Master-Arbeit Univ. St. Gallen, 2008.

University, Diss., 2001--Mannheim.

University, Diss., 2003--Kaiserslautern.

Universiẗat, Diss.--Jena, 2004.

M.Comm. / This study analyzes the effect of the subprime crisis on portfolio allocation from the perspective of dependence structure. Empirical evidence has proved that the multivariate normal distribution is inadequate to model portfolio asset return distribution - firstly because the empirical marginal distributions of asset returns are skewed and fat tailed; and secondly because it does not consider the possibility of extreme joint co-movement of asset returns (Fama and French, 1993; Richardson and Smith, 1993; Géczy, 1998; Longin and Solnik, 2001; Mashal and Zeevi, 2002). This study employs Archimedean copulas to capture both the dependence structure and the asymmetry of asset returns in the tails of the empirical distributions.

Die Auswahl des attraktivsten Portfolios von Investitionsprojekten zählt zu den kritischen Ma-nagementaufgaben. Angesichts mehrfacher Zielsetzungen und komplexer Projektabhängigkeiten bietet sich dazu ein zweistufiges Vorgehen an, das zunächst effiziente Portfolios identifiziert und den Entscheidungsträger anschließend bei der Suche in diesem Lösungsraum unterstützt. Bei einer großen Zahl an Vorschlägen können die möglichen Projektkombinationen aber nicht mehr in akzeptabler Zeit vollständig enumeriert werden. Adaptierte Meta-Heuristiken bieten hier einen Kompromiß zwischen dem Wunsch nach exakter Bestimmung aller Pareto-optimalen Investitionsprogramme und dem dazu nötigen Rechenaufwand. Dieser Beitrag beschreibt den entsprechenden Einsatz künstlicher Ameisen und diskutiert erste numerische Ergebnisse. / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Thesis advisor: Zhijie Xiao / My dissertation research examines empirical issues in financial economics with a special focus on the application of quantile regression. This dissertation is composed by two self-contained papers, which center around: (1) robust estimation of conditional idiosyncratic volatility of asset returns to offer better understanding of market microstructure and asset pricing anomalies; (2) implementation of coherent risk measures in portfolio selection and financial risk management. The first chapter analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation errors in the presence of non-Gaussianity, casting strong doubt on the positive intertemporal idiosyncratic volatility effect reported in the literature. We propose an alternative estimator for conditional idiosyncratic volatility for GARCH-type models. The proposed estimation method does not require error distribution assumptions and is robust non-Gaussian innovations. Monte Carlo evidence indicates that the proposed estimator has much improved sampling performance over the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-section portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang and Zhang (2006) exists intertemporally, i.e., stocks with high conditional idiosyncratic volatility earn abnormally low returns. We solve the major piece of this puzzle by pointing out that previous empirical studies have failed to consider both idiosyncratic variance and individual conditional skewness in determining cross-sectional returns. We introduce a new concept - the "expected windfall" - as an alternative measure of conditional return skewness. After controlling for these two additional factors, cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges. The second chapter examines portfolio allocation decision for investors with general pessimistic preferences (GPP) regarding downside risk aversion and out-performing benchmark returns. I show that the expected utility of pessimistic investors can be robustly estimated within a quantile regression framework without assuming asset return distributions. The asymptotic properties of the optimal portfolio weights are derived. Empirically, this method is introduced to construct the optimal fund of CSFB/Tremont hedge-fund indices. Both the in-sample and out-of-sample backtesting results confirm that the optimal mean-GPP portfolio outperforms the mean-variance and mean-conditional VaR portfolios. / Thesis (PhD) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Thesis (doctoral)--Universität, Kiel, 2000.

Die Möglichkeiten einer Private Equity- oder Venture Capital Gesellschaft Risikopolitik zu betreiben, wurden bisher in der wissenschaftlichen Literatur noch nicht hinreichend untersucht. Diese Arbeit analysiert die Möglichkeiten, welche für Private Equity- und Venture Capital Gesellschaften bestehen um ihre Risikosituation zu optimieren. Der Autor entwirft ein Risikopolitikmodell für die untersuchten Gesellschaften und entwickelt daraus zwei verschiedene Ansätze, mit denen eine Private Equity- bzw. Venture Capital Gesellschaft ihr Risiko steuern kann: Vor dem theoretischen Hintergrund der Neo-institutionellen Ökonomie wird die Beziehung zwischen der betroffenen Gesellschaft und einem einzelnen Portfoliounternehmen analysiert. Der theoretische Ansatz der Portfoliotheorie wird für die Untersuchung des übergeordneten Regelkreises der Risikopolitik herangezogen, der sich aus dem Portfolio von Beteiligungsunternehmen ergibt. Die dabei generierten Hypothesen wurden im Anschluss mit Hilfe der Ergebnisse einer empirischen Untersuchung analysiert. Dabei wurde festgestellt, dass für österreichische Private Equity- und Venture Capital Gesellschaften die Beziehung zu einem einzelnen Beteiligungsunternehmen sehr relevant ist, wohingegen Diversifikations-überlegungen weniger Bedeutung beigemessen wird. (Autorenref.)

Second order stochastic dominance is an optimal rule for portfolio selection of risk averse investors when we only know that the investors' utility function is increasing concave. The main advantage of SSD is that it makes no assumptions regarding the return distributions of investment assets and has been proven to lead to utility maximization for the class of increasing concave utility functions. A number of different SSD models have emerged in the literature for portfolio selection based on SSD. However, current SSD models produce the same SSD efficient portfolio for all risk averse investors, regardless of their risk aversion degree. In this thesis, we have developed a new SSD efficiency model, SSD-DP, which unlike existing SSD efficiency models in the literature, provides an SSD efficient portfolio as a function of investors' risk aversion degrees. The SSD-DP model is based on the linear programming technique and finds an SSD efficient portfolio by minimizing the dual power transform (DP) of a weighted portfolio of assets for a given risk aversion degree. We show that the optimal portfolio of the proposed model is SSD efficient, i.e. it is not dominated by SSD by any other portfolio, and, through empirical studies of historical data, we show that the method is a promising tool for constructing trading strategies.