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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 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.