Whether or not currency markets may be regarded as efficient or not has been a hotly debated issue in the academic literature over recent decades. Economic theory would suggest that these markets should be efficient because they are apparently good examples of a perfectly competitive market structure. On the other hand, empirical tests of the efficient market hypothesis within these currency markets unequivocally find them to be inefficient. There is still no good explanation for this conundrum and as a result a fair amount of effort is still expended on refining the empirical studies of market efficiency, a task which is taken up in the four empirical studies that comprise this thesis. Within efficient markets, prices are predicted to respond &quotquickly" with the arrival of new information and the empirical work in the thesis focuses on these issues by identifying three key areas for research, namely, price adjustment and volatility, volatility and the &quotnews", and the speed of price adjustment. In essence, the studies examine whether there is inefficient adjustment to news in terms of excessive volatility, whether or not news is actually the main driver of exchange rate volatility and whether or not &quotquickly" can be measured empirically. The empirical results reported within this thesis confirm that the Australian dollar has not been an excessively volatile currency, even though the level of volatility has been increasing; that the pattern of information flow explains a significant degree of the non constant variance in the returns of the world's most actively traded currencies, (i.e. information explains price innovation); that the reaction time to macroeconomic news occurs within seconds of a pre-scheduled announcement, and that the bulk of adjustment to fundamental value occurs within the hour. These findings are consistent with what would be expected within an efficient market. The results reported within this thesis therefore suggest that the currency markets studied are efficient, at least for the sample periods of the data used in the studies. Exchange rates adjust rapidly with information arrival albeit not completely. It is also the case that a number of additional research questions emerge from this research. For example we know that volatility is not excessive and that it is increasing. What we do not know is the point at which increasing volatility becomes excessive. We know that exchange rates react quickly with the arrival of macroeconomic news, but we do not know precisely how long it takes for volatility to return to preannouncement levels, or why the reaction to news is inconsistent. We also do not know what type of information best explains volatility above that which is explained by the systematic dissemination of information or why full adjustment to fundamental value does not occur? Answers to these questions provide a future research agenda. Answers may provide insight that will help financial economists explain the apparent failure of the speculative efficient hypothesis.

The aim of this thesis is to show that news analytics data can be utilised to improve the predictive ability of existing models that have useful roles in a variety of financial applications. The modified models are computationally efficient and perform far better than the existing ones. The new modified models offer a reasonable compromise between increased model complexity and prediction accuracy. I have investigated the impact of news sentiment on volatility of stock returns. The GARCH model is one of the most common models used for predicting asset price volatility from the return time series. In this research, I have considered quantified news sentiment as a second source of information and its impact on the movement of asset prices, which is used together with the asset time series data to predict the volatility of asset price returns. Comprehensive numerical experiments demonstrate that the new proposed volatility models provide superior prediction than the "plain vanilla" GARCH, TGARCH and EGARCH models. This research presents evidence that including news sentiment term as an exogenous variable in the GARCH framework improves the prediction power of the model. The analysis of this study suggested that the use of an exponential decay function is good when the news flow is frequent, whereas the Hill decay function is good only when there are scheduled announcements. The numerical results vindicate some recent findings regarding the utility of news sentiment as a predictor of volatility, and also vindicate the utility of the new models combining the proxies for past news sentiments and the past asset price returns. The empirical analysis suggested that news augmented GARCH models can be very useful in estimating VaR and implementing risk management strategies. Another direction of my research is introducing a new approach to construct a commodity futures pricing model. This study proposed a new method of incorporating macroeconomic news into a predictive model for forecasting prices of crude oil futures contracts. Since these futures contracts are iii iv more liquid than the underlying commodity itself, accurate forecasting of their prices is of great value to multiple categories of market participants. The Kalman filtering framework for forecasting arbitrage-free (futures) prices was utilized, and it is assumed that the volatility of oil (futures) price is influenced by macroeconomic news. The impact of quantified news sentiment on the price volatility is modelled through a parametrized, nonlinear functional map. This approach is motivated by the successful use of a similar model structure in my earlier work, for predicting individual stock volatility using stock-specific news. Numerical experiments with real data illustrate that this new model performs better than the one factor model in terms of accuracy of predictive power as well as goodness of fit to the data. The proposed model structure for incorporating macroeconomic news together with historical (market) data is novel and improves the accuracy of price prediction quite significantly.

