Figure 1.4 Left: All correlations are
Figure 1.5 The system consists of two independent subsystems with internal correlations
Figure 1.6 The system consists of two independent subsystems with internal correlations
Conclusions
Systemic financial risk is an important issue in view of the distress the banking systems all over the world have experienced in the recent years of crises. Even if breakdowns are prevented by the government, the related societal costs are extremely high.
We described the measurement of systemic risk, based on the structural approach originating from structural credit risk models. In particular, the cascading effects that are caused by mutual debt between the individual banks in the system were analyzed in detail. Furthermore, we related the notion of systemic risk to the copula structure, modeling dependency between the performances of the individual banks. The effects of different levels of dependency on the total systemic risk in terms of the value at risk of total losses were demonstrated by examples.
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Chapter 2
Supervisory Requirements and Expectations for Portfolio-Level Counterparty Credit Risk Measurement and Management
Introduction
A bank's counterparty credit risk (CCR) exposure quantifies how much money the counterparty might owe the bank in the event of default. The CCR quantity is broken down into current exposure (CE), which measures the exposure if the counterparty were to default today, and potential exposure (PE), which measures the potential increase in exposure that could occur between today and some time horizon in the future.
The time of default is typically modeled as a stochastic stopping time. As opposed to the known CE, the PE must be estimated, usually by simulation. First, the expected positive exposure (EPE) is computed by simulating a large number (on the order of 102 to 103) of different paths for the various underlying future prices in the possible market environments, using a so-called regularized variance-covariance matrix. Then the system prices each of the derivative transactions on each path for each sample date,3 computes collateral call amounts based on relevant marked-to-market