The pricing of subprime mortgage risk in good times and bad: evidence from the ABX.HE indices

Abstract

This article investigates the pricing of subprime mortgage risk using data for the ABX.HE indices, which have become a key barometer of market conditions during the recent financial crisis. After a discussion of ABX index mechanics and observed pricing patterns, we use regression analysis to establish the relationship between observed index returns and macroeconomic news as well as market-based proxies of various pricing factors. The results imply that declining risk appetite and heightened concerns about market illiquidity–likely due in part to significant short positioning–have provided a sizeable contribution to the observed collapse in ABX prices. In particular, while fundamental factors, such as housing market activity, have continued to exert an important influence on the subordinated indices, those backed by senior exposures have tended to react more to the general deterioration of the financial market environment. This provides further support for the inappropriateness of pricing models that do not account sufficiently for factors such as risk appetite and liquidity risk, particularly in periods of stress. In addition, as related risk premia can be captured by unconstrained investors, these findings lend support to government measures aimed at taking troubled assets off banks’ balance sheets (e.g. the Troubled Asset Relief Program).

Acknowledgements

The first version of this article was finalized in September 2008. Comments by Patrick McGuire, Nikola Tarashev, Haibin Zhu and by seminar participants at the BIS, ECB, the Joint Bundesbank-CEPR-CFS conference on Risk Transfer: Challenges for Financial Institutions and Markets and the 28th SUERF colloquium are gratefully acknowledged. The authors would also like to thank Jhuvesh Sobrun and Emir Emiray for expert help with the data. The views expressed in this article remain those of the authors and do not necessarily reflect those of the BIS or the ECB. The usual disclaimer regarding errors and omissions applies.

Notes

¹ Gorton (2008) argues that the introduction of the ABX indices was instrumental in actually starting the price adjustment in subprime mortgage markets (and subsequent crisis), as hitherto unknown information about the value of these mortgages (i.e. information that had been lost or clouded in the securitization process) was revealed.

² According to the Wall Street Journal (2007), when Swiss bank UBS wrote down its subprime-mortgage investments by $10 billion in December 2007, it looked to the ABX as a guidepost in determining values for its holdings. Likewise, Morgan Stanley and Citigroup reportedly cited the ABX as a factor in the sizeable writedowns announced in late 2007. Gorton (2008), in turn, claims that accountants initially seized on the ABX indices as the ‘price’, even for earlier vintages, of mortgage securitizations, which may have led to feedback effects by triggering repeated rounds of sales, markdowns and further sales.

³ Instead, on 10 September 2008, Markit announced the launch of a new ABX.HE 05-2 index series, to be based on qualifying MBS deals issued in the first half of 2005.

⁴ Ashcraft and Schuermann (2008) offer a detailed description of the subprime US mortgage market and of the factors contributing to its performance over time. See also Kiff and Mills (2007).

⁵ Overall, the structure of the ABX indices of subprime mortgage-based CDS shows a number of similarities with the iTraxx and CDX credit index families, which are based on baskets of corporate CDS. Some of the more important differences include the securitized nature of the ABX underlyings and the fact that there can be multiple credit events whereas corporate CDS contracts tend to terminate after one single event.

⁶ Supplementary indices, called ABX.HE.PENAAA, were introduced in May 2008 to provide additional pricing information for all four existing index vintages.

⁷ FICO scores measure the credit risk of individual borrowers based on a statistical analysis of their credit files. FICO scores range between 300 and 850, and subprime loans are often defined as those to borrowers with limited income and/or a score of 620 or below. See Frankel (2006) for details.

⁸ A requirement like that should provide a degree of protection against possible ‘ratings shopping’. See Fender and Kiff (2005).

⁹ See, for example, Lehman Brothers (2006). The ABX indices have typically referenced collateral from more than 15 originators and serviced by a similar number of master servicers.

¹⁰ See, for example, The Economist Magazine (2008) and Wall Street Journal (2007).

¹¹ Note, however, that simple metrics such as FICO scores and LTVs can be ‘gamed’ and that there is evidence that underwriting quality erosion occurred primarily in the ‘soft’ data that was less readily available to investors in securitized pools (and the ABX). See Anderson et al. (2008) and Keys et al. (2008); Gorton (2008) offers an opposing view.

¹² See Box 1 in Fender and Hoerdahl (2008) for details.

¹³ A second fixed leg may be paid to reimburse the protection seller for reversed writedowns and interest rate shortfalls, but is irrelevant for our purposes here and thus ignored through the remainder of this article.

¹⁴ The 2006-1 AAA index is quoted with a coupon of 18 basis points, whereas the corresponding BBB− index has a coupon of 267 basis points. The respective coupons for the 2006-2 vintage are 11 basis points at the AAA and 242 basis points at the BBB− level.

¹⁵ See, for example, Lehman Brothers (2005).