This dissertation consists of three related chapters that study financial market volatility, jumps and the economic factors behind them. Each of the chapters analyzes a different aspect of this problem. The first chapter examines tests for jumps based on recent asymptotic results. Monte Carlo evidence suggests that the daily ratio z-statistic has appropriate size, good power, and good jump detection capabilities revealed by the confusion matrix comprised of jump classification probabilities. Theoretical and Monte Carlo analysis indicate that microstructure noise biases the tests against detecting jumps, and that a simple lagging strategy corrects the bias. Empirical work documents evidence for jumps that account for seven percent of stock market price variance. Building on realized variance and bi-power variation measures constructed from high-frequency financial prices, the second chapter proposes a simple reduced form framework for modelling and forecasting daily return volatility. The chapter first decomposes the total daily return variance into three components, and proposes different models for the different variance components: an approximate long-memory HAR-GARCH model for the daytime continuous variance, an ACH model for the jump occurrence hazard rate, a log-linear structure for the conditional jump size, and an augmented GARCH model for the overnight variance. Then the chapter combines the different models to generate an overall forecasting framework, which improves the volatility forecasts for the daily, weekly and monthly horizons. The third chapter studies the economic factors that generate financial market volatility and jumps. It extends the recent literature by separating market responses into continuous variance and discontinuous jumps, and differentiating the market’s disagreement and uncertainty. The chapter finds that there are more large jumps on news days than on no-news days, with the fixed-income market being more responsive than the equity market, and non-farm payroll employment being the most influential news. Surprises in forecasts impact volatility and jumps in the fixed-income market more than the equity market, while disagreement and uncertainty influence both markets with different effects on volatility and jumps. JEL classification: C1, C2, C5, C51, C52, F3, F4, G1, G14 / Dissertation

This thesis provides evidence of how macroeconomic surprises, constructed as deviations from market expectations, impact daily spread changes of Czech, Polish and Hungarian (CEEC-3) government bonds and sovereign credit default swaps. Firstly, we carried out series of event studies that inspect the spreads' reactions to the announcements. Subsequently, we employed the general-to-specific modeling approach and arrived at thirty GARCH-type models that consider surprises' impact on both conditional mean and variance. We have found significant impacts on the mean, yet in terms of magnitude, the impact of macroeconomic surprises has not been superior to that of broad financial factors. The impact on spreads' volatility appears more consequential though it lacks a clear pattern: Both good and bad news have been found to affect the volatility in either direction. Our findings suggest that with respect to macroeconomic news, daily changes of the bond spreads are driven rather by inflation expectations than by credit risk considerations. Foreign news proxied by the German surprises seems to affect the CEEC-3 bond spreads mainly through the risk-free proxy - the German Bund yield. Contrary to studies using low-frequency macroeconomic data, we have found no evidence for the "wake-up call" hypothesis.

The thesis contains four essays, covering topics in the field of real-time macroeconometrics, forecasting and applied macroeconomics. In the first two chapters, I use recent techniques developed in the "nowcasting" literature in order to analyse and interpret the macroeconomic news flow. I use them either to assess current macroeconomic conditions, showing the importance of foreign indicators dealing with small open economies, or linking macroeconomic news to asset prices, through a model that help us interpret macroeconomic data and explaining the linkages between macro variables and financial indicators. In the third chapter, I analyse the link between macroeconomic data in real-time and the yield curve of interest rates, constructing a forecasting model which takes into account the peculiar characteristics of the macroeconomic data flow. In the last chapter, I present a Bayesian Vector Autoregression model built in order to analyse the last two crisis in the Eurozone (2008-09, and 2011-12) identifying their unique characteristics with respect to historical regularities, an issue of great importance from a policy perspective. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

