Time Dependent Relative Risk Aversion

Risk management and the thorough understanding of the relations between financial markets and the standard theory of macroeconomics have always been among the topics most addressed by researchers, both financial mathematicians and economists. This work aims at explaining investors’ behavior from a macroeconomic aspect (modeled by the investors’ pricing kernel and their relative risk aversion) using stocks and options data. Daily estimates of investors’ pricing kernel and relative risk aversion are obtained and used to construct and analyze a three-year long time-series. The first four moments of these time-series as well as their values at the money are the starting point of a principal component analysis. The relation between changes in a major index level and implied volatility at the money and between the principal components of the changes in relative risk aversion is found to be linear. The relation of the same explanatory variables to the principal components of the changes in pricing kernels is found to be log-linear, although this relation is not significant for all of the examined maturities.risk aversion, pricing kernels, time dependent preferences

Bayesian Networks and Sex-related Homicides

We present a statistical investigation on the domain of sex-related homicides. As general sociological and psychological theory on this specific type of crime is incomplete or even lacking, a data-driven approach is implemented. In detail, graphical modelling is applied to learn the dependency structure and several structure learning algorithms are combined to yield a skeleton corresponding to distinct Bayesian Networks. This graph is subsequently analysed and presents a distinction between an offender and a situation driven crime.Bayesian Networks, structure learning, offender profiling

Rating Companies with Support Vector Machines

The goal of this work is to introduce one of the most successful among recently developed statistical techniques - the support vector machine (SVM) - to the field of corporate bankruptcy analysis. The main emphasis is done on implementing SVMs for analysing predictors in the form of financial ratios. A method is proposed of adapting SVMs to default probability estimation. A survey of practically and commercially applied methods is given. This work proves that support vector machines are capable of extracting useful information from financial data although extensive data sets are required in order to fully utilise their classification power.Support vector machines; Company rating; Default probability estimation

SFB 649 Discussion Paper 2006-075 Inhomogeneous Dependency Modelling with Time Varying

Measuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of applications, though, requires a modelling framework different from the multivariate normal. In risk management the non-normal behaviour of most financial time series calls for nonlinear (i.e. non-gaussian) dependency. The correct modelling of non-gaussian dependencies is therefore a key issue in the analysis of multivariate time series. In this paper we use copulae functions with adaptively estimated time varying parameters for modelling the distribution of returns, free from the usual normality assumptions. Further, we apply copulae to estimation of Value-at-Risk (VaR) of a portfolio and show its better performance over the RiskMetrics approach, a widely used methodology for VaR estimation. JEL classification: C 1

A liquidity constrained investment approach

Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have a low liquidity compared to traditional assets, one needs to take into account liquidity issues when one puts them into the same portfolio. We propose use a LIquidity Bounded Risk-return Optimization (LIBRO) approach, which is a combination of the Markowitz framework under the liquidity constraints. The results show that cryptocurrencies add value to a portfolio and the optimization approach is even able to increase the return of a portfolio and lower the volatility risk. The codes used to obtain the results in this paper are available via www.quantlet.d

Data science & digital society

Data Science looks at raw numbers and informational objects created by different disciplines. The Digital Society creates information and numbers from many scientiHic disciplines. The amassment of data though makes is hard to Hind structures and requires a skill full analysis of this massive raw material. The thoughts presented here on DS2 - Data Science & Digital Society analyze these challenges and offers ways to handle the questions arising in this evolving context. We propose three levels of analysis and lay out how one can react to the challenges that come about. Concrete examples concern Credit default swaps, Dynamic Topic modeling, Crypto currencies and above all the quantitative analysis of real data in a DS2 context

Support Vector Machines: eine neue Methode zum Rating von Unternehmen

Die Rekordzahlen an Unternehmensinsolvenzen, die schlechte Ertragslage der deutschen Kreditinstitute in den vergangenen Jahren und der von Basel II ausgehende Druck zur Verwendung von realitätsnahen Ausfallwahrscheinlichkeiten haben es überdeutlich gemacht: Der Bedarf an leistungsfähigen Insolvenzprognosemodellen ist immens. Mehr denn je suchen Banken, aber auch andere Finanzdienstleister wie Venture-Capital-Firmen nach geeigneten Methoden, um möglichst treffsicher das Risiko abschätzen zu können, mit dem ein Unternehmen während einer gegebenen Vorhersageperiode insolvent wird. Fehleinschätzungen bei den Ausfallraten von Krediten haben nicht nur einzelwirtschaftliche Auswirkungen. Auch die Stabilität des Finanzsystems und damit die Liquiditätsversorgung der Volkswirtschaft hängen entscheidend davon ab, ob Banken ihre Kreditrisiken richtig einschätzen und eine adäquate Risikovorsorge treffen können. Forschungsarbeiten des DIW Berlin in Zusammenarbeit mit dem Center for Applied Statistics and Economics (CASE, Humboldt-Universität zu Berlin) haben ergeben, dass Support Vector Machines, kurz SVMs, den Anforderungen an qualitativ hochstehende Insolvenzprognosemodelle in besonderer Weise gerecht werden. Support Vector Machines als Instrument zur Vorhersage von Insolvenzen zu nutzen ist neu. Bei Klassifikationsproblemen in der Biometrie (Früherkennung von Krankheiten) und im Bereich der Mustererkennung werden SVMs jedoch bereits seit geraumer Zeit mit guten Ergebnissen eingesetzt.