We propose a method to extract significant risk interactions between Countries adopting the Graphical Lasso algorithm, used in graph theory to sort out the spurious effect of common components. In this context, the major issue is the definition of the penalization parameter. We propose a search algorithm aimed at the best separation of the variables (expressed in terms of conditional dependence) given an a priori desired partition. The case study focuses on Sovereign Bond Yields over the period 2009–2017. The proposed algorithm is used in systemic risk estimation of the Euro area sovereigns.
Arbia, G., Bramante, R., Facchinetti, S., Zappa, D., Sovereign co-risk measures in the Euro Area, in Book of Short Papers SIS 2018, (Palermo, 20-22 June 2018), Pearson Italia, Palermo 2018: 1429-1434 [http://hdl.handle.net/10807/128690]
Sovereign co-risk measures in the Euro Area
Arbia, Giuseppe;Bramante, Riccardo;Facchinetti, Silvia;Zappa, Diego
2018
Abstract
We propose a method to extract significant risk interactions between Countries adopting the Graphical Lasso algorithm, used in graph theory to sort out the spurious effect of common components. In this context, the major issue is the definition of the penalization parameter. We propose a search algorithm aimed at the best separation of the variables (expressed in terms of conditional dependence) given an a priori desired partition. The case study focuses on Sovereign Bond Yields over the period 2009–2017. The proposed algorithm is used in systemic risk estimation of the Euro area sovereigns.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.