Inference for large financial systems

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. Ha...

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Bibliographic Details
Published inMathematical finance Vol. 30; no. 1; pp. 3 - 46
Main Authors Giesecke, Kay, Schwenkler, Gustavo, Sirignano, Justin A.
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.01.2020
Subjects
Banking system
computational efficiency
Convergence
Financial systems
Inference
large interacting stochastic systems
likelihood inference
Mathematical models
Numerical methods
Parameter estimation
statistical efficiency
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Summary: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.
ISSN:0960-1627
1467-9965
DOI:10.1111/mafi.12222