SYSTEMIC RISK AND FINANCIAL CRISES: MODELLING INTERCONNECTIONS IN THE FINANCIAL SYSTEM TOMÁŠ KLINGER
CONTEXT APPROACH CALIBRATION RESULTS
RECAP: CONTEXT Declining margins Race for leverage Risk buildup The interconnections between sovereigns and banks: three phases System collapse Credit crunch State aid Transfer of risk to states Weak sovereigns Crisis of the Eurozone Sovereign default Transfer of risk back to banks QUESTIONS: Zdroj: Author Úzké spojení mezi bankami a státy: tři fáze a. Is the system better off without state aid? Or would a sudden total failure cost more than a slower process of regulated deleveraging? b. Which banking systems are systemically important? Is the answer to the first hypothesis different for different initiator banking systems c. Do all types of state aid have the same effect? Which ones are more efficient than others?
CONTEXT APPROACH CALIBRATION RESULTS
APPROACH NETWORK THEORY Describes interconnected structures Network is a graph defined as G = (N,E,f): o N node set o E edge set o f mapping edges on node pairs Useful for studying impulse trnsmission (contagion, credit shocks)
APPROACH NETWORK THEORY Describes interconnected structures Network is a graph defined as G = (N,E,f): o N node set o E edge set o f mapping edges on node pairs Useful for studying impulse trnsmission (contagion, credit shocks)
APPROACH NETWORK THEORY Describes interconnected structures Network is a graph defined as G = (N,E,f): o N node set o E edge set o f mapping edges on node pairs Useful for studying impulse trnsmission (contagion, credit shocks)
APPROACH NETWORK THEORY Describes interconnected structures Network is a graph defined as G = (N,E,f): o N node set o E edge set o f mapping edges on node pairs Useful for studying impulse trnsmission (contagion, credit shocks)
APPROACH AGENT-BASED MODELLING Bottom-up přístup, zkoumající chování množství subjektů, které na sebe vzájemně působí ve virtuálním prostředí o agenti jednotlivé finanční instituce nebo státy, o data jejich rozvahy, případně další informace o vzorce chování o o o kdy banka není schopná splatit úvěr, kdy a jak spustí odprodávání určitého množství aktiv, kdy a jaké stát využije možnosti pomoci bankám A B
APPROACH ROZVAHA MODELOVÉ BANKY Státní dluh Externí pasiva Mezibankovní aktiva Externí aktiva Mezibankovní pasiva Kapitál MULTIAGENTNÍ MODELOVÁNÍ Aktiva Pasiva Bottom-up přístup, zkoumající chování množství subjektů, které na sebe vzájemně působí ve virtuálním prostředí A Aktiva Pasiva o agenti jednotlivé finanční instituce nebo státy, o data jejich rozvahy, případně další informace o vzorce chování o o o kdy banka není schopná splatit úvěr, kdy a jak spustí odprodávání určitého množství aktiv, kdy a jaké stát využije možnosti pomoci bankám B
NEGATIVE SHOCKS Transmission: Credit channel Jednotlivé banky When the system is initialized, one or more banks assets may be entirely or partially written off as a result of an adverse event The next events depend on the size of the primary shock 1. The losses are written off from the bank s capital 2. The rest of the losses is written off from its interbank liabilities and the bank sends negative credit shocks further into the system Jednotlivá kola simulací 3. The rest of the losses is covered by the depositors
NEGATIVE SHOCKS Transmission: Market liquidity channel o o o Defaulting bank has to liquidate all of its assets Low market depth limits the capacity to absorb assets - too much assets on sale leads to lower asset price Asset prices are discounted in each simulation lap based on inverse demand function: o Each bank incurs a loss equal to its asset volume times price difference (assuming marking to market)
ROLE OF SOVEREIGNS Sovereign nodes may support banks in trouble in several ways: Recapitalization (targetting both credit and market liquidity channels) Asset buy-outs (targetting only the liquidity channel) The support weakens the states: 1. State deficit increases 2. CDS spread on sovereign debt increases 3. Implied probability of default increases When a sovereign defaults, it affects exposed banks with a credit shock In the end, more banks may fail
CONTEXT APPROACH CALIBRATION RESULTS
MODEL CALIBRATION Basic problem: Data pro indiviual bank connections is not available Solution: Aggregated data of connections among whole banking systems TOTAL ASSETS (EBA Database, databases of individual central banks)! Sovereign debt (Arslanalp & Tsuda (2012), IMF IFS Database) Interbank assets (BIS International Statistics) External assets (Dopočítáno) External liabilities (Dopočítáno) Interbank liabilities (BIS International Statistics) Capital (Bankscope) + CDS spreads (Bloomberg) + GDP (IMF IFS Database)
MODEL CALIBRATION Final structure of balance sheets in the calibrated system Panel A: Assets Panel B: Liabilities Source: Author
MODEL CALIBRATION Mutual exposures of banking systems(q4 2011) Panel A: Panel B: Zdroj: Autor na základě dat z BIS International Financial Statistics Poznámka: V Panelu A jsou hrany obarveny podle barvy uzlu věřitele, v Panelu B pak podle barvy uzlu dlužníka.
