Contributions in Merlin

We consider a group consisting of N business units. We suppose there are regulatory constraints for each unit, more precisely, the net worth of each business unit is required to belong to a set of acceptable risks, assumed to be a convex cone. Because of these requirements, there are less incentives to operate under a group structure, as creating one single business unit, or altering the liability repartition among units, may allow to reduce the required capital. We analyse the possibilities for the group to benefit from a diversification effect and economise on the cost of capital. We define and study the risk measures that allow for any group to achieve the minimal capital, as if it were a single unit, without altering the liability of business units, and despite the individual admissibility constraints. We call these risk measures cohesive risk measures, we prove cohesive risk measures are tail expectations but calculated under a different probability.

This paper addresses the question of optimal currency exposure for a risk-and-ambiguity-averse international investor. A robust mean-variance model with smooth ambiguity preferences is used to derive the optimal currency exposure. In the theoretical part, we show that the sample-efficient currency demand can be calculated as the solution to a generalized ridge regression. Through the lens of these results, we demonstrate that our ambiguity-based model offers a new explanation of the home currency bias. The investor's dislike for model uncertainty induces a disproportionately high currency hedging demand. The empirical analysis of currency overlay strategies employs the foreign exchange, equity, and bond returns over the period from 1999 to 2018. Our out-of-sample back-tests illustrate that accounting for ambiguity enhances the stability of estimated optimal currency exposures and significantly improves the portfolio performance net of transaction costs.

How does banks' geographical footprint change when a FinTech platform allows offering mortgages to regions without branch presence? Unique data on responses from different banks to each household yield three salient findings: First, banks offer 4% more often and 6 basis points cheaper when markets have high versus low concentration, implying more profitable follow-on business. Second, they offer 2% more often and 2 bps cheaper when unemployment or house price growth in the applicant's state are one standard deviation less correlated with those at home, improving portfolio diversification. Third, these offers are increasingly automated, using available hard information more efficiently.

This paper examines whether sports betting markets are semistrong-form efficient—i.e., whether new information is rapidly and completely incorporated into betting prices. We use the news of ghost matches in the top European football leagues due to the COVID-19 pandemic as the arrival of public information. Because spectators are absent in ghost games, the home field advantage is reduced, and we test whether this information is fully reflected in betting prices. Our results show that bookmakers systematically overestimate a home team’s winning probability during the first period of the ghost games, which suggests that betting markets are, at least temporally, not semistrong-form efficient. We exploit a betting strategy that yields a positive net payoff over more than one month.

We construct a derivative that depends on the SPY and VIX and, in this way, incorporates both the market risk premium and the variance risk premium. We show that the product's Sharpe ratio is higher than the SPY Sharpe ratio. If we invest $10000 into the product, the products' payoff is around $60000 at the end of 2018. In comparison, if we invest $10000 into the SPY, the SPY payoff is around $30000.

During the COVID-19 crash, U.S. stocks with higher institutional ownership performed worse. This under-performance is unrelated to revisions in earnings expectations, which suggests a disconnect between stock prices and firm fundamentals. Two mechanisms were at play: Institutions faced a sudden increase in redemptions and simultaneously attempted to de-risk their portfolios. Most types of institutional investors re-balanced portfolios toward financially strong firms, whereas hedge funds sold indiscriminately. Data from a discount brokerage (Robinhood) confirm that retail investors provided liquidity. Overall, the results suggest that when a tail risk realizes, institutional investors amplify price crashes.

The first Global Climate Strike on March 15, 2019 has represented a historical turn in climate activism. We investigate the cross-section of European stock price reactions to this event. Looking at a large sample of European firms, we find that the unanticipated success of this event caused a substantial stock price reaction on high-carbon intensity companies. These findings are likely driven by an update of investors' beliefs about the level of environmental social norms in the economy and the anticipation of future developments of climate regulation.

Many researchers seek factors that predict the cross-section of stock returns. In finance, the key is to replicate anomalies by long-short portfolios based on their factor scores, with microcaps alleviated via New York Stock Exchange (NYSE) breakpoints and value-weighted returns. In econometrics, the key is to include a covariance matrix estimator of stock returns for the (mimicking) portfolio construction. This paper marries these two strands of literature in order to test the zoo of cross-sectional anomalies by injecting size controls, basically NYSE breakpoints and value-weighted returns, into efficient sorting. Thus, we propose to use a covariance matrix estimator for ultra-high dimensions (up to 5,000) taking into account large, small and microcap stocks. We demonstrate that using a nonlinear shrinkage estimator of the covariance matrix substantially enhances the power of tests for cross-sectional anomalies: On average, ‘Student’ t-statistics more than double.

Existing shrinkage techniques struggle to model the covariance matrix of asset returns in the presence of multiple-asset classes. Therefore, we introduce a Blockbuster shrinkage estimator that clusters the covariance matrix accordingly. Besides the definition and derivation of a new asymptotically optimal linear shrinkage estimator we propose an adaptive Blockbuster algorithm that clusters the covariance matrix even if the (number of) asset classes are unknown and change over time. It displays superior all-around performance on historical data against a variety of state-of-the-art linear shrinkage competitors. Additionally, we find that for small and medium-sized investment universes the proposed estimator outperforms even recent nonlinear shrinkage techniques. Hence, this new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of asset returns. Furthermore, due to the general structure of the proposed Blockbuster shrinkage estimator the application is not restricted to financial problems.

Climate change may have a detrimental effect on a firm's financial performance. Using a forward-looking measure of climate risk exposure based on textual analysis of firms' 10-K reports, we assess whether climate risks---as disclosed to the regulator---are priced in the credit default swap (CDS) market. We construct this novel climate risk measure based on BERT, an advanced language understanding algorithm, and adapt it for our purpose. We differentiate between physical and transition risks and find that transition risk increases CDS spreads, especially after the Paris Climate Agreement of 2015. However, we do not find such an effect for physical risk.