|Researchers:||Monica Billio, Massimiliano Caporin, Lorenzo Frattarolo, Aleksey Kolokolov, Loriana Pelizzon, Zorka Simon|
|Category:||Financial Intermediation, Systemic Risk Lab|
This project was part of the team project "Systemic Risk and Network Connectivity".
Topic & Objectives
We propose a new model of European equity volatility and volatility spillovers based not only on stock returns but also on balance sheet data on financial bank connectedness. The system of connections among banks is extracted from banks’ foreign exposures provided by the BIS consolidated banking statistics from 2000. We estimated volatility and volatility spillovers using a proximity structure Baba-Engle-Kraft-Kroner (BEKK) model where the proximity matrices are derived from the banks’ foreign exposures observed quarterly. This empirical investigation allows us to shed light on the role of banks´ foreign exposures on risk spillovers.
We propose a spatial approach to model risk spillovers using financial time-varying proximities based on actual claims among entities. We show how these methods could be useful in (i) isolating influential and fragile entities and important risk channels, (ii) investigating the role of portfolio composition in risk transfers, and (iii) computing target exposures able to reduce system volatility. Our empirical application uses banks’ foreign exposures as a proxy for the euro area cross-country holdings.
- We find that Ireland, Greece, and France are playing a central role in spreading risk in the European stock markets and this spillover effect can be traced back to a physical claim channel: banks’ foreign exposures.
- Our in-sample analysis points to Ireland and Italy as the major sources of risk and Portugal and France as the recipients of most of it.
- The empirical application shows the ability of our model to give a reasonable description of European spillovers during the sovereign crisis. It uncovers the fundamental role of France and Portugal as risk receivers and the risk-spreading effectiveness of Italy and Ireland.
- We derive a covariance decomposition that allows us to understand the network-mediated contribution to variance, pointing out in particular how several network configurations can reduce the covariance.
- We document a change of relevance of exposures after regulatory interventions in the second half of 2012.
- We devise a counterfactual analysis able to obtain target exposures for risk mitigation based only on ex-ante information.
- Portugal is also a lesser source of risk while shocks coming from France appear mostly innocuous, if not beneficial, having a small risk-reducing and stabilizing effect.
- Germany transforms most risks it receives into diversification benefits while contributing negligibly as a risk spreader.
- Spain’s less-trivial role exploits network exposures for diversification benefits, while at the same time, it is the third source of network-mediated risk in the system. This middleman behavior in transferring risk from peripheral to major economies is direct with respect to France, and aided and mediated through Ireland with respect to Germany. This role is not understandable from the single-parameter significance.
|Monica Billio, Massimiliano Caporin, Lorenzo Frattarolo, Loriana Pelizzon||Networks in Risk Spillovers: A Multivariate GARCH Perspective|
Econometrics and Statistics
|2023||Financial Intermediation, Systemic Risk Lab||spatial GARCH; network; risk spillover; nancial spillover|
|225||Monica Billio, Massimiliano Caporin, Lorenzo Frattarolo, Loriana Pelizzon||Networks in Risk Spillovers: A Multivariate GARCH Perspective||2018||Financial Intermediation, Systemic Risk Lab||spatial GARCH; network; risk spillover; nancial spillover|