Contributions in Merlin

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.

We draw on mobile application data from 74 countries to document the effects of the COVID-19 pandemic on the adoption of digital finance and fintech. We estimate that the spread of COVID-19 and related government lockdowns have led to between a 24 and 32 percent increase in the relative rate of daily downloads of finance mobile applications in the sample countries. In absolute terms, this equates to an average daily increase of roughly 5.2 to 6.3 million application downloads and an aggregate increase of about 316 million app downloads since the pandemic’s outbreak to the present, taking into account prior trends. Most regions across the world exhibit notable increases in absolute, relative, and per capita terms. Preliminary analysis of country-level characteristics suggest that market size and demographics, rather than level of economic development and ex-ante adoption rates, drive differential trends.

Firms with credit-default swaps (CDS) traded on their debt may face "empty creditors'' as hedged creditors have less incentive to participate in firm restructuring. We test for the existence of empty creditors by employing an exogenous change to the bankruptcy code in Germany, that effectively removes their potential impact on CDS firms. Using a unique dataset on bank-firm CDS net notional and credit exposures we find that the probability of default for firms with CDS traded on them drops when the effect of empty creditors is removed. This effect increases in the average CDS hedge position of a firm's creditors and in the concentration of the firm's debt. Further, we find that firms with longer credit relationships, with higher average collateral ratios of their debt, and financially safer firms are less affected by empty creditors. Banks that are not capital constrained, and that are liquidity constrained recognise the empty creditor effect to a larger extent. Furthermore, banks' business models affect the degree to which they recognise the empty creditor effect. Where banks that monitor their creditors less and that earn a smaller portion of their income from interest activities, recognise the empty creditor effect to a larger extent.

The present article provides a novel theoretical way to evaluate tradeability in markets of ordinary exponential Lévy type. We consider non-tradeability as a particular type of market illiquidity and investigate its impact on the price of the assets. Starting from an adaption of the continuous-time optional asset replacement problem initiated by McDonald and Siegel (1986), we derive tradeability premiums and subsequently characterize them in terms of free-boundary problems. This provides a simple way to compute non-tradeability values, e.g. by means of standard numerical techniques, and, in particular, to express the price of a non-tradeable asset as a percentage of the price of a tradeable equivalent. Our approach is illustrated via numerical examples where we discuss various properties of the tradeability premiums.

The present article studies geometric step options in exponential Lévy markets. Our contribution is manifold and extends several aspects of the geometric step option pricing literature. First, we provide symmetry and parity relations and derive various characterizations for both European-type and American-type geometric double barrier step options. In particular, we are able to obtain a jump-diffusion disentanglement for the early exercise premium of American-type geometric double barrier step contracts and its maturity-randomized equivalent as well as to characterize the diffusion and jump contributions to these early exercise premiums separately by means of partial integro-differential equations and ordinary integro-differential equations. As an application of our characterizations, we derive semi-analytical pricing results for (regular) European-type and American-type geometric down-and-out step call options under hyper-exponential jump-diffusion models. Lastly, we use the latter results to discuss the early exercise structure of geometric step options once jumps are added and to subsequently provide an analysis of the impact of jumps on the price and hedging parameters of (European-type and American-type) geometric step contracts.