1,720,996 research outputs found
Exogene Variablen in dynamischen bedingten Korrelationsmodellen für Finanzmärkte
No full textIn this dissertation, I analyze determinants of conditional correlations. Specifically, I propose the generalized DCCX model that facilitates the analysis of the effects of exogenous variables such as macroeconomic announcements or other financial time series on conditional correlations. Furthermore, I show that it is necessary to take into account the effect of exogenous variables on conditional variances and demonstrate that employing a GARCHX model for variances is helpful. I test the model with several datasets. I find that conditional correlations between stocks and bonds increase as risk aversion rises even when controlling for macroeconomic announcements. While most macroeconomic news result in falling conditional correlations, the publication of news concerning future interest rates or inflation figures moves bond and stock prices in the same direction
Untersuchungen zur Direction-of-Change Prognose im Querschnitt von Aktienuniversen
No full textDie Arbeit versucht die Prognose der Richtung von Aktienkursen im Querschnitt. Dazu werden verschiedene Richtungsindikatoren identifiziert, die Richtungsprognose im Querschnitt in die Literatur eingeordnet und 2 aufeinander aufbauende empirische Untersuchung durchgeführt. Anhand der Ergebnisse der Analysen wird anschließend eine Anlagestrategie entwickelt. Die Analysen zeigen dabei, dass Prognosen zwar in gewissem Maße möglich sind, aber der Aufbau von profitablen Anlagestrategien anhand dieser Prognosen scheitert an Transaktionskosten
Eignen sich agentenbasierte Simulationsmodelle zur Value-at-Risk Prognose an Finanzmärkten?
No full textIn dieser Thesis wird die Vorhersagekraft von Value-at-Risk-Prognosen analysiert, welche von agentenbasierten Modellen erstellt werden. Um eine allgemeine Approximation der Modellparameter zu erhalten, wird die erste Kalibrierung mit der Methode der simulierten Momente durchgeführt. Für eine Abschätzung der ABM am aktuellen Zeitrand, wird anschließend eine Rolling-Window Maximum-Likelihood-Kalibrierung sowie eine Rolling-Window sequenzielle Monte-Carlo-Schätzung angewandt. Die VaR-Prognosen werden dann durch eine weitere zeitliche Iteration der Modelle generiert. Die Ergebnisse zeigen, dass ABM nicht nur für VaR-Prognosen geeignet, sondern auch in der Lage sind, die Prognosegüte gängiger Benchmarkmodelle zu übertreffen. Insbesondere kann festgestellt werden, dass ABM in hochvolatilen Rezessionsphasen besser abschneiden als die in der Praxis dafür verwendeten GARCH-Modelle, wodurch ABM die beste Wahl für VaR-Prognosen in Zeiten mit hohem Verlustrisiko sind
Multi-Agenten Modelle zur Modellierung von Maerkten auf Basis Neuronaler Netze
No full textOne of the challenges of financial research is to develop models that are capable of explaining and forecasting market price movements and returns.Agent based models focus directly on the underlying structure of the market. The basic idea is, that the market price dynamics arises from the interaction of many individual agents. Approaching financial markets in this manner, one starts off with the modeling of the agents´ decision making schemes on the microeconomic level of the market. Thereafter, market price changes can be determined on the macroeconomic level by a superposition of the agents´ buying and selling decisions. The aim of a (micro-)economic model is to explain market prices by a detailed causal analysis of the agents´ decision making behavior. The market price results from an aggregation of the agents´ decisions. Remarkably, agent-based financial markets provide a new explanatory framework supplementing the traditional economic concepts of equilibrium theory and efficient markets. Such a supplementing framework is needed, because in real-world financial markets the underlying assumptions of equilibrium or efficient market theory are often violated.As we will show, neural networks allow the integration of the decision behavior of individual economic agents into a market model. Based on the perspective of interacting agents, the resulting market model allows us to capture the underlying dynamics of financial markets, to fit real-world financial data, and to forecast future market price movements.In addition, we point out that neural networks allow to set up a joint framework of econometric model building. Besides the learning from data, one may integrate prior knowledge about the underlying dynamical system and first principles into the modeling. These elements are incorporated into the neural networks in form of architectural enhancements. This way of model building helps to overcome the drawbacks of purely data driven approaches
Essays on international asset pricing, cultural finance, and the price effect
No full textThis dissertation is not only a pioneer work in the new finance sphere cultural finance, but also a feat of fundamental research in international empirical asset pricing. I present significant evidence that the most basic stock characteristic, the nominal price, is consequential for stock returns (and associated with higher statistical moments) in a comprehensive cross-country dataset comprising 41 countries and a culture-dependent capital market anomaly (as it was already shown e.g. for the momentum effect).
For the case of Germany, I additionally provide an in-depth analysis of the price effect (i.e. a high/low price of an asset goes hand in hand with high/low subsequent returns) as this country offers a unique possibility to investigate the evolution and trigger of this genuinely price-based capital market anomaly due to a rapid and dramatic countrywide dispersion of stock prices in the aftermath of law amendments.
Furthermore, I find the explanatory power of risk factor mimicking hedge portfolios (especially RMRF, HML, and WML, i.e. the beta, value, and momentum factors), which are consistently implemented in empirical asset pricing models (like the FF 3-, 5-, and 6-factor models and the Carhart 4-factor model), as well as their effectiveness as investment styles to vary across cultures.
That is, the spectrum of this dissertation strikes both implications of the weak EMH that time series data (like the price) should have no informational value for future returns and assumptions of theoretical asset pricing models that (only) systematic risk (CAPM), future investment opportunities (ICAPM) or consumption risk (CCAPM) drives asset returns (universally).
Finally, yet importantly, I find evidence that even cultural characteristics in itself (measured via the cultural dimensions of Hofstede and others) have explanatory and predictive power for global, cross-sectional stock returns as well as characteristics-based (hedge) portfolio returns. By virtue of these contributions to pertinent financial research, this dissertation is an empirical primer for possible future fields of research culture-based/culture-neutral asset pricing, asset management, and asset allocation
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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