The global minimum-variance portfolio is a typical choice for investors because of its simplicity and broad applicability. Although it requires only one input, namely the covariance matrix of asset returns, estimating the optimal solution remains a challenge. In the presence of high-dimensionality in the data, the sample covariance estimator becomes ill-conditioned and leads to suboptimal portfolios out-of-sample. To address this issue, we review recently proposed efficient estimation methods for the covariance matrix and extend the literature by suggesting a multi-fold cross-validation technique for selecting the necessary tuning parameters within each method. Conducting an extensive empirical analysis with three datasets based on the Russell 3000, we show that the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio. In addition, we identify estimators that are strongly influenced by the choice of the tuning parameter and detect a clear relationship between the selection criterion within the cross-validation and the evaluated performance measure.

This project aims at creating an investment device to help investors determine which real estate units have a higher return to investment in Madrid. To do so, we gather data from Idealista.com, a real estate web-page with millions of real estate units across Spain, Italy and Portugal. In this preliminary version, we present the road map on how we gather the data; descriptive statistics of the 8,121 real estate units gathered (rental and sale); build a return index based on the difference in prices of rental and sale units(per neighbourhood and size) and introduce machine learning algorithms for rental real estate price prediction.

We analyze social network data from Twitter to uncover early-warning signals of COVID-19 outbreaks in Europe in the winter season 2020, before the first public announcements of local sources of infection were made. We show evidence that unexpected levels of concerns about cases of pneumonia were raised across a number of European countries. Whistleblowing came primarily from the geographical regions that eventually turned out to be the key breeding grounds for infections. These findings point to the urgency of setting up an integrated digital surveillance system in which social media can help geo-localize chains of contagion that would otherwise proliferate almost completely undetected.

One of the impediments in advancing actuarial research and developing open source assets for insurance analytics is the lack of realistic publicly available datasets. In this work, we develop a workflow for synthesizing insurance datasets leveraging CTGAN, a recently proposed neural network architecture for generating tabular data. Applying the proposed workflow to publicly available data in the domains of general insurance pricing and life insurance shock lapse modeling, we evaluate the synthesized datasets from a few perspectives: machine learning efficacy, distributions of variables, and stability of model parameters. This workflow is implemented via an R interface to promote adoption by researchers and data owners.

We consider a problem of finding an SSD-minimal quantile function subject to the mixture of multiple first-order stochastic dominance (FSD) and second-order stochastic dominance (SSD) constraints. The solution is explicitly worked out and has a closed relation to the Skorokhod problem. We then apply this result to solve an expenditure minimization problem with the mixture of an FSD constraint and an SSD constraint in financial economics.

In this paper, we introduce and develop the theory of semimartingale optimal transport in a path dependent setting. Instead of the classical constraints on marginal distributions, we consider a general framework of path dependent constraints. Duality results are established, representing the solution in terms of path dependent partial differential equations (PPDEs). Moreover, we provide a dimension reduction result based on the new notion of "semifiltrations", which identifies appropriate Markovian state variables based on the constraints and the cost function. Our technique is then applied to the exact calibration of volatility models to the prices of general path dependent derivatives.

In this paper, we investigate whether mixing cryptocurrencies to a German investor portfolio improves portfolio diversification. We analyse this research question by applying a (mean variance) portfolio analysis using a toolbox consisting of (i) the comparison of descriptive statistics, (ii) graphical methods and (iii) econometric spanning tests. In contrast to most of the former studies we use a (broad) customized, Equally-Weighted Cryptocurrency Index (EWCI) to capture the average development of a whole ex ante defined cryptocurrency universe and to mitigate possible survivorship biases in the data. According to Glas/Poddig (2018), this bias could have led to misleading results in some already existing studies. We find that cryptocurrencies can improve portfolio diversification in a few of the analyzed windows from our dataset (consisting of weekly observations from 2014-01-01 to 2019-05-31). However, we cannot confirm this pattern as the normal case. By including cryptocurrencies in their portfolios, investors predominantly cannot reach a significantly higher efficient frontier. These results also hold, if the non-normality of cryptocurrency returns is considered. Moreover, we control for changes of the results, if transaction costs/illiquidities on the cryptocurrency market are additionally considered.

Interactive simulation toolkits come in handy when teaching macroeconomic models by facilitating an easy understanding of underlying economic concepts and offering an intuitive approach to the models' comparative statics. Based on the example of the IS-LM model, this paper demonstrates innovative browser-based features well-suited for the shift in education to online platforms accelerated by COVID-19. The free and open-source code can be found alongside the standalone HTML files for the AD-AS and the Solow growth model at https://gitlab.tu-berlin.de/chair-of-macroeconomics/.