Systemic Financial Sector and Sovereign Risks
Xisong Jin () and
Francisco Nadal De Simone ()
No 109, BCL working papers from Central Bank of Luxembourg
This study takes a comprehensive approach to systemic risk stemming from Luxembourg’s Other Systemically Important Institutions (OSIIs), from the Global Systemically Important Banks (G-SIBs) to which they belong, from the investment funds sponsored by the OSIIs, from the housing market, from the non-financial corporate sector and from the sovereign. All sectoral balance sheets are integrated and the resulting systemic contingent claims are linked into a stochastic version of the general government balance sheet to gauge their impact on sovereign risk. Explicitly modelling default dependence and capturing the time-varying non-linearities and feedback effects typical of financial markets, the approach evaluates systemic losses and potential public sector costs from contingent liabilities stemming directly or indirectly from the financial sector. Various vulnerability and risk indicators suggest the sovereign is robust to a variety of shocks. The analysis highlights the key role of a sustainable fiscal position for financial stability.
Keywords: financial stability; sovereign risk; macro-prudential policy; banking sector; investment funds; default probability; non-linearities; generalized dynamic factor model; dynamic copulas (search for similar items in EconPapers)
JEL-codes: C1 E5 F3 G1 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-eec, nep-mac and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:bcl:bclwop:bclwp109
Access Statistics for this paper
More papers in BCL working papers from Central Bank of Luxembourg
Series data maintained by ().