We propose a long-run risk model with stochastic volatility, a time-varying mean reversion level of volatility, and jumps in the state variables. The special feature of our model is that the jump intensity is not affine in the conditional variance but driven by a separate process. We show that...

Private equity fund managers are typically required to invest their own money alongside the fund. We examine how this coinvestment affects the acquisition strategy of leveraged buyout funds. In a simple model, where the investment and capital structure decisions are made simultaneously, we show...

We examine the incentive effects of private equity (PE) professionals' ownership in the funds they manage. In a simple model, we show that managers select less risky firms and use more debt financing the higher their ownership. We test these predictions for a sample of PE funds in Norway, where...

We study self- and cross-excitation of shocks in the Eurozone sovereign CDS market. We adopt a multivariate setting with credit default intensities driven by mutually exciting jump processes, to capture the salient features observed in the data, in particular, the clustering of high default...

A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in...

We consider the continuous-time portfolio optimization problem of an investor with constant relative risk aversion who maximizes expected utility of terminal wealth. The risky asset follows a jump-diffusion model with a diffusion state variable. We propose an approximation method that replaces...

This paper addresses whether and to what extent econometric methods used in experimental studies can be adapted and applied to financial data to detect the best-fitting preference model. To address the research question, we implement a frequently used nonlinear probit model in the style of Hey...

In more and more situations, artificially intelligent algorithms have to model humans' (social) preferences on whose behalf they increasingly make decisions. They can learn these preferences through the repeated observation of human behavior in social encounters. In such a context, do...

With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for the moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the...

We examine how a firms' investment behavior affects the investment of a neighboring firm. Economic theory yields ambiguous predictions regarding the direction of firm peer effects and consistent with earlier work, we find that firms display similar investment behavior within an area using OLS...