Contributions in Merlin

We study a continuous-time contracting problem in which size plays a role. The agent may take on excessive risk to enhance short-term gains; doing so exposes the principal to large, infrequent losses. The optimal contract includes size as an instrument: downsizing along the equilibrium path may be necessary so as to preserve incentive compatibility. We characterize the principal's value function and the downsizing process, both of which depend on the nature of the liquidation value. When the latter has fixed and size-dependent components, there is an optimal (endogenous) liquidation size. In the special case where the liquidation value is linear in size, one may describe the solution in size-adjusted terms, which allows for the study of re-investment. The optimal contract is implemented using the full array of financial securities plus debt covenants; holding equity is essential to curb risk taking. Conflicts emerge between classes of security holders and explain phenomena like seniority of claims. Firms for which risk taking is less attractive can afford a higher leverage.

We develop a simple general equilibrium model to study the interactions between financial arbitrage and the real economy under collateral constraints. In good times, arbitrage activities help boost the production sectors by providing external funds to capital investment. However, when exposed to adverse shocks and panic market reactions, arbitrage also amplifies the financial distress and makes it easier for the economy to fall into self-fulfilling crises. Moreover, the possibility of regime switches triggered by exogenous shocks also complicates the path to recovery. The combination of financial distress and pessimistic market anticipation not only slows down the recovery process, but also can trap the economy in a less healthy steady state.

In this paper, we show both theoretically and empirically that the size of over-the-counter (OTC) markets can be reduced without affecting individual net positions. First, we find that the networked nature of these markets generates an excess of notional obligations between the aggregate gross amount and the minimum amount required to satisfy each individual net position. Second, we show conditions under which such excess can be removed. We refer to this netting operation as compression and identify feasibility and effciency criteria, highlighting intermediation as the key element for excess levels. We show that a trade-off exists between the amount of notional that can be eliminated from the system and the conservation of original trading relationships. Third, we apply our framework to a unique and comprehensive transaction-level dataset on OTC derivatives including all firms based in the European Union. On average, we find that around 75% of market gross notional relates to excess. While around 50% can in general be removed via bilateral compression, more sophisticated multilateral compression approaches are substantially more effcient. In particular, we find that even the most conservative multilateral approach which satisfies relationship constraints can eliminate up to 98% of excess in the markets.

We develop a novel dynamic model of banking showing that aggregate bank capital is an important determinant of bank lending. In our model commercial banks finance their loans with deposits and equity, while facing equity issuance costs. Because of this financial friction, banks build equity buffers to absorb negative shocks. Aggregate bank capital determines the dynamics of lending. Notably, the equilibrium loan rate is a decreasing function of aggregate capitalization. The competitive equilibrium is constrained inefficient, because banks do not internalize the consequences of individual lending decisions for the future loss-absorbing capacity of the banking sector. In particular, we find that unregulated banks lend too much. Imposing a minimum capital ratio helps tame excessive lending, which enhances stability of the banking system.

This paper develops a method to efficiently estimate hidden Markov models with continuous latent variables using maximum likelihood estimation. To evaluate the (marginal) likelihood function, I decompose the integral over the unobserved state variables into a series of lower dimensional integrals, and recursively approximate them using numerical quadrature and interpolation. I show that this procedure has very favorable numerical properties: First, the computational complexity grows linearly in time, which makes the integration over hundreds and thousands of periods well feasible. Second, I prove that the numerical error is accumulated sub-linearly over time; consequently, using highly efficient and fast converging numerical quadrature and interpolation methods for low and medium dimensions, such as Gaussian quadrature and Chebyshev polynomials, the numerical error can be well controlled even for very large numbers of periods. Lastly, I show that the numerical convergence rates of the quadrature and interpolation methods are preserved up to a factor of at least 0.5 under appropriate assumptions. I apply this method to the bus engine replacement model of Rust: first, I verify the algorithm’s ability to recover the parameters in an extensive Monte Carlo study with simulated datasets; second, I estimate the model using the original dataset.

