Tinbergen Institute

About: Tinbergen Institute is a education organization based out in Rotterdam, Netherlands. It is known for research contribution in the topics: Volatility (finance) & Competition (economics). The organization has 565 authors who have published 3157 publications receiving 82800 citations.

Observation driven mixed-measurement dynamic factor models with an application to credit risk

Abstract: We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and cross-sectional dependence due to shared dynamic latent factors. A feature of our model is that the likelihood function is known in closed form. This enables parameter estimation using standard maximum likelihood methods. We adopt the new framework for signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody's-rated firms from January 1982 to March 2010.

Reciprocity and Emotions when Reciprocators Know Each Other

Abstract: This is an experimental study of a three-player power-to-take game where a take authority is matched with two responders. The game consists of two stages. In the first stage, the take authority decides how much of the endowment of each responder that is left after the second stage will be transferred to the take authority (the so-called take rate). In the second stage, each responder can react by destroying any part of his or her own endowment. Two treatments are considered: one in which all players are 'strangers' to each other (random matching), and one in which the responders know each other from outside the lab and are more or less close 'friends' (whereas the take-authority is again randomly selected). We focus on how the intensity of ties between responders impacts the decisions, beliefs, and emotions of both the responders and the take-authority. Some of our findings are: (1) although take rates are about the same, friends destroy more than strangers when faced with high take rates; (2) coordination on the same destruction level is stronger among friends; (3) the high level of coordination among friends can be explained by their emotional reaction towards one another; (4) the difference between the actual and expected take rate is a much better predictor of experienced emotions and destruction than the difference between the actual and (what is considered as) the fair take rate.

Modelling Round-the-Clock Price Discovery for Cross-Listed Stocks Using State Space Methods

Abstract: U.S. trading in non-U.S. stocks has grown dramatically. Round-the-clock, these stocks trade in the home market, in the U.S. market and, potentially, in both markets simultaneously. We develop a general methodology based on a state space model to study 24-hour price discovery in a multiple markets setting. As opposed to the standard variance ratio approach, this model deals naturally with (i) simultaneous quotes in an overlap, (ii) missing observations in a non-overlap, (iii) noise due to transitory microstructure effects, and (iv) contemporaneous correlation in returns due to market-wide factors. We apply our model to Dutch stocks, cross-listed in the U.S. Our findings suggest a minor role for the NYSE in price discovery for Dutch shares, in spite of its non-trivial and growing market share.

Prediction of travel time variability for cost-benefit analysis

Abstract: Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.