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This Technical Note was prepared by Sheheryar Malik.
“Procyclicality and structural trends in investment allocation by insurance companies and pension funds,” Bank of England Procyclicality Working Group Discussion Paper, 2014.
Specifically, the group of banks included in the analysis includes Barclays, HSBC, RBS, Lloyd’s, and Standard Chartered. Insurers (life only) include Aviva, Legal & General, Prudential Plc, and Standard Life. The insurance sector—as dealt with in this note—refers specifically to life insurers.
Limitations of market-based data for such assessments, in general, may also encompass the role played by reduced trading volumes and liquidity. Such market conditions limit the extent to which asset prices reflect and reveal the fundamental information.
Note that the SA is normally used to measure credit risk in portfolios of loans. In contrast, in this exercise we apply the SA to measure risk in a portfolio of FEs across sectors. Widely known applications of the structural approach include the Credit Metrics framework (Gupton et al., 1997) and the KMV framework (Crosbie et al., 1998).
In what follows, distress and default are used interchangeably.
The generalization of this approach includes, in addition to the distress/default state, different credit risk quality states (ratings), and thus changes in credit risk quality are also triggered by changes in the firm’s asset value with respect to threshold values.
The distress dependence structure embedded in the multivariate CIMDO-density is recovered simultaneously when inferring the CIMDO-density. When modeling parametric copula functions, a key challenge is to calibrate adequately such functions. Due to the information constraints that modelers face when modeling risk, dependence modeling becomes a daunting task. The CIMDO approach recovers the CIMDO-copula simultaneously when inferring the multivariate density. Thus, no additional modeling is required for the CIMDO-copula.
The PMD modeling undertaken has been shown to be robust under restricted data environments according to the probability integral transform criteria; see Segoviano (2006), and Segoviano and Espinoza (2016), forthcoming.
For details on mapping CDS spreads into PoDs, see Hull, J., and A. White (2000), “Valuing Credit Default Swaps I: No Counterparty Default Risk,” Journal of Derivatives, 8 (Fall), pp. 29–40.
For details on the complete set of potential measures, please refer to Segoviano and Goodhart (2009).
In the ES computation we set α=0.01 (1 percent).
The decline in the marginal contribution to overall systemic risk by the banking sector was ascertained using a Shapley value risk attribution methodology; see Tarashev et al. (2010) and Segoviano et al. (2016).
SRISK measures the expected capital shortfall of the financial system, if equity values were to decline to global financial crisis levels. While computation of the Tail Risk Index focused on banks and insurers, the SRISK measure attributes more than 90 percent of variation in systemic risk to developments in these two sectors. The latter measure is available at V-Lab, http://vlab.stern.nyu.edu/en/.
Similar periods of banking sector distress were indentified using a CoVaR approach.
Banks may also be exposed to CCPs via equity ownership and contributions to default funds.
Insurers invest indirectly in banks’ debt and equity through investment funds, and also place deposits (albeit small amounts) with banks. Large exposure data collected from U.K. banks show that a few insurers lent large enough amounts through repo markets to appear in banks’ top 20 counterparties.