Research
Selected AC3R Research Articles
21st-century risks
Analyzing new threats - from financial contagion to climate crises to pandemics.
Social-economic resilience
Understanding how systems recover and adapt.
Regulation and policy
Designing legal structures to make risks manageable.
Achieving safety: Personal, private, and public provision
Enrico C. Perotti and Spyros Terovitis
We study how a primary need for minimum safety affects investment choices. In addition to risky projects, agents may choose to invest in personal assets they can control. Investing in personal assets serves as self-insurance, as they ensure a higher minimum return but offer a lower expected return than the risky project offers. In autarky, investors ensure a minimum return by personal assets, besides investing in the risky project. Private intermediaries and a safe rate arise endogenously to limit inefficient self-insurance, with self-insured investors holding bank equity to safeguard private safe debt. The endogenous conflict over interim risk choices is resolved by demandable debt, forcing early liquidation in states in which the ability of banks to repay debt holders remains uncertain. Our work highlights the unintended consequences of public provision of safety for private provision of safety and aggregate investment, demonstrating that these effects depend critically on whether public provision takes the form of public debt or deposit insurance.
Estimating option pricing models using a characteristic function-based linear state space representation
H. Peter Boswijk, Roger J. A. Laeven and Evgenii Vladimirov
We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jump-diffusions. Our procedure is based on the comparison between an option-implied, model-free representation of the conditional log-characteristic function and the model-implied conditional log-characteristic function, which is functionally affine in the model’s state vector. We formally derive an associated linear state space representation and the asymptotic properties of the corresponding measurement errors. The state space representation allows us to use a suitably modified Kalman filtering technique to learn about the latent state vector and a quasi-maximum likelihood estimator of the model parameters, for which we establish asymptotic inference results. Accordingly, the filtering and estimation procedure brings important computational advantages. We analyze the finite-sample behavior of our procedure in Monte Carlo simulations. The applicability of our procedure is illustrated in two case studies that analyze S&P 500 option prices and the impact of exogenous state variables capturing Covid-19 reproduction and economic policy uncertainty.
Localizing strictly proper scoring rules
Ramon F. A. de Punder, Cees G. H. Diks, Roger J. A. Laeven and Dick J. C. van Dijk
When comparing predictive distributions, forecasters are typically not equally interested in all regions of the outcome space. To address the demand for focused forecast evaluation, we propose a procedure to transform strictly proper scoring rules into their localized counterparts while preserving the score divergence and strict propriety. This is accomplished by applying the original scoring rule to a censored distribution. Our procedure nests the censored likelihood score as a special case. Among a multitude of others, it also implies a class of censored kernel scores that offers a (possibly multivariate) alternative to the threshold weighted Continuously Ranked Probability Score (twCRPS), extending its local propriety to more general weight functions than single tail indicators. Within this localized framework, we obtain a generalization of the Neyman Pearson lemma, establishing the censored likelihood ratio test as uniformly most powerful. For other tests of localized equal predictive performance, results of Monte Carlo simulations and empirical applications to risk management, inflation and climate data consistently emphasize the excellent power properties of censoring versus other localization methods.
Dynamic return and star-shaped risk measures via BSDEs
Roger J. A. Laeven, Emanuela Rosazza Gianin and Marco Zullino
This paper establishes characterization results for dynamic return and star-shaped risk measures
induced via backward stochastic differential equations (BSDEs). We first characterize a general family of static star-shaped functionals in a locally convex Fréchet lattice. Next, employing the Pasch-Hausdorff envelope, we build a suitable family of convex drivers of BSDEs inducing a corresponding family of dynamic convex risk measures of which the dynamic return and star-shaped risk measures emerge as the essential minimum. Furthermore, we prove that if the set of star-shaped supersolutions of a BSDE is not empty, then there exists, for each terminal condition, at least one convex BSDE with a non-empty set of supersolutions, yielding the minimal star-shaped supersolution. We illustrate our theoretical results in a few examples.
Compound multivariate Hawkes processes: Large deviations and rare event simulation
Raviar S. Karim, Roger J. A. Laeven and Michel Mandjes
In this paper, we establish a large deviations principle for a multivariate compound process induced by a multivariate Hawkes process with random marks. Our proof hinges on showing essential smoothness of the limiting cumulant of the multivariate compound process, resolving the inherent complication that this cumulant is implicitly characterized through a fixed-point representation. We employ the large deviations principle to derive logarithmic asymptotic results on the marginal ruin probabilities of the associated multivariate risk process. We also show how to conduct rare event simulation in this multivariate setting using importance sampling and prove the asymptotic efficiency of our importance sampling based estimators.
Robust multiple stopping—A duality approach
Roger J. A. Laeven, John G. M. Schoenmakers, Nikolaus Schweizer and Mitja Stadje
We develop a method to solve, theoretically and numerically, general optimal stopping problems. Our general setting allows for multiple exercise rights—that is, optimal multiple stopping—for a robust evaluation that accounts for model uncertainty with a dominated family of priors and for general reward processes driven by multidimensional jump-diffusions. Our approach relies on first establishing robust martingale dual representation results for the multiple stopping problem that satisfy appealing almost sure pathwise optimality properties. Next, we exploit these theoretical results to develop upper and lower bounds that, as we formally show, not only converge to the true solution asymptotically, but also constitute genuine prelimiting upper and lower bounds. We illustrate the applicability of our approach in a few examples and analyze the impact of model uncertainty on optimal multiple stopping strategies.