Economie
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The post-crisis analysis of January 30, 2026, via the Minsky Moment Detector, reveals that the European banking system has reached a critical tipping point. This event is caused by the convergence of the collapse of the Silver, the unwinding of the Yen Carry Trade, and the fall in Tech stocks, generating a shock of an unprecedented magnitude since 2008. The current crisis is not related to poor credit quality (solvency) but to a severe liquidity crisis exacerbated by convexity factors and increased counterparty risk on derivatives. Interbank confidence is compromised, as evidenced by the 45 basis point spike in the Euribor-OIS Spread over 24 hours. An estimate of 68% indicates a high probability of a severe tightening of the interbank market (Spread exceeding 100 basis points) within the next four days.
1. Transmission Dynamics: the Rate Derivates and CVA Channel
The main shock for European banks, particularly Deutsche Bank, stems from the severe flattening of the yield curve and the increase in counterparty credit risk adjustments (CVA). Major players manage Interest Rate Swap portfolios exceeding 50 times their market capitalization. The forced sale of Treasuries caused a rise in long-term rates and a significant negative convexity risk. The market illiquidity parameter (lambda) in Kyle’s model jumped by 450% at the end of January. For Deutsche Bank, every unexpected 10 basis point (bps) move in rates now results in a CVA adjustment of approximately 1.2 billion euros, compared to 250 million in normal periods. Concurrently, the USD/EUR Cross-Currency Basis Swap (XCCY) fell to -85 bps, signaling difficulties for European banks in obtaining dollars for their funding due to the disengagement of the U.S. Repo market (De-Risking). Steelldy identifies a nascent « Minsky Moment, » forcing banks to liquidate quality assets (gold, silver) to cover derivative margin calls, which weighs on the value of their own collateral.
2. The Domino Effect of Emerging Markets (EM Exposure)
The unwinding of the JPY Carry Trade led to a « Flight to Safety, » causing a sharp devaluation of emerging market currencies, particularly the Mexican Peso and the Brazilian Real (-12% in one session), with Santander and BNP acting as infection vectors. For Santander, a bank heavily exposed to Latin America, this situation creates a double negative effect: an unfavorable exchange rate risk impacting its net interest income (PNB) upon conversion of earnings into EUR, and a deterioration of non-performing loans (NPLs) due to the rising cost of local debt for its borrowers. A Monte Carlo simulation modeling a sustained 15% depreciation of emerging market currencies and a 100 basis point hike in the Fed’s key interest rate shows an erosion of Santander’s CET1 ratio by 140 basis points, threatening its minimum regulatory threshold.
3. Quantitative Modeling of Systemic Risk
To model the dynamics of systemic contagion, an SIR (Susceptible-Infected-Recovered) epidemiological model was integrated with Markov Regime Switching Regressions. The banking system is viewed here as a dependency graph where « Susceptible » banks have a liquidity buffer greater than 20%, « Infected » banks suffer a collateral run or a margin call, and « Recovered » banks are either recapitalized or liquidated. The transmission rate (beta), representing interbank correlations, was found to be 0.82, indicating that one failure almost immediately impacts the entire system, unlike the normal value of 0.12. The Markov Regression analysis confirms entry into a « Turbulent » Regime with a persistence probability of 0.95, suggesting that the market will not spontaneously return to normal without intervention. Since the assumption of asset decorrelation failed, the modeling of extreme co-movement risk relies on the use of Tail Copulas, specifically the Gumbel Copula. With a parameter theta=4.2, the tail dependence (λU=2−21/θ) reaches 0.78. This value implies that the risk of a pair of banks (examples: Deutsche Bank and BNP Paribas) simultaneously experiencing a 20% capital loss is 12 times higher than what a model based on standard Gaussian correlation would predict. Furthermore, this highlights the illusion of geographical diversification within Europe during a crisis.
4. Specific Analysis of Institutions (G-SIBs)
The specific analysis of G-SIBs, based on the Steelldy Risk Score (aggregate of market, credit, and liquidity sensitivities), highlights three critical institutions. Deutsche Bank shows a dominant risk related to its derivatives (rate/convexity), with a tipping point defined by a USD liquidity gap exceeding $15 billion. Its vulnerability lies in the extreme sensitivity of its derivatives book: a drop in high-yield bonds collaterally depreciates swaps.
BNP Paribas is dominated by counterparty risk with Hedge Funds/PB. Its critical threshold is set if the S&P 500 falls below 4800. As a major « Prime Broker, » a crash in Tech/precious metals stocks would trigger massive margin calls from its clients, threatening the bank with losses on loaned funds.
Finally, Santander is exposed to EM currencies and retail credit; its tipping point is a devaluation of the MXN/BRL exceeding 20%. Its capital is already impacted by emerging market exposures, and an interruption of « Prime Brokerage » by US banks restricting dollar access would worsen its refinancing crisis.
The European banking system is facing a structural liquidity crisis and extreme correlation, despite solid solvency ratios (CET1) compared to 2008. The fragility lies in the hidden leverage of interest rate derivatives and the volatility of foreign currency balance sheets. The final assessment indicates a brutal « repricing » of liquidity risk, pinpointing Deutsche Bank and BNP Paribas as potential epicenters of contagion. Urgent intervention by Central Banks via swap lines is necessary to stabilize the Dollar funding market.
