The paper analyzes effects of the financial crisis on stock market of the Czech Republic and Spain. We employ BEKK-GARCH model in order to study volatility spillovers and transmissions from the US stock market to stock markets of the Czech Republic and Spain. The multivariate GARCH models results show statistically significant, but relatively small, almost irrelevant volatility spillovers from the US stock market to stock markets of the Czech Republic and Spain. The Czech stock market exhibits higher conditional correlation coefficient than the Spanish stock market.

In this thesis, we analyze volatility spillovers between crude oil and food commodities. The principal hypothesis assumes crude oil to behave as a production factor of the agricultural food commodities, thence we are looking for appropriate price effects. We mainly employ wavelet coherence and partial wavelet coherence, which provide us with valuable insight into the commodities nexus, without any strict restraints and assumptions levied on our data. Secondly, we build a DCC-GARCH model in order to model the presumed volatility spillovers. We also perform several simple benchmark analyses, in particular we test for Granger causality and we compute the Pearson correlation coefficients. Our data sample, including 10 commodities and 2 indices, covers the latest decade, significantly widening the existing contextual literature. Our results are mostly compliant with related literature, especially regarding the crude oil-fuels bundle and food commodities bundle, respectively. Considering the main research question of volatility spillovers between food commodities and crude oil, our results are indicating reasonably strong relationships with crude oil for soybeans and corn, leaving cotton and wheat rather on the verge of strong relationship and finding cattle to be completely unrelated. Main merits of the thesis...

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Volatility spillovers in stock markets have become an important phenomenon, especially in times of crises. Mechanisms of shock transmission from one mar- ket to another are important for the international portfolio diversification. Our thesis examines impulse responses and variance decomposition of main stock in- dices in emerging Central European markets (Czech Republic, Poland, Slovakia and Hungary) in the period of January 2007 to August 2009. Two models are used: A vector autoregression (VAR) model with constant variance of resid- uals and a time varying parameter vector autoregression (TVP-VAR) model with a stochastic volatility. Opposingly of other comparable studies, Bayesian methods are used in both models. Our results confirm the presence of volatility spillovers among all markets. Interestingly, we find significant opposite trans- mission of shocks from Czech Republic to Poland and Hungary, suggesting that investors see the Central European exchanges as separate markets. Bibliographic Record Janhuba, R. (2012): Volatility Spillovers in New Member States: A Bayesian Model. Master thesis, Charles University in Prague, Faculty of Social Sciences, Institute of Economic Studies. Supervisor: doc. Roman Horváth Ph.D. JEL Classification C11, C32, C58, G01, G11, G14 Keywords Volatility spillovers,...

This study examines how stock market returns and volatility responses to macroeconomic news announcements in US and Europe, and oil prices. Moreover, the market risk associated with these stock markets based on selected countries and regions is also analyzed here. In all chapters, the data is in a weekly time horizon and it covers 21 countries from different contents. In particular, Data covers three different time periods, i.e. full sample from 1/1/2000 to 12/31/2015, before the financial crisis, i.e. from 1/1/2000 to 9/27/2008 and after the financial crisis, i.e. from 10/11/2008 to 12/31/2015. Chapter 2 studies the impact of macroeconomic news announcements on stock markets in 21 countries using US and European countries macroeconomic news announcements. The first part investigates the impact of macroeconomic news announcements surprises in US and European Countries on stock markets returns in these countries. The second part analyzes the impact of macroeconomic news announcements in US and European Countries on stock markets volatility in these countries. Our results show that stock markets in selected countries react differently to macroeconomic news announcement in US and Europe. Chapter 3 study the interaction and volatility spillover between oil prices and stock markets returns and volatility in selected countries and regions. Oil prices are based on West Texas Intermediate (WTI). The analysis use VAR(1)-GARCH(1,1) model to capture the interdependence between stocks market and oil prices. The findings show that there is interdependence between stock markets and oil price changes in most selected countries and regions. Chapter 4 study the market risk in stock markets returns in selected countries and regions using IGARCH(1,1) and GARCH(1,1) to obtain the value at risk (VaR) and the expected shortfall (ES). The findings of chapter 4 show that market risk was high for most selected countries before the financial crisis and low after the financial crisis.

