Fire sales are among the major drivers of market instability in modern financial systems. Due to iterated distressed selling and the associated price impact, initial shocks to some institutions can be amplified dramatically through the network induced by portfolio overlaps. In this paper we develop models that allow us to investigate central characteristics that drive or hinder the propagation of distress. We investigate single systems as well as ensembles of systems that are alike, where similarity is measured in terms of the empirical distribution of all defining properties of a system. This asymptotic approach ensures a great deal of robustness to statistical uncertainty and temporal fluctuations, and we give various applications. A natural characterization of systems resilient to fire sales emerges, and we provide explicit criteria that regulators may exploit in order to assess the stability of any system. Moreover, we propose risk management guidelines in form of minimal capital requirements, and we investigate the effect of portfolio diversification and portfolio overlap. We test our results by Monte Carlo simulations for exemplary configurations.

Bio: Nils Detering is a tenure track assistant professor in Financial Mathematics and Probability at the University of California, Santa Barbara (UCSB). Prior to joining UCSB Nils was a post-doctoral researcher at the University of Munich (Germany). During his post-doctoral period he was also a visiting fellow at the Newton institute in Cambridge (UK) and at the Institute for Pure and Applied Mathematics at UCLA. Nils holds a PhD in Quantitative Finance from Frankfurt School of Finance and Management and a Masters degree in pure mathematics from the University of Goettingen (Germany). Nils has several years of experience in the financial industry as a trader, structurer and financial engineer.

Nils research is mainly on asymptotic methods (large number of market participants/financial institutions) to analyse systemic risk in the financial system, often facilitating random graphs. Additional research interests are in infinite dimensional stochastic analysis and its applications to the modelling electricity markets.