Asset Clusters and Asset Networks in Financial Risk Management and Portfolio Optimization

In this work we use explorative statistical and data mining methods for financial applications like risk management, portfolio optimization and market analysis. The outcomes are visualized and the relations are quantified by mathematical measures. Researchers, analysts and decision makers can visually explore the structures and can carry out management initiatives based on automatic measures provided by the system. There are example applications to equity and loan portfolios

Maschinelle Intelligenz für Asset-Allokation und Portfoliokonstruktion

Maschinelle Intelligenz ist heute in der Lage, komplexe Zusammenhänge in Kapitalmarktportfolios zuverlässig zu erfassen und in die Portfoliokonstruktion und -steuerung zu integrieren. Es werden robustere Ergebnisse als mit den traditionellen Ansätzen nach „Markowitz“ und „Risk Parity“ erzielt.  Das liegt an einer neuen Qualität im Diversifikations- und Risikomanagementprozess. Zudem lassen sich effiziente, zunehmend digitalisierte Investmentprozesse realisieren. Dies gilt zum Beispiel für Aktien- und Multi-Asset-Portfolios im Bereich Smart Beta, Faktor-Investing und ESG

Jetzt absichern! Aber wie?

Das dritte Top des Aktienmarkts innerhalb von 15 Jahren fühlt sich anders an als die beiden Vorgänger in den Jahren 2000 und 2007. Den niedrigen Volatilitäten zum Trotz trauen die Anleger der ­Situation nicht. Welche Möglichkeiten gibt es zur Absicherung

Dynamische Korrelationen : Wegweiser für Managed Futures

Die liquiditätsgetriebene Markterholung der vergangenen Jahre hat den Trendfolger-Strategien nicht genützt. Der Aufwärtstrend an den Aktien- und Bondmärkten wurde durch die „Risk-on-/Risk-off“-Dynamik überlagert. In welchem Umfeld ist mit einem Trendwechsel zu rechnen? Aufschluss können dynamische Korrelationsanalysen geben

Handling risk-on/risk-off dynamics with correlation regimes and correlation networks

In this paper, we present a framework for detecting distinct correlation regimes and analyzing the emerging state dependences for a multi-asset futures portfolio from 1998 to 2013. These correlation regimes have been significantly different since the financial crisis of 2008 than they were previously; cluster tracking shows that asset classes are now less separated. We identify distinct “risk-on” and “risk-off” assets with the help of correlation networks. In addition to visualizing, we quantify these observations using suitable metrics for the clusters and correlation networks. The framework will be useful for financial risk management, portfolio construction, and asset allocation

Tail-risk protection trading strategies

We derive robust portfolio protection trading strategies by taking into account different aspects of time-variation and dynamics of distributional parameters of financial time series. To model financial time series we first account for the time-dependent dynamics of financial time series via a GARCH(1,1) process, which allows to incorporate volatility clustering and autoregressive behavior in volatility, both of which are well-documented stylized facts of financial time series. Second, we fit the GARCH residual (innovations) to different families of distributions including the Generalised Hyperbolic (GH) distribution and tempered stable distributions. Because of the their ability to incorporate a wide range of empirical stylized facts, both distribution families are popular in modelling financial data. In particular, the GH distribution contains the normal and Student-t-distributions as special cases. Aside from examining the time-varying behavior of the distributional parameters, we study the spread of the value-at-risk (VaR) between a non-normal and normal GARCH-innovation process. Because of the GARCH component, the magnitude of VaR, when viewed as a process over time, adapts quickly to changes in volatility. The distributional properties of the innovation process on the other hand provides information on skewness, excess kurtosis and in particular on the heaviness of the tails present in the data. The resulting VaR spread can therefore be used to derive an expectation of the frequency of extreme events which in turn generates signals of the temporal presence of tail risks. This information, in particular the information about potential ‘tail-risks’ contained in the short-term VaR is used to generate trading signals with the intention to protect against extreme losses and at the same time to not miss the upside. This portfolio protection trading strategy is compared to CPPI and protective put trading strategies, which are popular portfolio insurance strategies. Based on DAX returns from 1996 to 2013 we find that the tail-risk protection strategy outperforms the classical strategies, for example in terms of a higher Sharpe ratio and when comparing excess returns relative to maximum drawdown (Calmar ratio). These results are backed by robustness tests, e.g. by comparing the trading strategy to a randomly generated trading strategy