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Bank Efficiency and Systemic Risk

Cost efficiency is usually seen as healthy, but research finds a "dark side of efficiency": banks that cut costs aggressively can do so by under-investing in risk management, screening, and monitoring, taking on more risk that surfaces in a crisis. Fiordelisi, Marques-Ibanez & Molyneux (2011) document that higher efficiency can precede greater bank risk. Dr. Nguyen’s thesis (Chapter 3) develops this for systemic risk specifically: it estimates each bank’s systemic contribution with MES, SRISK, and ΔCoVaR and relates it to cost efficiency, using Granger-causality networks to map how distress propagates across institutions. The lesson is that an efficiency gain that comes from skimping on prudence can raise a bank’s contribution to system fragility.

Why it matters

A bank can look impressively lean for two very different reasons: genuine productivity, or quietly cutting the unglamorous spending on risk controls, loan screening, and monitoring. The second kind of "efficiency" is borrowed from the future: the saved cost shows up as extra risk that only bites in a downturn. Dr. Nguyen’s thesis makes this systemic, asking not just "is this bank riskier?" but "does its cost-cutting raise its contribution to a system-wide collapse?", measured with SRISK, MES, and ΔCoVaR and traced through networks of which banks Granger-cause distress in which others.

Formulas

Systemic-risk-on-efficiency relationship (thesis Ch. 3)
SystemicRiski,t=α+β Efficiencyi,t−1+γ Xi,t+εi,t\text{SystemicRisk}_{i,t} = \alpha + \beta\,\text{Efficiency}_{i,t-1} + \gamma\,X_{i,t} + \varepsilon_{i,t}
Systemic contribution (MES, SRISK, or Δ\DeltaCoVaR) regressed on lagged cost efficiency and controls XX. A "dark side" appears when β\beta implies higher efficiency raises systemic-risk contribution.
Granger-causality network link
j→Grangeri  ⟺  past distress of j helps predict distress of ij \xrightarrow{\text{Granger}} i \iff \text{past distress of } j \text{ helps predict distress of } i
A directed edge from bank jj to bank ii when jj’s lagged returns/volatility significantly predict ii’s, mapping interconnectedness and propagation paths across the system.

Worked examples

Scenario

Two banks report identical, improving cost-efficiency ratios. One achieved it through better technology, the other by cutting loan-screening and risk-control staff. Why might only the second raise systemic risk?

Solution

The technology-driven bank produces the same lending with fewer resources, with no extra risk. The cost-cutting bank bought its efficiency by weakening screening and monitoring, so it accumulates riskier, more correlated exposures that fail together in a downturn, raising its MES, SRISK, and ΔCoVaR. Identical efficiency ratios can thus mask opposite implications for system fragility, the dark-side mechanism.

Scenario

How does Dr. Nguyen’s thesis (Ch. 3) use Granger-causality networks to study systemic risk among banks?

Solution

It builds a directed network where an edge from bank jj to bank ii means jj’s past distress (returns or volatility) helps predict ii’s, capturing interconnection and propagation paths. Combined with firm-level MES/SRISK/ΔCoVaR, the network shows not only which banks are individually systemic but how shocks travel between them, and how cost efficiency relates to a bank’s position and contribution in that web.

Common mistakes

  • ✗A more efficient bank is always a safer bank. Efficiency gained by cutting risk management, screening, and monitoring can raise risk; this "dark side" means efficiency and safety are not the same thing.
  • ✗Bank efficiency only affects the individual bank, not the system. Dr. Nguyen’s thesis shows efficiency can relate to a bank’s contribution to systemic risk, measured with MES, SRISK, and ΔCoVaR across interconnected banks.
  • ✗Granger-causality networks prove one bank causes another’s failure. Granger causality is predictive, not structural: an edge means past distress of one bank helps predict another’s, which maps propagation paths but is not proof of direct causation.

Revision bullets

  • •"Dark side of efficiency": cost-cutting via weaker risk controls raises risk
  • •Fiordelisi, Marques-Ibanez & Molyneux (2011): efficiency can precede higher bank risk
  • •Dr. Nguyen’s thesis (Ch. 3) extends this to systemic risk (MES, SRISK, ΔCoVaR)
  • •Granger-causality networks map distress propagation across banks
  • •Identical efficiency ratios can mask opposite implications for system fragility

Quick check

The "dark side of efficiency" in banking refers to the finding that

In Dr. Nguyen’s thesis (Ch. 3), a Granger-causality edge from bank jj to bank ii means

Connected topics

Systemic RiskSIFIs / TBTFCoVaRMES / SRISK

Sources

  1. Fiordelisi, Marques-Ibanez & Molyneux (2011)
    Fiordelisi, F., Marques-Ibanez, D., and Molyneux, P. "Efficiency and Risk in European Banking." Journal of Banking & Finance 35 (5), 2011, 1315-1326.
    Evidence that lower cost and revenue efficiency can precede higher bank risk; the "dark side" motivation.
  2. Nguyen, V. P. (Thesis, Ch. 3)
    Nguyen, V. P. Bank Efficiency and Systemic Risk. Doctoral thesis, Chapter 3. (Author’s own research; relates cost efficiency to MES/SRISK/ΔCoVaR using Granger-causality networks.)
    The authoritative source in this atlas for the bank-efficiency-and-systemic-risk results and the MES/SRISK/ΔCoVaR estimation.
  3. Billio, Getmansky, Lo & Pelizzon (2012)
    Billio, M., Getmansky, M., Lo, A. W., and Pelizzon, L. "Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors." Journal of Financial Economics 104 (3), 2012, 535-559.
    Granger-causality network measures of connectedness and systemic risk across financial institutions.
How to cite this page
Dr. Phil's Quant Lab. (2026). Bank Efficiency and Systemic Risk. Derivatives Atlas. https://phucnguyenvan.com/concept/frm-bank-efficiency-systemic
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