculated constant correlation coefficients between the business cycle and sectoral stock returns. Then, I employed the DCC GARCH model to estimate time-varying correlation coefficients for each pair of the business cycle and sectoral stock returns. Finally, I ran regression of sectoral returns on dummy variables designed to capture the four stages of the business cycle. I found that though sectoral stock returns were closely related to the business cycle, they did not share some of its main characteristics. The second chapter developed two models in order to discuss possible asymmetric business cycle effects on US sectoral stock returns. One was a GARCH model with asymmetric explanatory variables and the other one was an ARCH-M model with asymmetric external regressors. In the second model, square root of conditional variance of the business cycle proxy was characterized as positive or negative risk, depending on the algebraic sign of past innovations driving the business cycle proxy. I found that some sectors changed their cyclicities from expansions to recessions. Negative shocks to business cycles had most power to influence sectoral volatilities. Positive and negative parts of business cycle risk had same effects on some sectors but had opposite effects on other sectors. A general conclusion of both models was that business cycle had stronger effects than own sectoral effects in driving sectoral returns. The third chapter discussed Chinese business cycle effects on US sectoral stock returns at two horizons. At a monthly horizon, the third lag of Chinese IP growth rate had positive effects on most sectors. The second lag of US IP growth rate had positive effects on almost all sectors. At a quarterly horizon, besides the extensive positive effects of the first lag of Chinese IP growth rate, the third and fourth lags also had effects on some sectors. The US IP growth rate had the same pattern, namely positive first and fourth lag effects and negative third lag effects. Using a 5-year rolling fixed window, I found that these business cycle effects were time-varying. The major changes in parameters resulted from the elimination of quota on textiles by WTO, the terrorist attacks on the US, and the 2007 financial crisis.

This dissertation investigates the dynamics of mean and volatility spillovers from the U.S. and three large (regional) Asia-Pacific stock markets to ten small (local) ones from June 2008 to May 2013. After a brief introduction to the main purposes and contributions of my research in Chapter 1, I examine the impact of lagged American and regional returns on the local markets in Chapter 2. By building up a univariate autoregressive model and treating lagged U.S. and regional returns as exogenous variables, I find that the local markets have statistically significant exposure to lagged returns of their own and the U.S. market only. The empirical results suggest that lagged American returns have exerted considerable mean spillover impact upon most of the local markets, whereas the large Asia-Pacific markets involved in this study have few such impacts. I study the linkage between the U.S. market and each of the regional markets in Chapter 3 by employing two specifications of the bivariate GARCH process—the BEKK and general dynamic covariance (DC) models—to capture common features of equity return data. Based on the results of carefully constructed diagnostic tests, the BEKK model is demonstrated to be more appropriate for the U.S.–China and U.S.–Japan cases, and the dynamic covariance model for the U.S.–Australia case. In Chapter 4, I discuss time-varying correlation of a local market with the U.S. market and with each regional market by proposing three Markov-switching shock spillover models. A comparison of model performance is drawn based on a series of model selection criteria. In fourteen cases, the local market is found to be more sensitive to regional shocks. Disturbances from two regional markets account for a higher proportion of local variance than those of U.S. origin. I conclude that the regional center, although having little mean spillover effect upon the local markets, has become increasingly influential in volatility transmission. Possible extended studies in the future as well as main findings in the preceding chapters are summarized in Chapter 5.

This thesis offers a comprehensive view of coffee trading on commodity markets. To describe the behavior of prices and their volatility, ARCH and GARCH models are used. These models analyse coffee prices of selected regions in Ethiopia, the birth place of coffee. The thesis connects the characteristics of soft commodity with current knowledge of financial econometrics. It also describes the effects of changes in exchange rates and oil prices on the price of coffee. Price volatility is examined with regard to deregulation and reforms on this market within the last three decades. Developments in the developing world caused a significant need for the progression and identification with the hedging instruments. These are closely linked to the globalized market with coffee, so it is conversely possible to absorb the shocks on small growers, who are significantly impacted by the globalized world.

