Master-Arbeit Univ. St. Gallen, 2009.

Master-Arbeit Univ. St. Gallen, 2009.

Techn. Universiẗat, Diss., 1999--Darmstadt.

Master-Arbeit Univ. St. Gallen, 2005.

Master-Arbeit Univ. St. Gallen, 2008.

Techn. Universiẗat, Diss., 2004--Berlin.

This thesis consists of three essays and empirically studies the behaviour of emerging and frontier stock markets against instability in the commodity and international financial markets. The first essay considers symmetric and asymmetric dynamic conditional correlation multivariate GARCH models to examine the correlations between the Gulf Cooperation Council (GCC) stock markets and the Brent and OPEC crude oil price indices and to gauge the oil shocks effect on the dynamics of the GCC stock markets. The analysis uses weekly data covering the period December 31st, 2003 to December 27th, 2012. The results show that: (i) two of the GCC stock markets are asymmetrically correlated with both the Brent and OPEC crude oil price indices and only two are symmetrically correlated with Brent oil; (ii) all the GCC stock markets exhibit positive and symmetric conditional correlations overtime and these correlations are more pronounced during periods of high oil price fluctuations. The second essay investigates the contagion effect and volatility spillovers from the U.S. financial, the Dubai and the European debt crises to the GCC stock markets, with particular focus on financial and non-financial sectors. It uses weekly data for the period December 31st, 2003 to January 28th, 2015 and applies GARCH models and indicators of crisis. The empirical results show that: i) contagion effects are present on some of the GCC stock markets and are more pronounced during the U.S. financial and Dubai debt crises, with a larger impact on financial sectors; ii) there is significant evidence of volatility spillovers from the financial sectors of the U.S., European and Dubai stock markets to some of the GCC sectors considered, even though spillovers are rather weak in magnitude. The last essay investigates the extent to which the GCC stock markets are correlated and integrated with those of the Asian countries. The analysis is carried out using the Johansen cointegration approach, the dynamic conditional correlation (DCC) GARCH model, and a standard correlation analysis based on a rolling window estimation scheme. The sample period of the analysis spans from December 31st, 2003 to September 30th, 2015. The empirical analysis offers three main results. First, there is a relatively moderate evidence of cointegration among some of the GCC and Asian stock markets particularly with of those of strong economic linkages among them. Second, evidence of time-varying correlation is found in some cases, while not large in magnitude, and shocks to volatility are highly persistence. Third, stock returns show a common trend exists, only during the global financial crisis.

Made available in DSpace on 2014-06-12T15:08:44Z (GMT). No. of bitstreams: 2 arquivo6629_1.pdf: 483511 bytes, checksum: 460bb4e1d78316dc231ec47b5f2e7d5d (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / Apesar do Brasil ser o maior produtor e exportador de açúcar do mundo, o mercado interno brasileiro sofre com a ausência de um mercado de futuros com liquidez dentro do país para esse produto em questão. Para contornar esse problema, agentes passaram a negociar contratos futuros de açúcar em Nova Iorque a fim de amenizar a exposição aos riscos oriundos do mercado à vista. Dessa forma, o presente trabalho teve como objetivo avaliar qual seria a melhor estratégia de hedge para aquele agente que mantém uma posição de açúcar no mercado brasileiro. Feita uma revisão da literatura especializada, decidiu-se realizar estimações dos modelos de hedge com o intuito de sugerir a melhor estratégia a ser tomada mediante, também, a um contexto de volatilidade. Dois principais modelos foram discutidos e estimados: um modelo VEC, o que fornece a taxa ótima de hedge estático, e outro modelo que incorpora o conteúdo informacional da variabilidade dos preços, que é o modelo GARCH BEKK diagonal. Os resultados apontaram que realmente a atuação em mercados futuros reduz o risco do agente frente à exposição completa no mercado à vista, porém foi inconclusivo ao se tratar com freqüências diferentes das amostras. Mas ao se trabalhar com dados mensais, sugere-se que o mais aconselhável é avaliar constantemente a posição de contratos futuros, principalmente para aquelas empresas de grande porte que estejam dispostas a arcar com os custos operacionais de obter essa informação

The rapid advancement in encryption and network computing gave birth to new tools and products that have influenced the local and global economy alike. One recent and notable example is the emergence of virtual currencies, also known as cryptocurrencies or digital currencies. Virtual currencies, such as Bitcoin, introduced a fundamental transformation that affected the way goods, services, and assets are exchanged. As a result of its distributed ledgers based on blockchain, cryptocurrencies not only offer some unique advantages to the economy, investors, and consumers, but also pose considerable risks to users and challenges for regulators when fitting the new technology into the old legal framework. This paper attempts to model the volatility of bitcoin using 5 variants of the GARCH model namely: GARCH(1,1), EGARCH(1,1) IGARCH(1,1) TGARCH(1,1) and GJR-GARCH(1,1). Once the best model is selected, an OLS regression was ran on the volatility series to measure the day of the week the effect. The results indicate that the TGARCH (1,1) model best fits the volatility price for the data. Moreover, Sunday appears as the most significant day in the week. A nontechnical discussion of several aspects and features of virtual currencies and a glimpse at what the future may hold for these decentralized currencies is also presented.

One of the essential features of financial time series data is volatility. It is often the case that, over time, structural changes occur in volatility, and an accurate estimation of the volatility of financial time series requires careful identification of the change-points. A common approach to modeling the volatility of time series data is based on the well-known Generalized Autoregressive Conditional Heteroscedastic (GARCH) model. Although the problem of change-point estimation of volatility dynamics derived from the GARCH model has been considered in the literature, these approaches rely on parametric assumptions of the conditional error distribution, which are frequently violated in financial time series. This misspecification of error distribution may lead to change-point detection inaccuracies, resulting in unreliable GARCH volatility estimates. In this dissertation, we introduce novel change-point detection algorithms based on a semiparametric GARCH model. The proposed semiparametric GARCH model retains the structural advantages of the GARCH process while incorporating the flexibility of nonparametric conditional error distribution. Consequently, the likelihood function and the corresponding volatility estimates obtained via this semiparametric approach are more accurate than the traditional Quasi-Maximum Likelihood Estimation (QMLE) method that relies on an assumed parametric error distribution. The main objective of the change-point estimation problem is to detect the exact number and locations of the change-points. This dissertation proposes an innovative semiparametric GARCH process in developing solutions for change-point estimation problems. Specifically, a penalized likelihood approach based on a semiparametric GARCH model and an efficient binary segmentation algorithm is developed to estimate the change points' locations. The results demonstrate that in terms of change-point identification and estimation accuracy for multiple GARCH process variations, the proposed semiparametric method outperforms the commonly used approaches to change-point analysis in financial data.