Contributions in Merlin

Repo rates frequently exceed unsecured rates in practice. As an explanation, this paper derives a constrained-arbitrage relation between the unsecured rate, the repo rate, and the illiquidity adjusted expected rate of return of the underlying collateral. The theory is based on unsecured borrowing constraints in the market for liquidity. Repos and security cash-market trades are alternative means to get liquidity. Collateral spreads (unsecured less repo rate) can turn negative if borrowing constraints tighten, unsecured rates spike down, or from a depressed and illiquid security market. The constrained-arbitrage theory sheds light on the evolution of collateral spreads over time.

We study how the CEO's power over the board of directors affects pay levels and the structure of optimal compensation contracts and derive unexpected results. First, a more powerful CEO generally receives more pay and a contract with a higher pay-performance sensitivity (PPS) if firm performance is low. In contrast, if firm performance is high, more CEO power translates into less pay and a lower PPS. Second, considering a special case of our general model, we show that more powerful CEOs receive higher salaries, more stocks but a nonincreasing number of options. Third, we find that the presence of a powerful CEO generally leaves the optimal use of relative performance evaluation unaffected. However, we identify conditions under which the sensitivity of CEO pay to peer performance can be increasing in the CEO's power over the board. Overall, our results suggest that frequently used indicators of poor (or sound) compensation practices should be interpreted with care.

In this paper, we investigate whether companies located in liberal market economies report more explicitly about specific CSR topics than firms located in coordinated market economies. For a sample of 3,384 CSR reports of European and US firms, we perform a textual analysis over the period 2008 – 2016. Our results confirm that companies located in LMEs report more explicitly about education and philanthropy; for parental leave and climate, we find a negative and significant association. We also find that CSR performance, size and report length are positively associated with the explicitness of our topic-specific CSR disclosure scores. Our results are robust to a variety of alternative measures.

This paper analyses stock market reactions after the occurrence of major negative corporate social responsibility (CSR) events and the possibility of mitigating these effects through the upfront provision of CSR information in firms’ annual reports. For this purpose, we follow a three-step procedure. First, we analyse the major concerns gathered from REPRisk® data via event study analysis. Herein, we cover a window of 5 to 20 days. Second, we analyse all annual reports of the firms mentioned in the covered period over the entire time horizon and conduct a textual analysis to examine firms’ disclosure of CSR information. Finally, we draw conclusions from the two approaches and show that firms with more upfront CSR information suffer from stronger negative market reactions after the occurrence of a negative CSR event. Herein, we show that if the occurrence of a negative CSR event conflicts with investors’ expectations, then it leads to an important update of investors’ beliefs about firms’ prospects. Our results also confirm that such an event leads to an adjustment of the subsequent year’s CSR disclosure in the annual reports.

In this paper, we test the realization effect, i.e., that risk-taking increases after a paper loss, whereas risk-taking decreases after a realized loss, using gambling data from a real casino. During a particular casino visit, losses are likely perceived as paper losses because the chance to offset prior losses remains effective until leaving the casino. However, when casino customers leave the casino, the final account balance is realized. Using individual-level slot machine gambling records, we find that risk-taking after paper losses increases during a visit and that this effect is more pronounced for larger losses. Conversely, risk-taking across multiple visits is not altered if the realized losses are comparatively small, whereas risk-taking is reduced if realized losses are comparatively large.

We analyze the impact of quantitative easing by the Federal Reserve, European Central Bank and Bank of England on cross‐border credit flows. Relying on comprehensive loan‐level data, we find that Fed QE strongly boosts cross‐border credit granted to Turkish banks by banks located in the US, Euro Area and UK, while ECB and BoE QEs work only moderately through banks in the EA and UK, respectively. In general QE works at short maturities across bank locations and loan currencies, more strongly for weaker lenders and borrowers, and may have resulted in maturity mismatches in Turkish banks searching for yield.

This paper studies the effects of a supply shock to the liquidity of credit default swap (CDS) markets on bond spreads. Using as a laboratory the universe of CDS transactions done by German banks, our model is identified by changes in CDS market liquidity due to the exit of a large dealer. We find that the CDS market converges to a new equilibrium, where traded volumes are lower and bid-ask spreads are higher. Bond yields increase in response, with stronger effects for the non-investment-grade class. Individual portfolio data indicate that the effect is partly driven by investors decreasing their holdings of both CDS and related bonds. We, therefore, show that derivative markets can affect demand in underlying securities and, subsequently, the issuers’ cost of capital.

Antibiotic resistance constitutes a major health threat. Predicting bacterial causes of infections is key to reducing antibiotic misuse, a leading cause of antibiotic resistance. We combine administrative and microbiological laboratory data from Denmark to train a machine learning algorithm predicting bacterial causes of urinary tract infections. Based on predictions, we develop policies to improve prescribing in primary care, highlighting the relevance of physician expertise and time-variant patient distributions for policy implementation. The proposed policies delay prescriptions for some patients until test results are known and give them instantly to others. We find that machine learning can reduce antibiotic use by 7.42 percent without reducing the number of treated bacterial infections. As Denmark is one of the most conservative countries in terms of antibiotic use, targeting a 30 percent reduction in prescribing by 2020, this result is likely to be a lower bound of what can be achieved elsewhere.

This paper studies the real effects of a change in the dealer composition in OTC markets. Using as a laboratory the universe of CDS transactions entered into by German banks and the exit of a large dealer in November 2014 as a shock, we first show that the CDS market converges to a new equilibrium, with lower traded volumes and higher bid-ask spreads. Using individual portfolios we find that in response investors rebalance both their CDS holdings and bond portfolios. In particular, CDS protection buyers decrease their holdings of the bonds underlying CDS contracts. The effects are strongest for non-investment grade bonds. We therefore show that the exit of large dealers from OTC markets could affect investor demand in related securities and, in consequence, the issuers cost of capital.

Importance of distributional channels in macroeconomic dynamics has been the object of considerable attention in empirical studies. Despite significant amount of effort aimed at incorporating heterogeneity into macroeconomics, however, their explicit inclusion in the standard policy toolbox is far from widespread. A relevant obstacle, in such cases, is the computation of equilibria. I propose a global solution method for the computation of infinite-horizon, heterogeneous agent macroeconomic models with aggregate uncertainty. Details of the algorithm are illustrated by presenting its application to a an example model: in it, aggregate dynamics depends explicitly on firm entry and exit, and individual choices are often constrained by a form of market incompleteness. Existing computational strategies are either unfeasible or provide inaccurate solutions. Moreover, global solutions are computationally expensive because the minimal representation of the aggregate state space - and thus the aggregate law of motion - faces the curse of dimensionality. The proposed strategy thus combines adaptive sparse grids with a cross-sectional density approximation, and introduces a framework for solving the more general class of dynamic models with firm or household heterogeneity accurately.