Sentence examples for banks as a function from inspiring English sources

Open image in new window Fig. 1 Contour plots of the number of defaults for n=100 banks as a function of a bankruptcy costs and b fire sales, averaged over 1000 simulations of Π.

Open image in new window Fig. 2 Number of defaults for n=100 banks as a function of a integration and b diversification of the cross-holdings matrix C, realized fraction of cross-holdings' market value κ=0.8, averaged over 100 simulations of Π, each averaged over 100 simulations of C. The simulation procedure is explained in Remark 4.1.

Open image in new window Fig. 13 Capital requirements for core and periphery banks as a function of fire sales (varying ρ and fixed γ=0.2) a separately and b together with different levels of bankruptcy costs (varying β and fixed α=1). Figure (b) includes bankruptcy costs for varying β with ρ=0.5 fixed.

Open image in new window Fig. 10 Contour plots of the number of defaults for n=100 banks as a function of a bankruptcy costs and b fire sales, conditional on an initial core shock, averaged over 100 simulations of Π, simulated as a core-periphery random network.

Open image in new window Fig. 5 Contour plot of the number of defaults for n=100 banks as a function of both parameters of fire sales, ρ and γ, for α=0.9, β=0.9, c=0.5, d=10, κ=0.8, (mathbb {I}=boldsymbol {1}) in a and (mathbb {I}=boldsymbol {0}) in b, averaged over 100 simulations of Π, each averaged over 100 simulations of C. The simulation procedure is explained in Remark 4.1.

Open image in new window Fig. 4 Standard deviation of the number of defaults for n=100 banks a as a function of both parameters of bankruptcy costs for 1000 simulations of Π, and b as a function of integration of the cross-holdings matrix C for 100 simulations of Π, each averaged over 100 simulations of C. The simulation procedure is explained in Remark 4.1.

Prudential regulation is shown to operate at a collective level, regulating each bank as a function of both its joint (correlated) risk with other banks as well as its individual (bank-specific) risk.

Open image in new window Fig. 12 Contour plot of the average number of bank defaults as a function of fire sales parameters ρ banks and γ in an ENF-model, averaged over 100 simulations of Π and periphery link choices.

Figure 12 displays the average defaults in the banking system as a function of the fire sales parameters.

The EBRD sees good local banks as a crucial part of functioning financial markets.

The author extends an improved structure of feedback neural network to formulate the error function in the optimization of QMF banks as a Lyapunov energy function to find the Hopfield-related parameters.