Risk assessment on interbank networks has drawn attention from researchers since the 2007 Subprime mortgage crisis.The lack of data for interbank transactions, which are usually not disclosed unless required by regulatory bodies, is one of the most critical difficulties to this research. A remedy to this issue is the dense reconstruction of interbank networks by using balance sheet data. The Maximum-Entropy estimation has been adopted by literature, however, this method produces networks with unrealistic properties: too dense in terms of having too many links. One alternative is sparse reconstruction that proposed by literature recently. This thesis applies the Message-Passing algorithm, which is extensively applied in Thermodynamics or Computer Science, and is suggested by Mastromatteo et al.  for application in network reconstruction. Dense networks and sparse networks are reconstructed from Statistics on Depository Institutions data provided by Federal Deposit Insurance Corporation, and are compared by performance in both network properties and contagion simulations. The popular contagion mechanisms proposed by Furfine  and the model of liquidity dry-up contagion proposed by Malherbe  are adopted and compared in contagion simulations. Results show that dense networks and sparse networks perform differently in network properties and in contagions triggered by single-bank failures, while for contagions triggered by multiple-bank failures, both types of networks perform similarly. Furfine’s mechanism fail to predict some bank failures via the credit risk contagion on liquidity side, while these failures can be simulated by the liquidity dry-up model via fire-sale and marking-to-market effect. Both mechanisms overestimate the losses before the crisis, yet this signals the instability of the banking system, while the liquidity dry-up model proposes an explanation for why the banking system did not fail before the crisis, regarding to whether the equilibrium of high liquidity will shift to the self-fulfilling liquidity dry-up equilibrium. Implications on regulation are given.
|Date of Award||23 Apr 2016|
|Supervisor||Andreas Krause (Supervisor) & Simone Giansante (Supervisor)|
- Systemic risk
- Liquidity dry-up
- Interbank network