The representative-agent approach is widely used in consumption-based asset pricing. From a theoretical point of view, quite restrictive assumptions on the underlying economy are needed for asset prices to depend only on aggregate consumption. A heterogeneous-agent financial market model is presented with all investors following simple rebalancing rules where aggregation already fails. A meaningful specification of a representative agent is still possible: Instead of asset prices per se the representative agent indicates the direction in which relative asset prices tend to in expectation from one time period to the next. The objective function of the representative agent is independent of the set of rebalancing rules participating in the market.

The transition from compulsory schooling to upper-secondary education is a crucial and frequently difficult step in the educational career of young people. In this study, we analyze the impact of one non-cognitive skill, locus of control, on the intention and the decision to delay the transition into post-compulsory education in Switzerland. We find that locus of control, measured at ages 13–14, has a significant impact on the intention to delay the transition into upper-secondary education. Furthermore, we find that the intention to delay the transition is strongly correlated with the actual delay, measured one and a half years after the intention. Finally, students with the initial intention to delay but successfully continuing into upper-secondary education show a stronger internal locus of control than comparable students who do delay their transition.

How, and to what extent, does an interconnected financial system endogenously amplify external shocks? This paper attempts to reconcile some apparently different views emerged after the 2008 crisis regarding the nature and the relevance of contagion in financial networks. We develop a common framework encompassing several network contagion models and show that, regardless of the shock distribution and the network topology, precise ordering relationships on the level of aggregate systemic losses hold among models. We argue that the extent of contagion crucially depends on the amount of information that each model assumes to be available to agents. Under no uncertainty about the network structure and values of external assets, the well-known Eisenberg and Noe (2001) model applies, which delivers the lowest level of contagion. This is due to a property of loss conservation: aggregate losses after contagion are equal to the losses incurred by those institutions initially hit by a shock. This property implies that many contagion analyses rule out by construction any loss amplification, treating de facto an interconnected system as a single aggregate entity, where losses are simply mutualised. Under higher levels of uncertainty, as captured for instance by the DebtRank model, losses become non-conservative and get compounded through the network. This has important policy implications: by reducing the levels of uncertainty in times of distress (e.g. by obtaining specific data on the network) policymakers would be able to move towards more conservative scenarios. Empirically, we compare the magnitude of contagion across models on a sample of the largest European banks during the years 2006- 2016. In particular, we analyse contagion effects as a function of the size of the shock and the type of external assets shocked.

This paper analyzes brief episodes of high-intensity quotes turnover and revision-"bursts" in quotes-in the U.S. equity market. Such events occur very frequently, around 400 times a day for actively traded stocks. We find significant price impact associated to this market-maker initiated event, about five times higher than during non-burst periods. Bursts in quotes are concurrent with short-lived structural break in the informational relationship between market makers and market takers. During bursts, market makers no longer passively impound information from order flow into quotes-a departure from traditional market microstructure paradigm. Rather, market makers significantly impact prices during bursts in quotes. Further analysis shows that there is asymmetry in adverse selection between the bid and ask sides of the limit order book and only a sub-population of market makers enjoy an informational advantage during bursts. Our results call attention to the need for a new microstructure perspective in understanding modern high-frequency limit order book markets.

We introduce a network valuation model (hereafter NEVA) for the ex-ante valuation of claims among financial institutions connected in a network of liabilities. Similar to previous work, the new framework allows to endogenously determine the recovery rate on all claims upon the default of some institutions. In addition, it also allows to account for ex-ante uncertainty on the asset values, in particular the one arising when the valuation is carried out at some time before the maturity of the claims. The framework encompasses as special cases both the ex-post approaches of Eisenberg and Noe and its previous extensions, as well as the ex-ante approaches, in the sense that each of these models can be recovered exactly for special values of the parameters. We characterize the existence and uniqueness of the solutions of the valuation problem under general conditions on how the value of each claim depends on the equity of the counterparty. Further, we define an algorithm to carry out the network valuation and we provide sufficient conditions for convergence to the maximal solution.