Les marchés financiers sont sujets quotidiennement à la diffusion de statistiques économiques ainsi que leurs prévisions par des institutions publiques et privées. Ces annonces sont prévues ou non prévues. Les annonces prévues sont organisées selon un calendrier connu à l’avance par tous les opérateurs. Ces annonces telles que les statistiques d'activité, d’exportation ou de sentiments, sont publiées une fois par mois par des agences spécialisées telles que Bloomberg. La diffusion d’une statistique économique ou financière est toujours précédée par la publication de sa prévision calculée comme la médiane de toutes les prévisions individuelles fournies par les agents. Cette médiane est un proxy de la vision commune des opérateurs et aide à la construction d'une représentation collective de l'environnement économique. Le premier chapitre de ma thèse a pour objectif d'analyser l'hétérogénéité dans la prévision des annonces macroéconomiques est testée grâce à des données mensuelles de prévisions issues d'enquêtes conduites par Bloomberg, sur une série d'indicateurs macroéconomiques. S’ensuit alors une deuxième problématique. Quels sont aux yeux des investisseurs, les critères qui permettent de considérer qu’une annonce est plus importante qu’une autre ? L’analyse du processus par lequel une information est incorporée dans les cours, nous a éclairés sur l’existence d’une forte rotation dans les statistiques considérées comme importantes (Market Mover indicators). Le deuxième chapitre tente donc de répondre à cette problématique. Dans un dernier chapitre je m’interroge sur la dynamique des prix post-publications d’annonces macroéconomiques et financières. Des connections sont réalisées entre les Jumps sur les cours des actifs et les annonces macroéconomiques, financières mais aussi imprévues. / Financial markets are subjected daily to the diffusion of economic indicators and their forecasts by public institutions and even private ones. These annoncements can be scheduled or unscheduled. The scheduled announcements are organized according to a specific calendar and known in advance by all operators. These news such as activity indicators, credit, export or sentiments’ surveys, are published monthly or quarterly by specialized agencies to all operators in real time. Our thesis contributes to diferent literatures and aims to thoroughly analyze the three phases of the diffusion process of new information on financial markets : anticipation of the announcement before its publication, interest that arouse its publication and impact of its publication on market dynamics. The aim of the first chapter is to investigate heterogeneity in macroeconomic news forecasts using disaggregate data of monthly expectation surveys conducted by Bloomberg on macroeconomic indicators from January 1999 to February 2013. The second chapter examines the impact of surprises associated with monthly macroeconomic news releases on Treasury-bond returns, by paying particular attention to the moment at which the information is published in the month. In the third chapter we examine the intraday effects of surprises from scheduled and unscheduled announcements on six major exchange rate returns (jumps) using an extension of the standard Tobit model with heteroskedastic and asymmetric errors.

This dissertation thesis consists of three essays on new EU foreign exchange markets (FX), i.e. the Czech koruna, Polish zloty and Hungarian forint. In the first two essays, the impact of foreign macroeconomic news announcements and central banks' monetary policy settings on the value and volatility of examined exchange rates is analyzed. In the third chapter, the conditional comovements and volatility spillovers on new EU FX markets is examined. The aim of this thesis is to contribute to the existing empirical literature by providing new evidence of the examined currencies during periods, which have not been examined yet (after the Global financial crisis (GFC), during the EU debt crisis and during currency interventions in the Czech Republic). The first essay (Chapter 2) examines the impact of Eurozone/Germany and US macroeconomic news announcements and monetary policy settings of the ECB and the Fed on the value of new EU member states' currencies. It is a complex analysis of 1-minute intraday dataset performed by event study methodology (ESM). We observe different reactions of exchange rates in pair with the US dollar on the US macroeconomic announcements and Euro-expressed FX rates on Germany macro news during the EU debt crisis and after it. We also provide evidence of leaking news, showing...