The aim of the thesis is to present a robust conceptual framework for risk management of non-financial companies in order to improve decision making in the area of hedging with derivative instruments. Application of modern quantitative methods.

This diploma thesis inquiries into business activities of the electricity market retailer. Thesis starts with an explanation of retailers role and its relations with other market participants in the Czech Republic. In application part there are two solved examples of tasks hidden behind retailers profitable operations.

The main goal of this thesis is to suggest an appropriate approach to interest rate risk modeling in the Czech financial market environment in various situations. Three distinct periods are analyzed. These periods, which are the period before the global financial crisis, period during the financial crisis and in the aftermath of the global financial crisis and calming subsequent debt crisis in the eurozone, are characterized by different evaluation of liquidity and credit risk, different relationship between financial variables and market participants and different degree of market regulations. Within this goal, an application of the BGM model in the Czech financial market environment is crucial. Use of the BGM model for the purpose of predicting a dynamics of a yield curve is not very common. This is firstly due to the fact that primary use of this model is a valuation of interest rate derivatives while ensuring the absence of arbitrage and secondly its application is relatively difficult. Nevertheless, I apply the BGM model to obtain predictions of the probability distributions of interest rates in the Czech and eurozone market environment, because its complexity, direct modeling of a yield curve based on market rates and especially a possibility of parameter estimation based on current swaptions volatilities quotations may lead to a significant improvement of predictions. This improvement was also confirmed in this thesis. Use of swaptions volatilities market quotations is especially useful in the period of unprecedented mone- tary easing and increased number of central banks and other regulators interventions into financial markets that occur after the financial crisis, because it reflects current market expectations which also include future interventions. As a consequence of underdevelopment of the Czech financial market there are no market quotations of Czech koruna denominated swaptions volatilities. I suggest their approximations based on quotations of euro denominated swaptions volatilities and also using volatilities of koruna and euro forward rates. Use of this approach ensures that predictions of the Czech yield curve dynamics contain current market expectations. To my knowledge, any other author has not presented similar application of the BGM model in the Czech financial market environment. In this thesis I further predict a Czech and Euro area money market yield curve dynamics using the CIR and the GP models as representatives of various types of interest rates models to compare these predictions with BGM predictions. I suggest a comprehensive system of three criteria, based on comparison of predicti- ons with reality, to describe a predictive power of selected models and an appropria- teness of their use in the Czech market environment during different situations in the market. This analysis shows that predictions of the Czech money market yield curve dynamics based on the BGM model demonstrate high predictive power and the best 8 quality in comparison with other models. GP model also produces relatively good qua- lity predictions. Conversely, predictions based on the CIR model as a representative of short rate model family completely failed when describing reality. In a situation when the economy allows negative rates and there is simultaneously a significant likelihood of their implementation, I recommend to obtain predictions of Czech money market yield curve dynamics using GP model which allows existence of negative interest rates. This analysis also contains a statistical test for validating the predictive power of each model and information on other tests. Berkowitz test rejects a hypothesis of accurate predictions for each model. However, this fact is common in real data testing even when using relatively good model. This fact is especially caused by difficult fulfilment of test conditions in real world. To my knowledge, such an analysis of the predictive power of selected interest rate models moreover in the Czech financial market environment has not been published yet. The last goal of this thesis is to suggest an appropriate approach to obtaining pre- dictions of Czech government bonds risk premium dynamics. I define this risk premium as a difference between government bond yields and fixed rate of CZK IRS with the same length. I apply the GP model to describe the dynamics of this indicator of the Czech Republic credit risk. In order to obtain a time series of the risk premium which are necessary for estimation of GP model parameters I firstly estimate yield curves of Czech government bonds using Svensson model for each trading day since 2005. Resulting si- mulations of risk premium show that the GP model predicts the real development of risk premiums of all maturities relatively well. Hence, the proposed approach is suitable for modeling of Czech Republic credit risk based on the use of information extracted from financial markets. I have not registered proposed approach to risk premium modeling moreover in the Czech financial market environment in other publications.