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«WP/06/139 The Credit Risk Transfer Market and Stability Implications for U.K. Financial Institutions Jorge A. Chan-Lau and Li Lian Ong © 2006 ...»

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The formation of bancassurance groups through the merger of banks with insurers represents another example of cross-sector linkages. In the United Kingdom, for example, ownership interests of U.K. banks in insurance companies have been significant, with 6 of the 10 largest U.K.-owned banks having equity shares in life insurance subsidiaries at end-2003. This is in contrast to the direct credit exposure of U.K. banks to the life insurance sector, with loans to insurers and pension funds amounting to just over 6 percent of the major banks’ Tier 1 capital. Existing empirical evidence shows that the equity prices of individual bancassurers in the United Kingdom were adversely affected by disruptions in the U.K. life insurance sector over the 2001–03 period, suggesting a spillover effect though ownership links (Monks and Stringa, 2005).

Meanwhile, the development of techniques to repackage credit risk into “slices” has facilitated the increasing shift of credit risk away from the banking sector.14 Credit risk has See Rule (2001).

These techniques, to a large extent, have been borrowed from mortgage-backed securities (MBSs). Lessons learned in this market have been transferred to the credit derivatives market.

-9been transferred to insurance companies and to other capital market participants such as hedge funds, mutual funds, and pension funds.15 Banks account for the major share of CRT market activity—they use CRT instruments for diversifying or hedging risks in their banking books (portfolio management). Banks also provide investor services by devising and intermediating CRT products and make markets for credit derivatives (ECB, 2004).

Individual banks could be involved in both portfolio management and intermediation activities. In the United Kingdom, the larger banks (by assets) participate in the CRT market.

Within the European Union, insurance companies are the largest buyers of credit risk outside the banking system (ECB, 2004). Different types of insurers have been using CDOs and CDSs to take on credit risk at varying levels of seniority and forms, commensurate with their balance sheet needs and regulatory restrictions (see Rule, 2001). In countries such as Denmark, German, Japan, the Netherlands, and the United Kingdom, life insurers are reportedly seeking more credit risk in order to increase the yield on their assets. Ironically, these investors may then have to seek recourse from their respective banks by drawing on their credit lines if losses crystallize during a credit event, in order to meet their obligations under these CRT instruments.

The transfer of credit risk between institutions also gives rise to several other risk factors.16 Market risk is associated with changes in the credit spreads of names in the underlying portfolio of a CDO tranche. The seller is often exposed to liquidity risk as well, as it may be difficult to sell an asset quickly in an insufficiently liquid secondary market. That said, investors with less liquid liabilities than banks such as life insurers and hedge funds may benefit from the liquidity premium. The use of standardized tranches on credit derivatives indices to hedge exposures to single tranches gives rise to basis risk, since the instruments are not perfectly matched. Counterparty risk arises from the possibility that the risk buyer may default in settling a claim, while legal risk refers to the lack of complete and timely documentation, in the event of a dispute over a particular transaction. Ratings risk arises from the fact that ratings tend to reflect the average risk of a security, without factoring in the dispersion of risk around its mean (Cousseran and Rahmouni, 2005). This may limit the usefulness of ratings given the structured nature CDOs.17 There is also the possibility of “ratings arbitrage,” wherein CDO issuers may be tempted to choose rating agencies based on As an example, credit risk in the banking sector spiked up in early May 2005 on rumors that hedge funds active in credit derivative markets might have incurred large losses following the ratings downgrade of automobile companies General Motors (GM) and Ford. The influence of hedge funds on the banking industry is largely due to the substantial contribution of hedge funds to investment banks’ fee income. The banks generate these fees by providing trading ideas, financing positions, and executing trades on behalf of hedge funds.

Hence, factors affecting hedge funds’ performance affect banks’ profitability. For instance, idiosyncratic shocks that reduce hedge funds’ creditworthiness increase counterparty risk to banks involved in financing these funds’ positions. It was not entirely surprising, then, that banks’ equity prices fell and credit spreads widened when hedge funds’ investment strategies underperformed following the automobile companies’ downgrades.

See Appendix II for a detailed discussion on the individual risk factors. It should be noted that these risks are not unique to CRT instruments.

It should be noted that rating agencies are constantly refining the rating criteria applied to structured products.

- 10 the best rating that is assigned to their particular issue or tranche, to minimize funding costs.

Model risk arises in the valuation of CDOs using a myriad of complex models that continue to evolve.


A direct assessment of the exposures of financial institutions in the United Kingdom to credit derivatives—and their implications for financial stability—is not possible, given the lack of available information in this area. To overcome this limitation, we estimate the exposure of a firm to the credit derivatives market by examining the extent to which developments in the credit derivatives market explain the variability of the firm’s equity returns in the longer run.

The assumption that such a relationship exists is reasonable, given that gains/losses on holdings of CRT instruments are quickly manifest in a company’s financial data in an efficient market.18 We assess this exposure indirectly by using the vector autoregression (VAR) approach first suggested by Hasbrouck (1991a, 1991b). In this case, we estimate the model using daily data for the period August 28, 2003–September 15, 2005.

The choice of factors in our model is guided by the requirement that the econometric model captures both systemic risk in the financial system and the specific risk associated with credit derivatives products. The major financial services groups analyzed here represent either the largest life insurers or major banking groups, which list their shares locally in the United Kingdom: Aviva, Barclays, Halifax Bank of Scotland (HBOS), Hongkong and Shanghai Banking Corporation (HSBC), Lloyds, Legal and General, Royal and Sun Alliance, and Royal Bank of Scotland (RBS).