Le sujet principal de cette thèse est de proposer de nouvelles méthodes d'extractions de signaux avec applications en finance. Par signaux, nous entendons soit un signal sur lequel repose une stratégie d'investissement; soit un signal perturbé par un bruit, que nous souhaitons retrouver. Ainsi, la première partie de la thèse étudie la contagion en volatilité historique autours des annonces de résultats des entreprises du Nasdaq. Nous trouvons qu'autours de l'annonce, l'entreprise reportant ses résultats, génère une contagion persistante en volatilité à l’encontre des entreprises appartenant au même secteur. Par ailleurs, nous trouvons que la contagion en volatilité varie, selon le type de nouvelles reportées, l'effet de surprise, ou encore par le sentiment de marché à l'égard de l'annonceur. La deuxième partie de cette thèse adapte des techniques de dé-bruitage venant de l'imagerie, à des formes de bruits présentent en finance. Ainsi, un premier article, co-écrit avec Matthieu Garcin, propose une technique de dé-bruitage innovante, permettant de retrouver un signal perturbé par un bruit à variance non-constante. Cet algorithme est appliqué en finance à la modélisation de la volatilité. Un second travail s'intéresse au dé-bruitage d'un signal perturbé par un bruit asymétrique et leptokurtique. En effet, nous adaptons un modèle de Maximum A Posteriori, couramment employé en imagerie, à des bruits suivant des lois de probabilité de Student, Gaussienne asymétrique et Student asymétrique. Cet algorithme est appliqué au dé-bruitage de prix d'actions haute-fréquences. L'objectif étant d'appliquer un algorithme de reconnaissance de formes sur les extrema locaux du signal dé-bruité. / The main objective of this PhD dissertation is to set up new signal extraction techniques with applications in Finance. In our setting, a signal is defined in two ways. In the framework of investement strategies, a signal is a function which generates buy/sell orders. In denoising theory, a signal, is a function disrupted by some noise, that we want to recover. A first part of this PhD studies historical volatility spillovers around corporate earning announcements. Notably, we study whether a move by one point in the announcer historical volatility in time t will generate a move by beta percents in time t+1. We find evidences of volatility spillovers and we study their intensity across variables such as : the announcement outcome, the surprise effect, the announcer capitalization, the market sentiment regarding the announcer, and other variables. We illustrate our finding by a volatility arbitrage strategy. The second part of the dissertation adapts denoising techniques coming from imagery : wavelets and total variation methods, to forms of noise observed in finance. A first paper proposes an denoising algorithm for a signal disrupted by a noise with a spatially varying standard-deviation. A financial application to volatility modelling is proposed. A second paper adapts the Bayesian representation of the Rudin, Osher and Fatemi approach to asymmetric and leptokurtic noises. A financial application is proposed to the denoising of intra-day stock prices in order to implement a pattern recognition trading strategy.

This diploma thesis is devoted to the linkages among stock, bond and foreign exchange markets in the Czech Republic, Austria, Germany and Poland during the period from the beginning of the year 2007 to the end of the year 2014. In order to complexly describe the interconnections among the markets, we utilized two kinds of spillover indices (from the generalized and structural VAR model), dynamic correlation coefficients obtained from the multivariate GARCH model and contemporaneous coefficients from the structural VAR model that was identified through heteroskedasticity in structural shocks. These methods enabled us to describe the linkages among the markets from different angles, to capture their time evolution and to obtain a notion about the transmission mechanism among these markets in Central Europe. The results, inter alia, indicate an intensifying interconnection among the markets during crisis periods, lowering impact of stock markets, increasing influence of bonds and a dominant role of German bonds and Austrian stocks. At the same time, we were able to capture the influence of the European sovereign debt crisis on the spillovers and on the intensity of linkages among the markets. We showed that the intensity of linkages among bond markets relented, probably as a result of higher emphasis on the...

