Climate Stress-test of the Financial System Stefano Ba*ston , - - PowerPoint PPT Presentation

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Climate Stress-test of the Financial System Stefano Ba*ston , - - PowerPoint PPT Presentation

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Climate Stress-test of the Financial System Stefano Ba*ston , FINEXUS Center for financial networks and sustainability, Dept. of Banking and Finance, Univ. of Zurich 2 Outline Network analysis of direct and indirect exposures Disclosure

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Climate Stress-test

  • f the Financial System

Stefano Ba*ston, FINEXUS Center for financial networks and sustainability,

  • Dept. of Banking and Finance, Univ. of Zurich
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2

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  • Network analysis of direct and indirect exposures
  • Disclosure of climate-relevant financial informaFon is key to

improve risk esFmaFons and create the right incenFves for

  • investors. However, beHer disclosure may not be sufficient.
  • The Fming and credibility of the implementaFon of climate

policies maHer.

  • Early and stable policy framework: smooth carbon-asset

values adjustments

  • a late and abrupt implementaFon: adverse systemic

consequences for the financial system. Outline

(1) BaSston, S., Mandel, Antoine, Monasterolo, I., Schuetze, F., VisenFn, G.: A Climate stress-test of the EU financial system. Available SSRN id=2726076. (2016).

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Climate Relevant Sectors Financial Sectors Shocks on financial exposures Shocks on Financial provisions Climate Policies Climate Change Financial Regulation Systemic risk, inequality

The Source of Complexity in the Climate – Finance nexus

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  • Dashboard on EuroArea network-based stress-test

hHps://simpolproject.eu/2016/06/09/debtrank-2/ [BaSston et al 2016, Leveraging the network. StaFsFcs and Risk Modeling, 1–33].

  • Dashboard Climate Stress-test financial

hHps://simpolproject.eu/2016/06/10/climate-stress-test/ [BaSston et al. 2016, A Climate stress-test of the financial system. Available at SSRN id=2726076.].

Dashboards

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6

Loan Porgolios of Major Euro Area Banks – Leverage across sectors

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7

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TradiFonal systemic risk model predict liHle contagion, because two key assumpFons rule out contagion by construcFon 1. R =1 i.e. banks assets can be liquidated at any Fme with no loss 2. Only default valua=on: obligaFon’s value unaffected by losses on

  • bligor’s equity unless default.

Conserva=on constraint on losses in the process. à network structure is irrelevant for the aggregate losses. à Almost no banks’ defaults ajer iniFal defaults

The NO-CONTAGION Paradox

In a distress contagion accounFng framework, intra-financial contagion approximated by simple and instrucFve formula H = ε s + (1-RE) β ε s = 2 ε s where H is the relaFve equity loss in the banking system, b is the interbank leverage, e is the external asset leverage, and RE is the recovery rate on external assets.

(1) VisenFn et al. 2016 “Rethinking Financial Contagion”, (2) BaSston, Caldarelli, D’errico, Gurciullo, S. (2016). Leveraging the network. StaFsFcs and Risk Modeling, 1–33. BaSston, S., Roukny, T., SFglitz, J., Caldarelli, G. & May, R. The Price of Complexity in Financial Networks. PNAS (2016) www.pnas.org/content/113/36/10031.full

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FINEXUS climate stress test methodology

² New framework based on network analysis to assess the largest exposures of financial actors to climate policy risks ² 3 key conceptual/methodological innovaFons:

  • 1. Reclassifica=on of NACERev2 sectors
  • 2. QuanFficaFon of direct exposure through external assets
  • 3. Assessment of indirect exposure, including intra-financial

interlinkages

DATASETS:

  • Bvd Orbis and Bankscope
  • ECB Data Warehouse
  • NACE code descripFon

Ba#ston, S., Mandel, Antoine, Monasterolo, I., Schuetze, F., Visen.n, G.: A Climate stress-test of the financial system. Available SSRN id=2726076. (2016).

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Banks

(MFIs)

Total assets: 31 T Equity=A-L: 3 T

NFC

(Non-financial corporaFons)

Total assets: 21T

HH

(Households)

Total assets: 22T

Gov

(Government) Total assets: 5T

OFI

(Other Financial InsFtuFons)

Total assets: 26.8T

Non-MMF IF

(Non-MMF Investment funds) Total assets: 10.3T

I&PF

(Insurance& Pension funds) Total assets: 9.3T

9% 13% 16% 5% 15% 6% 30% 12% 5 % 24%

Equity holdings Bond holdings Loans holdings NB!: NormalizaFon for all actors – by the total assets

Some indirect exposures of financial sectors to the real economy

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Banks

(MFIs)

Total assets: 31 T Equity=A-L: 3 T

NFC

(Non-financial corporaFons)

Total assets: 21T

HH

(Households)

Total assets: 22T

Gov

(Government) Total assets: 5T

OFI

(Other Financial InsFtuFons)

Total assets: 26.8T

IF

(Non-MMF Investment funds) Total assets: 10.3T

I&PF

(Insurance& Pension funds) Total assets: 9.3T

Financial exposures Macro-economic feedback

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Fossil-fuel extrax/on sector U/li/es Energy-intensive Housing Transport Main channels:

  • 1. Fossil-Fuel->U/li/es->Transport
  • 2. Fossil-Fuel->Transport
  • 3. U/li/es->Housing

4.U/li/es->Energy-intensive Reclassifica.on logic:

  • 1. U/li/es, transport, housing -

top sectors for GHG emissions (scope 1)

  • 2. Fossil-fuels (low direct but high

indirect emissions)

  • 3. Ac/vi/es affected by climate

policy either through costs or revenues). Fossil-fuel supply Electricity supply Electricity supply

Figure 1. Diagram illustraFng the reclassificaFon of sectors from NACE Rev2 codes into climate relevant sectors.

Methods: iden=fica=on of the climate sensi=ve sectors

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Methods: iden=fica=on of direct&indirect exposures to the the climate sensi=ve sectors

Ai =

S∈S

∑

αij

Equity +αij Bonds +αij Loans j∈S

∑

⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟+ Ri

Direct exposures: through assets of the market player

Ai

AFS = αiS

i∈F

∑

αij

  • Total assets of the financial actor i
  • Monetary value of exposure of i to j

S

  • Set of climate-relevant sectors
  • Exposure of insFtuFon F to

a given climate sector

Indirect exposures: through interlinckages of the market player with its couterparFes

Ai = αij

Equity(Aj)+αij Bonds(Aj)+αij Loans(Aj) j∈F

∑

⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟+ αik

Equity +αik Bonds +αik Loans k∈A/F

∑

⎛ ⎝ ⎜ ⎞ ⎠ ⎟+ Ri

αij

0 ⋅α jk

  • Product of exposures along the chain
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Methods: iden=fica=on of the climate sensi=ve sectors

Fossil-fuel U,li,es Energy- intensive Housing Transport B C D F H NACE2 codes Climate-sensi,ve sectors Asset PorBolio by instrument Equity Bonds Loans Reclassifica,on of economic sectors from NACE2 into climate-sensi,ve sectors Classifica,on of assets according to instrument and climate-sensi,ve sectors Asset PorBolio by climate sector

TradiFonal Proposed

Ba#ston, S., Mandel, Antoine, Monasterolo, I., Schuetze, F., Visen.n, G.: A Climate stress-test of the EU financial system. Available SSRN id=2726076. (2016).

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Equity holdings in EU and US listed companies. Sector composiFon of aggregate insFtuFonal sectors world-wide according to BvD data 2015.

Results: Exposure to climate sensi=ve sectors

Ba#ston, S., Mandel, Antoine, Monasterolo, I., Schuetze, F., Visen.n, G.: A Climate stress-test of the EU financial system. Available SSRN id=2726076. (2016).

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PorYolio composi=on of top world-wide Investment Funds: climate-sensi=ve sectors exposure

u This micro-level approach allows us to understand heterogeneity of investors’ exposure and porgolio allocaFon.

Ba#ston, S., Mandel, Antoine, Monasterolo, I., Schuetze, F., Visen.n, G.: A Climate stress-test of the EU financial system. Available SSRN id=2726076. (2016).

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PorYolio composi=on of top world-wide Banks: climate-sensi=ve sectors exposure

u This micro-level approach allows us to understand heterogeneity of investors’ exposure and porgolio allocaFon.

Ba#ston, S., Mandel, Antoine, Monasterolo, I., Schuetze, F., Visen.n, G.: A Climate stress-test of the EU financial system. Available SSRN id=2726076. (2016).

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Rela=ve porYolio composi=on of top world- wide Banks: climate-sensi=ve sectors exposure

Ba#ston, S., Mandel, Antoine, Monasterolo, I., Schuetze, F., Visen.n, G.: A Climate stress-test of the EU financial system. Available SSRN id=2726076. (2016).

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Impact on the top 50 listed EU banks of a 100% shock in the market capitalizaFon of the climate-sensiFve sectors in different, progressive aggregaFons.

Exercise 1. Upper bound of Euro Area banks’ loss: 100% shock on Fossil-Fuel+U=li=es sector

  • Equity loss of EU banks from a fossil-fuel sector shock only is 2.55%, and increases to

6.08% when including indirect effects.

  • Losses increase to 13.18% (direct effect) and 27.91% (direct and indirect effect) when

including uFliFes and energy-intensive industries on equity shares (1.2 T).

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Exercise 2. Shocks obtained from LIMITS IAM database

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Exercise 2. Shocks obtained from LIMITS IAM database

First round losses (top) and second round losses (boHom)

  • f a “brown” and “green”

banks’ equity

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Exercise 2. Shocks obtained from LIMITS IAM database

Value at Risk (5% significance) for the 20 most affected EU banks in the dataset, under the scenario of green (brown) investment strategy. Darker colors: VaR(5%) in the distribuFon of first-round losses. Lighter colors: VaR(5%) in the distribuFon of first- and second - round losses together.

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Climate Relevant Sectors Financial Sectors Shocks on financial exposures Shocks on Financial provisions Climate Policies Climate Change Financial Regulation Systemic risk, inequality

The Source of Complexity in the Climate – Finance nexus

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  • Financial interconnectedness maHers for financial stability

and macro-prudenFal policy.

  • In the presence of uncertainty on value of assets backing

up obligaFons, and uncertainty on default resolu=on process, direct losses from shocks can be doubled, due to indirect losses via intra-financial complexity1.

  • Further, inaccuracy on price of (systemic) risk increases

with complexity2.

  • CollecFve moral hazard: the financial system does not pay

for the Conclusion

(1) BaSston, S., Puliga, M., Kaushik, R., Tasca, P., & Caldarelli, G. (2012). DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk. ScienQfic Reports, 2, 1–6. BaSston, Caldarelli, D’errico, Gurciullo, S. (2016). Leveraging the network. StaFsFcs and Risk Modeling, 1–33. (2) BaSston, S., Roukny, T., SFglitz, J., Caldarelli, G. & May, R. The Price of Complexity in Financial Networks. PNAS (2016) www.pnas.org/content/113/36/10031.full

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  • Climate policies as potenFal source of (endogenous) shocks to

the financial system.

  • TradiFonal cost-benefit analyses: aggregate esFmates not

adequate to idenFfy individual risks and their propagaFon through the financial system.

  • Network analysis of financial dependencies: direct and

indirect exposures to climate-policy relevant sectors represent a large por=on of investors’ porYolios – in parFcular for investment funds and pension funds. Conclusions

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Findings of our study1 suggest that:

  • Disclosure of climate-relevant financial informa=on is key to

improve risk esFmaFons and create the right incenFves for

  • investors. However, beHer disclosure may not be sufficient.
  • The =ming and credibility of the implementaFon of climate

policies maHer. An early and stable policy framework would allow for smooth carbon-asset values adjustments and lead to potenFal net winners and losers.

  • In contrast, a late and abrupt policy implementaFon would

have adverse systemic consequences for the financial system. Conclusions

(1) BaSston, S., Mandel, Antoine, Monasterolo, I., Schuetze, F., VisenFn, G.: A Climate stress-test of the financial system. Available SSRN id=2726076. (2016).