Contributions in Merlin

One of the concerns in the debate on climate change is whether financial flows contribute to the reduction of emissions. This presentation looks at the role bond market- and bank-based debt play in the allocation of resources to fossil fuel in the context of the risk of stranded assets. The presentation shows that banks continue to provide financing to fossil fuel firms that the bond market would not finance as long as they do not price the risk of stranded assets. In this setting, stranded assets risks may have shifted to large banks.

We study the impact of economic policy uncertainty on the term structure of nominal interest rates. In a general equilibrium model populated by an uncertainty-averse agent, we show that political uncertainty not only affects the yield curve and the corresponding volatility term structure but also bond risk premia carry a premium for political uncertainty. Our model simultaneously captures both the shape of the yield curve and the hump shape of yield volatilities, a stylized feature that is hard to match with a theoretical model. Our model gives rise to a set of testable predictions for which we find strong support in the data: Higher policy uncertainty leads to a significant decline in yield levels and increases bond yield volatilities. Moreover, policy uncertainty predicts future short rates and has an ambiguous effect on term premia. Finally, short (long) maturity bond risk premia respond negatively (positively) to increases in policy uncertainty.

While the birthday effect has been widely studied in the economics and sport literature, this study introduces the concept of the ‘real’ birthday effect. It holds that birthday celebrations yield fatigue, which negatively affect running performance. The purpose of this study is to examine the effect of players’ birthdays on post-birthday running performance using data from the German Football Bundesliga (2011–2018). The dataset includes 1,040 different field players who played in 2,142 matches (n= 49,233). Running performance was measured by the total distance covered during the game (in m per minute) and the number of intensive runs (>20 and <24 km/h; per minute). On average, players ran 123.68 m and performed 0.70 intensive runs per minute, which are equivalent to 11.1 km and 63 intensive runs during a 90-minute match. The results of fixed effects panel regression analyses, which also control for various player, game, and team characteristics, show that players perform significantly fewer intensive runs on the day following their birthday. This finding supports the concept of the ‘real’ birthday effect. Football coaches should reconsider fielding players on match days succeeding their birthday as these players perform significantly fewer intensive runs.

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The thesis uses bitcoin data to estimate parameters, with which it is then tried to assess whether the model proposed in Gabaix and Koijen (2021) can explain the mean and standard deviation of bitcoin returns. The model uses the price elasticity of demand to generate observed prices and is stated in perturbations around a baseline. The results suggest that the model does not account for the full picture of return patterns observed. The moments resulting from the simulation are far larger in absolute terms than the observed moments and the estimation results are hardly statistically signi cant. Albeit, a parameter of the model, the speed of mean reversion of flows, has not been estimated, a back-of-the-envelope calculation implies a negative and very large value to counter the effects of the estimation results. A sensible estimation of this parameter is beyond the scope of this thesis. Bitcoin is well suited for price elasticity estimations because it is only marginally influenced by supply shocks i.e., all the shocks can be ascribed to the demand side. Moreover, the full record of transactions is in theory available to the public. A wide range of studies point to the effects trading has on prices, yet only recently has literature emerged arguing for inelastic  financial markets. The model presented in Gabaix and Koijen (2021) uses flows to funds to drive asset prices away from their fundamental values. This mechanism influences the stochastic discount rate in the model. For the estimations, two datasets are used. One dubbed `daily data' is provided by Stütz et al. (2020).1 This dataset consists of bitcoin transaction data aggregated on a daily and an entity level, transactions of bitcoin holders with only one address are omitted. The second called `block-level data' has been downloaded through the GraphSense API (Haslhofer et al., 2021). This dataset has been aggregated on a block-level and contains all transactions. Both datasets entail information about on-chain transactions. With this information, the estimations are performed on the transformed data. The data used is stated in deviations from its rolling averages. The rolling averages serve as a baseline. The results of the estimations suggest a positively inelastic demand and contrarian investing agents. The simulations suggest that the model overpredicts the volatility in bitcoin markets and overstates the premium paid by investors, in absolute terms.

Semantic information of 3D scans is a fundamental step for many scene understanding applications such as autonomous driving and 3D reconstruction. The goal of this work is to obtain the semantic segmentation of indoor point clouds using deep learning techniques. While semantic segmentation with deep learning of 3D point clouds still faces several challenges. In this work, instead of directly using deep learning on 3D point clouds, we first extract the panoramic images from 3D point clouds. These panoramic images are then used as input to a CNN model for semantic segmentation. Finally, we map the obtained segmentation panoramas to the 3D point cloud and develop a viewer interface to visualize the point cloud and its corresponding panoramic images. Therefore, the segmented point cloud can be analyzed in the interface visually.

We develop the first top-down method to estimate the greenness of financial portfolios, in terms of alignment to the EU Taxonomy for sustainable activities. We also develop a method to estimate, at the same time, the portfolio exposure to climate transition risk. We provide sector-level, standardized and transparent coefficients for both estimates, based on definitions of greenness and transition risk that are applicable across countries. We analyse the portfolios of Euro Area investors in 2022, based on the confidential Securities Holdings Statistics of the European Central Bank. We find that, overall, the greenness of Euro Area investors’ portfolios is lower than their exposure to transition risk (2.8% vs. 11.7%). Across financial institutions, we estimate greenness and exposure to transition risk, respectively, at 3.2% and 12% for investment funds, at 0.8% and 5% for banks and at 4.8% and 15.1% for insurers. Our analysis also shows that investors with large amounts invested in green activities can have at the same time large exposures to transition risk.