I am interested in a broad range of topics from the fields of (computational) econometrics, its application and all research about the German health sector.
My research which was internationally published or is at a mature stage is described in the following.
The Gain–Loss-Ratio, proposed by Bernardo and Ledoit (2000), can either be used as a performance measure on a market with known prices or to derive price intervals in incomplete markets. For both applications, there is a considerable theoretical drawback: it reaches infinity for nontrivial cases in many standard models with continuous probability space. In this paper, a more general ratio is proposed, which includes the original Gain–Loss-Ratio as a limit case. This “Substantial Gain–Loss-Ratio” is applicable in case of continuous probabilities. Additionally, in its function as a performance measure it helps illuminate the source of out-performance that a portfolio reveals.
(Published in Finance Research Letters Vol. 12C, Pages 58-66 as 'Weakening the Gain-Loss Ratio measure to make it stronger')
In the present paper a panel model, which describes the relationship of individual labor income and stock prices in Germany, is estimated. I identify groups of individuals that cluster concerning the model parameters that describe firstly the individual labor income dynamics and secondly the relationship between the individual labor income and financial markets. Methodically a Bayesian model-based non-Gaussian panel data approach, proposed by Juarez and Steel (2010) is used. A group of individuals with a high cluster assignment probability is found. The characteristics of this group, who’s individuals share the same autoregressive dynamics and a common, relatively high dependence on financial markets are investigated further. It can be shown that this group has a statistical significantly different partition of major occupational groups. This leads to implications for various literatures, such as the pricing of human capital contracts, the hedging of individual income risk, portfolio optimization or asset pricing.
(Published in Applied Economics Letters (2016): 1-4)
The Substantial-Gain-Loss Ratio (SGLR) was developed to overcome some drawbacks of the Gain- Loss-Ratio (GLR) by Bernardo and Ledoit (2000). It is a tool that allows working in continuous probability spaces without loosing the positive properties of the GLR. This is achieved by slightly changing the condition for a Good-Deal, i. e. on the most extreme but at the same time very small part of the statespace. The determination of price-intervals as well as the comparison of a portfolio performance in the light of different asset-pricing models on a theoretical level in models with continuous probability spaces becomes possible. Additionally, the source or distribution of differences in performance is illuminated via so-called b-diagrams.1 Although the SGLR has many attractive properties, it is not yet clear how to calculate it. So far it was only calculated for trivial cases, i.e. assuming a constant stochastic discount factor M = 1. This paper presents an algorithm for the calculation of the SGLR in empirical cases.
(Published in Computational Economics 54.2 (2019): 613-624)
In the 21st century, the digital revolution is finally taking place. In this paper a colleague and I identify several channels through which the internet effects labour income. We use multivariate time series analysis on German data to underpin our hypothesises.
(Published in The Empirical Economics Letters (2017): pp 73-81)
Human capital is a key economic factor in both macro- and microeconomics, and, at least for most people, by far their largest asset. Surprisingly, relatively little effort has been undertaken in the extant literature to empirically determine the value of individual human capital. This paper aims at closing this gap. We use the Substantial-Gain-Loss-Ratio to calculate Good-Deal bounds for securitizations of individual labour income one year ahead. Our procedure is applied to US data. We evaluate the attractiveness of hypothetical human capital contracts and can thereby identify investors’ favourites.
In this paper we propose a new tool that measures the attractiveness of a policy decision, based on a social welfare function in wealth and labor-income. To motivate the form of welfare function, it is derived from an simplifed life-cycle model of consumption and saving for a representative citizen. The new measure is motivated by the Gain-Loss-Ratio as in Bernardo and Ledoit (2000) and further takes up the idea of an inherent robustness-check as in Voelzke (2015). In particular it investigates the impact of small modifcations at the tails of the distribution.
The hedging potential of occupational income indices
Human capital is by far the largest asset for most individuals and many of them face therby a cluster risk that can't be hedged by classical financial markets. We investigate to which amount setting up and securitizing occupational indices allows to hedge parts of the labor income risk.
We empirically investigate the time-varying influence of speculative activity on returns volatility in Chinese futures markets for commodities. By analyzing the lead lag relationship between speculative activity and returns volatility over time, we aim at finnding out if either speculative activity destabilizes futures returns or if volatility drives speculative activity. To measure speculative activity a speculation ratio, defined as trading volume divided by open interest, is used. We apply a two-step time-varying VAR model with stochastic volatility to six heavily traded metal and agricultural contracts to show how the relationship between returns volatility and the speculation ratio evolves over time. Time-varying Granger causality tests as well as impulse response analyzes provide further insight into the dependence structure of speculation dynamics and returns volatility. Surprisingly, our results indicate that only volatility drives speculation and not vice versa.
(Published in Journal of Futures Markets 39.4 (2019): 405-417)
In this paper we investigate a panel-data set of future-market derived commodity prices concerning explosive behavior as an indication for speculative price bubbles. We provide test-results of the Generalised Sub-ADF test by Phillips et al. (2015) on ten time series of major commodities prices over a timespan of 35 years. To aggregate the test-results we use the stagewise rejective multiple test procedure by Hommel (1988).