MODEL CALIBRATION Mutual exposures of banking systems(q4 2011) Panel A: Panel B: Zdroj: Autor na základě dat z BIS International Financial Statistics Poznámka: V Panelu A jsou hrany obarveny podle barvy uzlu věřitele, v Panelu B pak podle barvy uzlu dlužníka.
MODEL CALIBRATION Mutual exposures of banking systems(q4 2011) Panel A: Panel B: Zdroj: Autor na základě dat z BIS International Financial Statistics Poznámka: V Panelu A jsou hrany obarveny podle barvy uzlu věřitele, v Panelu B pak podle barvy uzlu dlužníka.
Stát (dlužník) MODEL CALIBRATION Sovereign debt to banking systems (Q4 2011) Exposures of banking sectors as of Q3 2012: Bankovní systém (věřitel) Zdroj: Autor na základě dat z Arslanalp & Tsuda (2012), IMF International Statistics a BIS International Financial Statistics
Stát (dlužník) MODEL CALIBRATION Sovereign debt to banking systems (Q4 2011) Exposures of banking sectors as of Q3 2012: Bankovní systém (věřitel) Zdroj: Autor na základě dat z Arslanalp & Tsuda (2012), IMF International Statistics a BIS International Financial Statistics
MODEL CALIBRATION Exposures of banking sectors as of Q3 2012: Financial system exposure to sovereign debt Q4 2011: Source: Author based on data from Arslanalp & Tsuda (2012), IMF International Statistics a BIS International Financial Statistics
CONTEXT APPROACH CALIBRATION RESULTS
Počet Number bankovních of banking systémů systems v platební in neschopnosti default Number of banking systems in default SIMULATION RESULTS Average results of state aid Panel A: Recapitalization Panel B: Asset buy-outs System illiquidity State aid intensity System illiquidity State aid intensity Source: Author
Number of banking systems in default Počet bankovních Number of banking systémů systems v platební in default neschopnosti Results of recapitalization Source: Author State aid intensity Intenzita State státní aid pomoci intensity (rekapitalizace) CDS sensitivity:
Počet Number bankovních of banking systémů systems v platební in default neschopnosti Počet bankovních Number of banking systémů systems v platební in default neschopnosti Results of recapitalization Source: Author Intenzita státní State pomoci aid intensity (rekapitalizace) Intenzita State státní aid pomoci intensity (rekapitalizace) CDS sensitivity:
Number of banking systems in default Počet bankovních Number of banking systémů systems v platební in default neschopnosti Results of asset buyouts Source: Zdroj: Autor Author State aid intensity Intenzita State státní aid pomoci intensity (odkup aktiv) CDS sensitivity:
Number of banking systems in default Počet bankovních Number of banking systémů systems v platební in default neschopnosti Results of asset buyouts Intenzita State státní aid pomoci intensity (odkup aktiv) Source: Author State aid intensity CDS sensitivity:
THANK YOU.