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- Global economy, globalization, financial system, financial crisis, European Union, European integration, financial governance, systemic risk (1)
- economics, behavioral finance theory, financial econometrics, financial markets, investor sentiment, attention, time series, nonlinearities, structural breaks, volatility, information, social media platforms (1)
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With the ripple effects of the global financial crisis of 2008 exhibiting enduring rifts in the global economy to date, an assessment of the crisis as being rooted in both market and regulatory failure sheds light on the significance and the severity of the challenges cross-border financial capitalism presents nation states with in the wake of globalization. As externalities increase, the threats the unprecedented interdependence and instability of the modern financial system pose are unlikely to recede; on the contrary, they are bound to
become more pressing. This is of considerable significance for financial governance, implying that sovereign nation states – formally legitimized to conduct regulatory functions – must construct robust cross-border structures to cope with the challenges of governing an inherently crisis-prone system.
In an attempt to address the underlying shortcomings exposed by the crisis – among them that the regulatory and supervisory architecture was not commensurate with the complexity and sophistication of financial markets – the European Union embarked on an ambitious reform path. The potential capacity of European integration in this regard, though central in the academic debate, has yet to be analyzed systematically with respect to systemic risk in terms of both its systemic qualities and political embeddedness. Drawing on a refined definition thereof set out by Willke et al. (2013), this research aims to shed light on how these themes resonate in the European context to inform the critical analysis of
conducted reforms. Based on the assumption that cross-border finance requires integrated governance schemes to ensure its integrity and efficacy, the central goals are to (i) assess both systemic-risk related reform measures and the challenges they are confronted with, and (ii) illuminate the significance of reform, while underpinning the case for enhanced integration.
Drawing on a broad theoretical framework combining insights from various EU integration theories to trace the rationale and assess the potential and significance of supranational integration, and constructing an analytical framework within which to assess the order-, legitimacy- and expertise-related challenges current structures are confronted with, i.e. factors inhibiting governance capacity, the research concludes that though substantive reforms have largely failed to address the core systemic issues exposed by the crisis, there has indeed been substantial progress in terms of the reform of the institutional governance
architecture at the European level. While monumental challenges remain, it would be premature to discredit the response in its entirety. The analysis highlights the European Union’s remarkable capacity to adjust, with institutional responses essentially at the boundaries of legal and political feasibility. Given what is at stake, however, it contends that – with a view to future challenges – supranational governance regimes remain short of optimal scope and must be strengthened to forestall the gradual erosion of governance capacity vis-à-vis an increasingly interdependent and fragile financial system.
This doctoral thesis is concerned with two separate but intertwined topics in the field of financial econometrics: (i) the measurement and relevance of new sources of information on financial markets in the form of online investor sentiment and attention and (ii) nonlinearities in financial time series in the form of structural breaks. According to classical finance theory, competition among rational investors, often called arbitrageurs, leads to an equilibrium in which prices on capital markets equal the present value of expected future cash flows. Under this theoretical lens, the trading decisions of irrational investors have no significant impact on prices since their demands are offset by rational investors. However, the classical finance theory fails to fit the extreme levels of and changes in stock prices corresponding to events such as the Great Crash of 1929 or the Dot.com bubble of the 1990s, which are difficult to align with any rational explanation. Akin to the notion of "animal spirit" first coined by Keynes (1936), behavioral finance theory sets out to augment the classical model by explicitly taking into account two assumptions: Firstly, trading activities of investors are thought of to be partially influenced by subjective beliefs about investment risks and future cash flows, generally referred to as investor sentiment. Secondly, there are limits to arbitrage in the sense that betting against sentiment-driven investors is associated with higher risks and costs. Thus, inconsistent with predictions of the classical finance theory, arbitrageurs do not aggressively force prices to fundamentals. On this basis, irrational (collective) investor behavior has moved into the focus of modern finance theory and corresponding empirical applications. The widespread internet access and usage of social media platforms in recent years have led to new sources of information - and with them new sources and types of data that can be used by researchers and practitioners alike - pertaining to this collective investor behavior and corresponding financial market outcome: Short messages published on social media platforms such as Twitter or StockTwits on the one hand and online search queries on the other. The first part of this thesis makes use of such data in empirical financial applications, also from a high-frequency intraday perspective, in order to assess its impact on predictions of financial variables and to unravel new relationships. In general, it is reasonable to assume that many relationships in economics and finance are nonlinear. Thus, several kinds of nonlinearities can arise when considering financial markets and time series of financial variables that are not necessarily approximated well by simple linear models. Relating to the behavioral finance literature, the model of De Long et al. (1990) proposes that in the presence of sentiment-prone noise traders the price of a risky asset evolves as a nonlinear function of these noise traders' average bullishness (i.e., their mean misperception of the expected price) and its variance. Though being of a different philosophical nature than sentiment-induced noise trader theories, some other models of trade based on noninformational reasons, such as changes in risk aversion or liquidity needs, also involve nonlinear relations. The second part of the thesis focuses on one often overlooked kind of nonlinearity that entails potentially more severe implications, namely structural breaks in financial time series. Structural breaks, also referred to as change-points, in the data generating process underlying a given univariate time series do not only constitute a source of nonlinearity that can be modeled but also a more subtle source of nonstationarity. Given that endeavors of time series model building and prediction usually demand some stationarity assumption to be made, the latter poses a common problem in the analysis of univariate economic and financial time series. Matters are complicated by the fact that the exact number and timing of structural breaks are usually unknown ex-ante. Therefore, the consistent estimation of structural breaks, or change-points, has been studied extensively in the related literature. This thesis adds to the ongoing discussion by proposing a two-step model selection procedure for the detection and timing of change-points in structural break autoregressive models. A similar methodology is then used to investigate the effect of Box-Cox transforms on the estimation of structural breaks in realized volatility time series.
Throughout the last decades, investigations on market linkages and investor behavior in times of turmoil and uncertainty have received the attention of researchers and financial practitioners alike. This dissertation offers five distinct research papers which contribute to the existing literature on this overarching topic. First, we provide a thorough analysis of the time-varying linkages between regional and global equity markets. Second, and in line with the notion of increasing equity market integration over time, we investigate different types of flights to quality in times of stock market turmoil. Third, we provide novel empirical evidence on the usefulness of new sources of information on investor behavior towards the measurement of financial market linkages. Fourth, building on the increasing relevance of these new sources of information, we demonstrate that different measures for online investor attention do not necessarily constitute equivalent proxies for the latent variable. Last, we contribute to the strands of financial literature dealing with the estimation of dynamic linkages between financial markets and variables in the form of time-varying correlations. More specifically, we propose a score-driven extension to the well-known dynamic conditional correlation model which provides a means to quantify the time-varying influence of news on correlation dynamics. Taking the severe impact of recent and current crisis events on financial markets into consideration, the research papers comprised in this dissertation are of uttermost importance for financial market participants.
