S. Model

The model uses a system of fi ve variables to summarize key linkages, across various U.S.

fi nancial markets, acting as proxies for overall market liquidity, funding liquidity, default risk, and attitudes toward risks. While the shocks orig- inated in the subprime mortgage market, they were readily transmitted to the ABCP market.

The turbulence in ABCP refl ects the funding illiquidity experienced by SIVs and conduits resulting from concerns about the increasing market illiquidity risk of the underlying struc- tured credit securities as they became diffi cult to value. The ABCP link is measured by the spread between three-month ABCP rates and U.S. Trea- sury bill yields (Figure 3.3).

As the problems with SIVs and conduit facilities spread, banks came under increasing pressure to fund those that they had sponsored. Uncertainties with respect to the magnitude of the problem for individual institutions and treasurers’ concerns about future funding needs were quickly refl ected in unsecured longer-term funding markets. As a proxy of these funding liquidity pressures, the sec- ond variable examined in the system is the spread between the three-month U.S. interbank LIBOR rate and the overnight index swap (Figure 3.3).

As turbulence in markets heightened, fi nan- cial markets more generally showed signs of stress. Volatility increased, refl ecting higher uncertainty, and many investors shifted their positions to the safest and most liquid asset classes.²⁶ As such, the third variable, the vari-

26Market participants often equate an increase in mar- ket volatility with a diminished risk appetite of investors.

ance of returns in the S&P 500 stock market index, proxies market volatility and uncertainty (Figure 3.4).²⁷ The fourth variable is the spread between the fi ve-year on-the-run and off-the-run U.S. Treasury notes as a measure of overall mar- ket liquidity pressures (Figure 3.5).²⁸ Finally, the cost of insurance against default—credit default swap spreads—of several representative large complex fi nancial institutions is used to proxy the default risk of fi nancial institutions.²⁹ It also increased sharply during the crisis (Figure 3.4).

In sum, fi ve variables in the system are assumed to capture the key links that created systemic risks in fi nancial markets:³⁰

27Market volatility is often proxied by the Chicago Board of Options Exchange Volatility Index (VIX), which measures the implied volatility priced into S&P 500 equity index options. This variable was not chosen because the model used to estimate the transmission, a multivariate GARCH model, is based on a volatility estimate, and so using VIX would represent examining the volatility of a volatility measure.

28The “on-the-run” Treasury note is usually the most recently issued of a particularly liquid maturity and is used for pricing other assets. An on-the-run note becomes “off-the-run” when a new note is issued in that maturity bracket. Other alternative measures of over- all market liquidity were also examined, including the spread between the 10-year and the two-year on-the-run and off-the-run U.S. Treasury securities, and the spread between the 10-year U.S. Treasury bond and other less liquid maturities. Overall, the fi ndings were broadly in line with the fi ve-year, on-the-run spread. Fleming (2003) notes that the various measures are imperfect proxies of U.S. Treasury market liquidity, but that the fi ve-year and the two-year note spreads showed the biggest increase during the 1998 Long-Term Capital Management crisis in response to a desire for investors to move to the most liquid assets. The high demand for two- and fi ve-year Treasury notes for potential repurchases suggests this variable may capture some funding liquidity as well as market liquidity.

29This variable was created by taking the unweighted daily average of the fi ve-year credit default swaps (cont.) for the following institutions:Morgan Stanley, Merrill Lynch, Goldman Sachs, Lehman Brothers, JPMorgan, Deutsche Bank, Bank of America, Citigroup, Barclays, Credit Suisse, UBS, and Bear Stearns.

30The data examined in this model clearly constitute a simplifi cation of the dynamics that may occur during periods of stress. For example, in practice, the widening of the ABCP and LIBOR-OIS spreads could also poten- tially refl ect an unobserved component that represents changes in the perceived credit risk of the collateral backing ABCP, and in the perceived credit risk of banks.

abcp = ABCP—U.S. Treasury bill (ABCP fund- ing liquidity)

LIBOR = LIBOR—OIS (bank funding liquidity) five = fi ve-year on-the-run vs. off-the-run

U.S. Treasury notes (market liquidity) ret = S&P 500 returns (volatility)

CDS = credit default swap spreads for fi nan- cials (solvency risk).

Empirical Results

The data sample begins on January 3, 2006 and ends on December 24, 2007, although the fi nal week of 2007 is omitted in order to avoid end-of-year effects in the strained interbank money markets.³¹ The model was estimated using a DCC GARCH specifi cation by Engle (2002), which allows the conditional variance and covariance to be time-varying risk measures.³² By directly parameterizing the conditional correla- tions and accounting for their potential time variation, this model is best interpreted as a gen- eralization of the Constant Conditional Correla- tion specifi cation suggested by Bollerslev (1990).

