| By: |
Bernardi, Mauro;
Costola, Michele |
| Abstract: |
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). |
| Keywords: |
VAR estimation,Financial Networks,Bayesian inference,Sparsity,Spike-and-Slab prior,Stochastic Search Variable Selection,Expectation-Maximisation |
| Date: |
2019 |
| URL: |
http://d.repec.org/n?u=RePEc:zbw:safewp:244&r=all |