This thesis presents a collection of papers that has been published, accepted or submitted for publication. They assess price, volatility and market relationships in the five regional electricity markets in the Australian National Electricity Market (NEM): namely, New South Wales (NSW), Queensland (QLD), South Australia (SA), the Snowy Mountains Hydroelectric Scheme (SNO) and Victoria (VIC). The transmission networks that link regional systems via interconnectors across the eastern states have played an important role in the connection of the regional markets into an efficient national electricity market. During peak periods, the interconnectors become congested and the NEM separates into its regions, promoting price differences across the market and exacerbating reliability problems in regional utilities. This thesis is motivated in part by the fact that assessment of these prices and volatility within and between regional markets allows for better forecasts by electricity producers, transmitters and retailers and the efficient distribution of energy on a national level. The first two papers explore whether the lagged price and volatility information flows of the connected spot electricity markets can be used to forecast the pricing behaviour of individual markets. A multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model is used to identify the source and magnitude of price and volatility spillovers within (intra-relationship) and across (inter-relationship) the various spot markets. The results show evidence of the fact that prices in one market can be explained by their own price lagged one-period and are independent of lagged spot prices of any other markets when daily data is employed. This implies that the regional spot electricity markets are not fully integrated. However, there is also evidence of a large number of significant ownvolatility and cross-volatility spillovers in all five markets indicating that shocks in some markets will affect price volatility in others. Similar conclusions are obtained when the daily data are disaggregated into peak and off-peak periods, suggesting that the spot electricity markets are still rather isolated. These results inspired the research underlying the third paper of the thesis on modelling the dynamics of spot electricity prices in each regional market. A family of generalised autoregressive conditional heteroskedasticity (GARCH), RiskMetrics, normal Asymmetric Power ARCH (APARCH), Student APARCH and skewed Student APARCH is used to model the time-varying variance in prices with the inclusion of news arrival as proxied by the contemporaneous volume of demand, time-of-day, day-of-week and month-of-year effects as exogenous explanatory variables. The important contribution in this paper lies in the use of two latter methodologies, namely, the Student APARCH and skewed Student APARCH which take account of the skewness and fat tailed characteristics of the electricity spot price series. The results indicate significant innovation spillovers (ARCH effects) and volatility spillovers (GARCH effects) in the conditional standard deviation equation, even with market and calendar effects included. Intraday prices also exhibit significant asymmetric responses of volatility to the flow of information (that is, positive shocks or good news are associated with higher volatility than negative shocks or bad news). The fourth research paper attempts to capture salient feature of price hikes or spikes in wholesale electricity markets. The results show that electricity prices exhibit stronger mean-reversion after a price spike than the mean-reversion in the normal period, suggesting the electricity price quickly returns from some extreme position (such as a price spike) to equilibrium; this is, extreme price spikes are shortlived. Mean-reversion can be measured in a separate regime from the normal regime using Markov probability transition to identify the different regimes. The fifth and final paper investigates whether interstate/regional trade has enhanced the efficiency of each spot electricity market. Multiple variance ratio tests are used to determine if Australian spot electricity markets follow a random walk; that is, if they are informationally efficient. The results indicate that despite the presence of a national market only the Victorian market during the off-peak period is informationally (or market) efficient and follows a random walk. This thesis makes a significant contribution in estimating the volatility and the efficiency of the wholesale electricity prices by employing four advanced time series techniques that have not been previously explored in the Australian context. An understanding of the modelling and forecastability of electricity spot price volatility across and within the Australian spot markets is vital for generators, distributors and market regulators. Such an understanding influences the pricing of derivative contracts traded on the electricity markets and enables market participants to better manage their financial risks.

This thesis consists of four self-contained essays on the various topics in finance.  The first essay, The Information Content of The Systematic Risk Structure of Corporate Yields for Future Real Activity: An Exploratory Empirical Investigation, constructs a proxy for the systematic component of the risk structure of corporate yields (or systematic risk structure), and tests how well it predicts real economic activity in the United States. It finds that the systematic risk structure predicts the growth rate of industrial production 3 to 18 months into the future even when other leading indicators are controlled for, outperforming other models. A regime-switching estimation also shows that the systematic risk structure is very successful in identifying and capturing different growth regimes of industrial production.  The second essay, How Much Leverage is Too Much, or Does Corporate Risk Determine the Severity of a Recession? investigates whether financial conditions of the U.S. corporate sector  can explain the probability and severity of recessions. It proposes a measure of corporate vulnerability, the Corporate Vulnerability Index (CVI) constructed as the default probability for the entire corporate sector. It finds that the CVI is a significant predictor of the probability of a recession 4 to 6 quarters ahead, even controlling for other leading indicators, and that an increase in the CVI is also associated with a rise in the probability of a more severe and lengthy recession 3 to 6 quarters ahead.  The third essay, Asian Flu or Wall Street Virus? Tech and Non-Tech Spillovers in the United States and Asia (with Jorge A. Chan-Lau), using TGARCH models, finds that U.S. stock markets have been the major source of price and volatility spillovers to stock markets in the Asia-Pacific region during three different periods: the pre-LTCM crisis period, the “tech bubble” period, and the “stock market correction” period. Hong Kong SAR, Japan, and Singapore were sources of spillovers within the region and affected the United States during the latter period. There is also evidence of structural breaks in the stock price and volatility dynamics induced during the “tech bubble” period.  The fourth essay, Coping with Financial Spillovers from the United States: The Effect of U. S. Corporate Scandals on Canadian Stock Prices, investigates the effect of U.S. corporate scandals on stock prices of Canadian firms interlisted  in the United States. It finds that firms interlisted during the pre-Enron period enjoyed increases in post-listing equilibrium prices, while firms interlisted during the post-Enron period experienced declines in post-listing equilibrium prices, relative to a model-based benchmark. Analyzing the entire universe of Canadian firms, it finds that interlisted firms, regardless of their listing time, were perceived as increasingly risky by Canadian investors after the Enron’s bankruptcy. / Diss. Stockholm : Handelshögskolan, 2003