This study presents a dynamic model for the private equity market in which information revelation and uncertainty rationally explain the cyclical pattern of investment flows into private equity. The net benefit of private equity over public equity is i) uncertain and ii) agents have private...

We study the role of private equity firms in cross-border mergers and acquisitions. We find that private equity-owned firms are more likely to become targets in crossborderM&A transactions. This effect is particularly strong in transactions where the target or its shareholders actively reach out...

We study the effects of a bank’s engagement in trading. Traditional banking is relationship-based: not scalable, long-term oriented, with high implicit capital, and low risk (thanks to the law of large numbers). Trading is transactions-based: scalable, short-term, capital constrained, and with...

I study a model of market-liquidity provision by levered intermediaries that, besides operating trading desks, run deposit-taking franchises. Levered intermediaries’ heightened incentive to absorb risk helps to counteract liquidity-provision frictions that, in an unlevered economy, would lead...

This discussion paper resulted in a publication in <Stochastic Models</I> (2012). Volume 28(3), pages 478-502.<P> We apply the splitting method to three well-known counting problems, namely 3-SAT, random graphs with prescribed degrees, and binary contingency tables. We present an enhanced version of the splitting method...</p></stochastic>

We compare three stochastic user equilibrium traffic assignment models multinomial probit, nested logit, and generalized nested logit), using a congestible transport network. We test the models in two situations: one in which they have theoretically equivalent coefficients, and one in which they...

In this paper we study a challenging call center operation problem. The goal of our analysis is to identify an optimal policy for allocating tasks to agents. As a first step, we discuss promising randomized policies and use stochastic approximation for finding the optimal randomized policy when...

Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels,...

This discussion paper resulted in a publication in 'Quantitative Finance', 2010, 10, 177-194.<P> When using an Euler discretisation to simulate a mean-reverting square root process, one runs into the problem that while the process itself is guaranteed to be nonnegative, the discretisation is not....</p>

In a Bayesian analysis, different models can be compared on the basis of theexpected or marginal likelihood they attain. Many methods have been devised to compute themarginal likelihood, but simplicity is not the strongest point of most methods. At the sametime, the precision of methods is often...