Market prices are used in our analysis, because they are readily available on a daily basis (as opposed to accounting data), and quickly transmit financial information about individual companies.19 Equity prices for the major financial groups are obtained from Bloomberg L.L.P. The slope of the yield curve, measured as the difference between the yields on the 10year and 2-year U.K. government bonds, is included as a measure of contemporaneous economic conditions that would lead to simultaneous movements in equity returns and structured credit product prices. The yield data are obtained from the generic 2-year and 10year yield series constructed by Bloomberg L.L.P.

While there has been a rapid proliferation of CRT products, credit derivatives are among the most widely used products. Consequently, the introduction and rapid acceptance of benchmark credit derivatives indices, specifically iTRAXX in Europe, has helped develop a two-way market for standardized CDOs. Given that the tranche seniority of a CDO affects its riskiness, we include as factors the prices of the equity tranche and a number of mezzanine

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tranches with varying degrees of seniority.20 The super-senior tranche is not included in the analysis since its time series just started in mid-2004. Price data for the different tranches are obtained from JPMorgan.

Given the vector of n endogenous variables, Yt=(y1t, y2t,..., ynt)', the corresponding

unrestricted VAR system of order p is given by:

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where c is an n-vector of constant terms, Φi (i=1,...,p) are n-by-n coefficient matrices, and εt is a vector of uncorrelated, independent, and identically distributed error terms. The error terms are also serially uncorrelated. Under certain technical conditions, described in detail in econometrics texts like Hamilton (1994), the vector autoregression system in equation (1)

admits the following vector moving average representation (VMA):

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variable yj on variable yi. Therefore, the long-term cumulative impact of variable yj on variable yi can be measured by adding up the coefficients associated with the lag

operatorψ ij ( L) :

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Equation (3) suggests that variance decomposition can be used to quantify the overall importance of innovations to variable yj for explaining subsequent realizations of variable yi vis-à-vis the other endogenous variables. Specifically, the overall importance of variable yj is

captured by the relative share of the variance of variable yi that it explains:

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where σ ε2j is the variance of the innovation to variable yj. Note that our VAR framework does not choose a particular ordering of the variables entering equation (1), and hence it is a statistical description of the dynamic interrelations among the variables analyzed. While a structural VAR may offer some advantages for interpreting the data, it requires specifying a priori a causal ordering of the variables, which we do not deem appropriate for this study.

In interpreting the results, we do not make any assumption as to whether a particular institution is long or short the credit exposure. We assume that, in case of defaults, losses fall within the attachment and detachments points of the benchmark iTRAXX. Therefore, the higher the fraction of equity return volatility explained by a senior tranche, the lower both the credit exposure of the firm and the potential impact on financial stability.

Our empirical results suggest that U.K. insurance companies tend to have more conservative exposures to the CRT market. Table 1 shows the longer-term impact of volatility in the credit derivatives market on the stock price returns of our sample companies.21 The major insurance companies tend to be more exposed to volatility in the more senior mezzanine tranches, with attachment points of 9–12 percent and 12–22 percent.

In contrast, the bancassurance businesses tend to be more exposed to riskier CRT products.

They appear to have greater exposure to the junior mezzanine tranches (with attachment points of 3–6 percent and 6–9 percent), with the exception of HBOS. The apparent conservatism of HBOS, which is substantially exposed to the senior mezzanine tranche (with attachment points 12–22 percent), could possibly be explained by the fact that it is also the biggest life insurer in the United Kingdom, in addition to being one of the five biggest banks in the country. Barclays also appears to be most exposed to the senior mezzanine tranche.

The empirical evidence suggests that the CRT market does not pose a substantial threat to financial sector stability in the United Kingdom, at this point. While our sample of financial institutions is admittedly rather small, thus making it difficult to generalize this finding, the results suggest that: (i) there are sufficiently diverse holdings across major institutions in the U.K. market, which are potentially active in the CRT market, to limit the extent of any impact if markets were to experience a negative shock; and (ii) insurance companies—at In this model, the shocks are not orthogonalized. This means that the variance decomposition ranks the importance of every shock, but does not represent the actual percentage that each shock contributes to a particular share price, since the shocks may be correlated. In other words, the sum of the individual variances would not be equal to the total variance because of the covariance terms, but the rankings hold since it is equivalent to a renormalization.

- 13 least the major ones—which are risk buyers, appear to prefer the tranches that better insulate them from first losses.22 In our view, an important threat posed by a credit event in the credit derivatives market is that of reputation risk, which could result in contagion. In other words, the failure of one financial institution could have the knock-on effect of denting public confidence in the financial sector in general, especially given the increasing interlinkages among different segments of the financial sector. In the United Kingdom, inter-relationships between the banking and insurance sectors are especially significant, as discussed earlier. In the current environment, where the market is rapidly evolving and credit yields remain relatively low, institutional investors may be tempted to take riskier bets and move down the credit spectrum to increase the returns on their investments. It is thus important for authorities to continue monitoring developments in these markets and to obtain more detailed information on the exposure of institutions—identified by supervisors as being potentially systemic—to CRT products.

The data are subsequently divided into two subperiods—August 28, 2003 to September 6, 2004, and September 7, 2004 to September 15, 2005—and the VAR approach is applied to each sub-sample. The results suggest that the major U.K. financial institutions have become more conservative in their involvement in credit derivatives over time. These institutions became more exposed to volatility in the more senior mezzanine tranches over the two subperiods, with the exception of RBS. The holdings across institutions remained diverse over time.

Table 1. Impact of Volatility in the CRT Market on Major U.

K. Financial Groups, August 2003–September 2005 (In percent)

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