The DCC GARCH model is estimated using fi rst differenced data, as augmented Dickey- Fuller tests indicate that the ABCP, LIBOR and CDS spreads exhibit nonstationarity during the second half of 2007.

Prior to July 2007, there is evidence of only limited implied correlations between the vari- ables in the system. During the crisis period,

Similarly, CDS prices and the credit premia implicit in LIBOR rates may also partly refl ect additional compensa- tion for market participants’ risk appetite and overall uncertainty in the markets. Disentangling these compo- nents is diffi cult, since they are nonobservable and can be time-varying. Michaud and Upper (2008) fi nd that credit risk measures have little explanatory power for the day-to- day fl uctuations in the LIBOR-OIS spread. However, the Bank of England (2007) notes that credit concerns since October 2007 appear to account for a more signifi cant portion of LIBOR spreads.

31The estimation was also conducted from 2003 onward, and the fi ndings did not change appreciably, indicating that the low volatility in U.S. markets during 2006 does not bias the results.

32This model avoids the criticism that tests of contagion are biased when the heteroscedasticity of the returns is not modeled explicitly (Forbes and Rigobon, 2002).

correlations become more important and their magnitudes increase sharply. In particular, there is a more pronounced interaction between market and funding liquidity (Figure 3.13). In addition, solvency considerations, measured in terms of CDS spreads, also become signifi cant, whereas they were relatively unconnected to the other variables before the subprime crisis, suggesting that concerns about solvency arose as liquidity diffi culties increased, and indicating funding illiquidity as a source of diffi culty. As the representative set of graphs in Figure 3.13 illustrates, a clear break in the time-varying cor- relation structure of the variables is observed at the end of July 2007, consistent with the onset of the fi nancial turbulence.

Advanced Economies Model

As described above, the initial U.S. subprime mortgage shock also affected fi nancial interme- diaries abroad, many of whom funded struc- tured securities with ABCP. This was most clearly the case for Canadian fi nancial intermediaries, but also for many entities in Europe. As such, a multivariate DCC GARCH model is used to examine the spillovers between U.S. and inter- national money markets.

Three potential links are examined. The fi rst one refl ects the potential link between U.S.

funding liquidity pressures, proxied by the three- month U.S. LIBOR rate over the OIS rate, and funding pressures in Canada, the euro area, and the United Kingdom, proxied by three-month LIBOR rates relative to the overnight index swaps in each zone. The second link addresses the potential relationship between U.S. ABCP and international interbank spreads. Finally, the third potential spillover is captured by the overall U.S.

market liquidity measure, proxied by the spread between the fi ve-year, on-the-run versus the fi ve- year, off-the-run U.S. Treasury notes, and the LIBOR spreads in the various countries.

Empirical Results

The correlations between the U.S. funding liquidity measures (ABCP and LIBOR spread)

ANNEX 3.1. LIQUIDITY DYNAMICS SINCE SUMMER 2007

and the international LIBOR spreads in Canada, the euro area, and the United Kingdom, are all of relative small magnitude and fairly stable before the subprime crisis. Figure 3.14 shows some of the time-varying conditional correlations, for instance, between the U.S. LIBOR spread and those in the United Kingdom and euro area, as well as between the U.S. ABCP spread and the LIBOR spreads in the United Kingdom and Canada.

During the crisis period, the correlations between U.S. funding liquidity and international LIBOR spreads increase sharply. The elevated implied correlation between the U.S. ABCP spread and the Canadian LIBOR spread during the crisis period possibly refl ects the transmis- sion of the U.S. subprime mortgage and ABCP shock to Canadian fi nancial institutions (Fig- ure 3.14).The correlation between international spreads and U.S. LIBOR is more pronounced than the correlation between international spreads and the U.S. Treasury fi ve-year, on- the-run spread. Overall, this is consistent with market participants’ views that funding, more than market illiquidity, has been the important mode of transmission of shocks across countries.

Finally, unlike the pre-crisis period, when there were limited interactions between the Cana- dian, euro area, and UK LIBOR spreads, those interactions become more important during the crisis period.

Emerging Markets Model

The international model for advanced econo- mies discussed above is extended to include some key emerging markets.³³ Specifi cally, two measures of U.S. funding liquidity (one for the interbank money market and the other for funding liquidity in the ABCP market), as well as the fi ve-year, on-the-run spread measure of U.S.

market liquidity, are linked to the bond spreads and stock market returns in Brazil, Mexico, and Russia. This is done across each of the two asset

33The computational demands of the multivariate DCC GARCH model restricts the number of countries that can be examined.

