High-dimensional sparse financial networks through a regularised regression model

  • We propose a shrinkage and selection methodology specifically designed for network inference using high dimensional data through a regularised linear regression model with Spike-and-Slab prior on the parameters. The approach extends the case where the error terms are heteroscedastic, by adding an ARCH-type equation through an approximate Expectation-Maximisation algorithm. The proposed model accounts for two sets of covariates. The first set contains predetermined variables which are not penalised in the model (i.e., the autoregressive component and common factors) while the second set of variables contains all the (lagged) financial institutions in the system, included with a given probability. The financial linkages are expressed in terms of inclusion probabilities resulting in a weighted directed network where the adjacency matrix is built “row by row". In the empirical application, we estimate the network over time using a rolling window approach on 1248 world financial firms (banks, insurances, brokers and other financial services) both active and dead from 29 December 2000 to 6 October 2017 at a weekly frequency. Findings show that over time the shape of the out degree distribution exhibits the typical behavior of financial stress indicators and represents a significant predictor of market returns at the first lag (one week) and the fourth lag (one month).

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Author:Mauro Bernardi, Michele CostolaORCiD
URN:urn:nbn:de:hebis:30:3-492349
URL:https://ssrn.com/abstract=3342240
Parent Title (English):SAFE working paper series ; No. 244
Series (Serial Number):SAFE working paper series (244)
Publisher:SAFE
Place of publication:Frankfurt am Main
Document Type:Working Paper
Language:English
Year of Completion:2019
Year of first Publication:2019
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2019/02/28
Tag:Bayesian inference; Expectation–Maximisation; Financial Networks; Sparsity; Spike–and–Slab prior; Stochastic Search Variable Selection; VAR estimation
Issue:February 12, 2019
Page Number:51
HeBIS-PPN:447081160
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / House of Finance (HoF)
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht