[Paper Review] Basel III capital surcharges for G-SIBs fail to control systemic risk and can cause pro-cyclical side effects

Sebastian Poledna, Olaf Bochmann+1 more|arXiv (Cornell University)|Feb 10, 2016

Banking stability, regulation, efficiency36 references3 citations

TL;DR

This paper uses an agent-based model to compare two systemic risk (SR) regulation approaches: capital surcharges for G-SIBs (Basel III style) versus network topology optimization. It finds that while capital surcharges reduce SR only if substantially larger than Basel III levels, they induce pro-cyclical effects and reduce efficiency; in contrast, re-shaping financial network topology through a systemic risk tax is more effective and stable.

ABSTRACT

In addition to constraining bilateral exposures of financial institutions, there are essentially two options for future financial regulation of systemic risk (SR): First, financial regulation could attempt to reduce the financial fragility of global or domestic systemically important financial institutions (G-SIBs or D-SIBs), as for instance proposed in Basel III. Second, future financial regulation could attempt strengthening the financial system as a whole. This can be achieved by re-shaping the topology of financial networks. We use an agent-based model (ABM) of a financial system and the real economy to study and compare the consequences of these two options. By conducting three "computer experiments" with the ABM we find that re-shaping financial networks is more effective and efficient than reducing leverage. Capital surcharges for G-SIBs can reduce SR, but must be larger than those specified in Basel III in order to have a measurable impact. This can cause a loss of efficiency. Basel III capital surcharges for G-SIBs can have pro-cyclical side effects.

Motivation & Objective

• To evaluate whether Basel III capital surcharges for G-SIBs effectively reduce systemic risk without unintended side effects.
• To investigate whether re-shaping financial network topology can more efficiently and stably reduce systemic risk than leverage reduction.
• To design and test a systemic risk tax (SRT) that internalizes the marginal systemic impact of individual financial transactions.
• To compare the efficiency, stability, and pro-cyclical effects of capital surcharges versus network-based regulation using agent-based simulations.
• To quantify the marginal contribution of individual loans and transactions to systemic risk using DebtRank and expected systemic loss metrics.

Proposed method

• Develops an agent-based model (ABM) of a financial system with banks, interbank lending, and real economy interactions.
• Uses DebtRank to measure systemic impact, defined as the expected loss in the system due to a node's default.
• Calculates marginal systemic risk contribution of each transaction using the change in expected systemic loss upon adding a liability.
• Proposes a Systemic Risk Tax (SRT) proportional to the marginal increase in expected systemic loss from a transaction.
• Employs three computer experiments: (1) Basel III-style capital surcharges, (2) network topology optimization via SRT, and (3) baseline system without intervention.
• Measures outcomes using cascade size, total losses, and average transaction volume to assess stability and efficiency.

Experimental results

Research questions

• RQ1 Do Basel III-style capital surcharges for G-SIBs significantly reduce systemic risk, or are they ineffective at current levels?
• RQ2 What are the pro-cyclical side effects of capital surcharges, and how do they affect financial system efficiency?
• RQ3 Can re-shaping financial network topology through a systemic risk tax reduce systemic risk more effectively than capital surcharges?
• RQ4 What is the marginal systemic impact of individual interbank loans, and can it be quantified using DebtRank and expected systemic loss?
• RQ5 How do network-based regulation and capital-based regulation compare in terms of stability, efficiency, and resilience to shocks?

Key findings

• Basel III capital surcharges for G-SIBs have a measurable effect on reducing systemic risk only if significantly larger than the levels specified in the framework.
• Capital surcharges induce pro-cyclical effects, amplifying financial instability during economic downturns due to countercyclical leverage constraints.
• The systemic risk tax (SRT) successfully internalizes the marginal systemic impact of transactions, enabling efficient network topology optimization.
• Re-shaping financial network topology through the SRT is more effective and efficient at reducing systemic risk than capital surcharges.
• The marginal contribution of a single loan to systemic risk can be precisely quantified using the change in expected systemic loss upon its addition.
• The model shows that network-based regulation leads to lower total losses and smaller default cascades compared to capital surcharge regimes.