Inference for Large Financial Systems

56 Pages Posted: 4 Aug 2017 Last revised: 9 Jan 2019

See all articles by Kay Giesecke

Kay Giesecke

Stanford University - Management Science & Engineering

Gustavo Schwenkler

Boston University - Questrom School of Business

Justin Sirignano

Imperial College London - Department of Mathematics; University of Illinois at Urbana-Champaign

Date Written: May 30, 2018

Abstract

We treat the parameter estimation problem for mean-field models of large interacting financial systems such as the banking system and a pool of assets held by an institution or backing a security. We develop an asymptotic inference approach that addresses the scale and complexity of such systems. Harnessing the weak convergence results developed for mean-field financial systems in the literature, we construct an approximate likelihood for large systems. The approximate likelihood has a conditionally Gaussian structure, enabling us to design an efficient numerical method for its evaluation. We provide a representation of the corresponding approximate estimator in terms of a weighted least-squares estimator, and use it to analyze the large-system and large-sample behavior of the estimator. Numerical results for a mean-field model of systemic financial risk highlight the efficiency and accuracy of our estimator.

Keywords: Interacting stochastic systems, likelihood inference, weak limits, large system asymptotics, indirect inference

JEL Classification: C13, C20, C58

Suggested Citation

Giesecke, Kay and Schwenkler, Gustavo and Sirignano, Justin, Inference for Large Financial Systems (May 30, 2018). Boston University Questrom School of Business Research Paper. Available at SSRN: https://ssrn.com/abstract=3012751 or http://dx.doi.org/10.2139/ssrn.3012751

Kay Giesecke

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States
(650) 723 9265 (Phone)
(650) 723 1614 (Fax)

HOME PAGE: http://www.stanford.edu/~giesecke/

Gustavo Schwenkler (Contact Author)

Boston University - Questrom School of Business ( email )

595 Commonwealth Ave
Boston, MA 02466
United States

HOME PAGE: http://www.gustavo-schwenkler.com

Justin Sirignano

Imperial College London - Department of Mathematics ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

HOME PAGE: http://jasirign.github.io

University of Illinois at Urbana-Champaign ( email )

601 E John St
Champaign, IL 61820
United States

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