¹⁶ See Markit (2007).

¹⁷ See UBS (2007); calculation of writedowns requires deal-level knowledge about the effective attachment and detachment points of the various tranches of ABX constituent deals, which will depend on the amount of overcollateralization and accumulated excess spread.

¹⁸ See Box 1 in Fender and Hoerdahl (2007).

¹⁹ See UBS (2008) for details.

²⁰ It took until June 2008 for the first ABX index, the 06-2 BBB−, to actually suffer its first principal writedown event (an amount of 1.278 cents per dollar traded); further writedowns on the lower-rated ABX 06-2 and ABX 07-1 indices followed in July and in subsequent months.

²¹ Sensitivities for assets and liabilities in a HEL MBS transaction will be different in that interest payments on liabilities will tend to reset faster. Abstracting from any hedges that may be in place, declining interest rates will thus translate into increasing ‘excess spread’ earned on the assets relative to what is paid out on the liabilities. Excess spread, in turn, offers additional protection for investors. See UBS (2007).

²² See Demyanyk and van Hemert (2008) who use logit regressions to find that the quality of subprime loans deteriorated for six consecutive years before the crisis, with the decline masked by high house price appreciation between 2003 and 2005. Similarly, Anderson et al. (2008), employing a hazard rate model to decompose foreclosure rates for subprime mortgages, attribute foreclosures about equally to underwriting quality and economic conditions.

²³ See chapter VI in BIS (2008) for a description of market developments during the onset of the financial crisis.

²⁴ Asset pricing theory suggests that current prices fully reflect the publicly available information about the state of the economy. Therefore, it is not the published level of a macroeconomic variable that affects the prices of securities or derivatives, but the unexpected component of the new information.

²⁵ Nonfarm payrolls are known to be the single most important macroeconomic news release in the US, with well documented effects for a variety of financial assets; see Fleming and Remolona (1997). The other variables are suggested by authors such as Calomiris et al. (2008), who employ a panel VAR model to investigate the interaction of foreclosure rates, house prices and other economic variables. They find that employment shocks explain some 7–9% of the forecast variance of foreclosure rates at horizons of 8 and 20 quarters. Similar effects are found for (existing) home sales and building permits, whereas shocks to house price growth explain some 25% of the 20-quarter forecast variance of foreclosure rates.

²⁶ The resulting downgrade counts, aggregated into vintage-specific indices covering all five rating categories (RAT061 and RAT062) and an overall index (RAT06X), identify 48 days with downgrades on at least one underlying instrument over the period through end-June 2008. The maximum count for the 06-1 and 06-2 vintages is 14 and 51 downgrades/day, respectively, on 8 April 2008 and 30 January 2008. With 100 MBS bonds referenced by each individual ABX vintage, individual readings of our ratings indices can be interpreted as the percentage number of underlying bonds downgraded.

²⁷ The source for both sets of data is JP Morgan Chase, which allows us to back out the index- and vintage-specific duration assumptions used in the calculation of JP Morgan's implied spreads (See section on pricing mechanics above).

²⁸ Part of the observed Libor movements is going to reflect changes in counterparty credit and liquidity premia; see the section on risk appetite and liquidity below.

²⁹ See Huang and Neftci (2003) for details on the importance of liquidity premia in swap spreads.

³⁰ An alternative setup using feasible GLS with cross-sectional fixed effects was run to check our results for robustness and generated broadly similar results with regard to the size and significance of the various coefficients.

³¹ First order differences can be interpreted as pure surprises in a random walk model (along with the surprise components of economic data announcements), which justifies regression setups without lagged variables.

³² This setup extends Fender and Scheicher (2008), who estimate a very similar model based only on ABX 06-1 returns.

³³ A key empirical finding from the ratings literature is that spread changes tend to anticipate negative rating announcements, especially when extreme deterioration in credit quality materializes within a short time period; see Hull et al. (2004). Nevertheless, negative rating events (i.e. downgrades and announcements of reviews for possible downgrade) are generally found to give rise to statistically significant contemporaneous price or spread movements. However, the changes are often economically insignificant and much smaller than would be suggested by the magnitude of the rating change itself; see Cantor (2004).

³⁴ Across CDS index tranches, Scheicher (2008) finds R ² values ranging from 0.11 (most senior tranche) to 0.55 (mezzanine tranche).

³⁵ Alternatively, the regression setup may be inappropriately specified.

³⁶ This is in line with Danis and Pennington-Cross (2005), who examine the performance of subprime mortgage loans on the basis of a set of logit models to find, among other things, that changes in interest rates affect prepayments, defaults and delinquencies. Changes in interest rates, therefore, are going to convey information about subprime mortgage risk that goes beyond the technical factors mentioned earlier.

³⁷ Under IO pricing, high upfront payments result in contractual patterns akin to loans extended by the protection buyer to the protection seller with uncertain amortization profiles.