Contributions in Merlin

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.

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In this paper, we give a double twist to the problem of planning under uncertainty. State-of-the-art planners seek to minimize the localization uncertainty by only considering the geometric structure of the scene. In this paper, we argue that motion planning for vision-controlled robots should be perception aware in that the robot should also favor texture-rich areas to minimize the localization uncertainty during a goal-reaching task. Thus, we describe how to optimally incorporate the photometric information (i.e., texture) of the scene, in addition to the the geometric one, to compute the uncertainty of vision-based localization during path planning. To avoid the caveats of feature-based localization systems (i.e., dependence on feature type and user-defined thresholds), we use dense, direct methods. This allows us to compute the localization uncertainty directly from the intensity values of every pixel in the image. We also describe how to compute trajectories online, considering also scenarios with no prior knowledge about the map. The proposed framework is general and can easily be adapted to different robotic platforms and scenarios. The effectiveness of our approach is demonstrated with extensive experiments in both simulated and real-world environments using a vision-controlled micro aerial vehicle.

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We analyze American put options in a hyper-exponential jump-diffusion model. Our contribution is threefold. Firstly, by following a maturity randomization approach, we solve the partial integro-differential equation and obtain a tight lower bound for the American option price. Secondly, our method allows us to disentangle the contributions of jump and diffusion for the American early exercise premium. Finally, using American-style options on S&P 100 index from 2007 until 2013, we estimate a range of hyper-exponential specifications and investigate the implications for option pricing and jump-diffusion disentanglement. We find that jump risk accounts for a large part of early exercise premium.