–0.05 0 0.05 0.10 0.15 0.20

–0.10 –0.05 0 0.05 0.10 0.15 0.20 0.25 0.30

–0.05 0 0.05 0.10 0.15 0.20

–0.35 –0.30 –0.25 –0.20 –0.15 –0.10

Sources: Bloomberg L.P.; Datastream; and IMF staff estimates.

Note: The horizontal lines represent the arithmetic average of the correlations before and after the break in late July 2007.

1Spread between yields on 90-day U.S. asset-backed commercial paper (ABCP) and on three-month U.S. Treasury bills.

2The unweighted daily average of the five-year credit default swaps (CDS) for the following institutions: Morgan Stanley, Merrill Lynch, Goldman Sachs, Lehman Brothers, JPMorgan, Deutsche Bank, Bank of America, Citigroup, Barclays, Credit Suisse, UBS, and Bear Stearns.

3Spread between yields on three-month U.S. LIBOR and on the three-month U.S.

overnight index swap.

4Spread between yields on five-year off-the-run and on-the-run U.S. Treasury notes.

5S&P 500 stock market returns.

Figure 3.13. U.S. Model: Implied Correlations from Dynamic Conditional Correlation GARCH Specification

Correlation (ABCP,¹ CDS²)

Correlation (LIBOR,³ CDS)

Correlation (LIBOR, Five⁴)

Correlation (Returns,⁵ CDS)

2006 2007 2006 2007

2006 2007

2006 2007

classes across countries in order to capture any potential differences between them. The sover- eign bond spreads are measured by JPMorgan’s Emerging Market Bond Index Plus (EMBI+) for each country, and the stock market returns are calculated from the respective local stock market indices in domestic currency.

Empirical Results

During the subprime crisis, a heightened interaction between the U.S. funding liquidity measures and the stock markets is evident for all three markets (Figure 3.15).³⁴ Correlation changes are most pronounced between the U.S.

LIBOR spreads and the Mexican stock market returns, as well as the U.S. ABCP spreads and the Mexican and Russian bond spreads. Simi- larly, it is found that the time-varying correlation between U.S. funding liquidity and the bond spreads in Brazil, Mexico, and Russia rises. It is also noteworthy that, for both the stock market and bond spreads models, the correlation mag- nitudes among the emerging countries exam- ined here are higher than with the U.S. funding liquidity measures during the sample period.

Co-movements among emerging countries have increased in recent years and became more pro- nounced during crises. The fi ndings from intro- ducing the U.S. market liquidity variable, the fi ve-year, on-the-run spread, were inconclusive.³⁵

European Central Bank and Fed Interventions To assess the impact of the emergency response of central banks, two methods were used.³⁶ First, a univariate GARCH model is estimated for both the U.S. Fed and the ECB for a sample period ranging from July 26 to Decem-

34The other period of recent increased correlation between the various measures of U.S. funding and market liquidity shocks and emerging markets examined occurs on February 28, 2007, following a sharp correction in China’s Shanghai stock market.

35Similarly inconclusive results were obtained from the U.S. two-year and 10-year on-the-run spreads.

36See also Frank, Hesse, and Klueh (forthcoming).

0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0 0.1 0.2 0.3 0.4 0.5

0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0 0.1 0.2 0.3 0.4 0.5 0.6

–0.10 –0.05 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35

Figure 3.14. Advanced Economies Model: Implied Correlations from Dynamic Conditional Correlation GARCH Specification

Correlation (ABCP,¹ UK²)

Correlation (ABCP, Canada³)

2006

Sources: Bloomberg L.P.; Datastream; and IMF staff estimates.

Note: The horizontal lines represent the arithmetic average of the correlations before and after the break in late July 2007.

1Spread between yields on 90-day U.S. asset-backed commercial paper (ABCP) and three-month U.S. Treasury bills.

2Spread between yields on three-month pound sterling LIBOR and the UK three-month overnight index swap.

3Spread between yields on three-month Canadian dollar LIBOR and the Canadian dollar three-month overnight index swap.

4Spread between yields on three-month U.S. dollar LIBOR and the three-month U.S.

overnight index swap.

5Spread between yields on three-month euro LIBOR and the euro area three-month overnight index swap.

6Spread between yields on five-year off-the-run and on-the-run U.S. Treasury notes.

–0.15 –0.10 –0.05 0 0.05 0.10 0.15 Correlation

(LIBOR, euro area⁵) Correlation

(LIBOR,⁴ UK)

Correlation (Euro area, Canada)

Correlation (Five,⁶ Canada)

2007 2006 2007

2006 2007

2006 2007

2006 2007 2006 2007

ANNEX 3.1. LIQUIDITY DYNAMICS SINCE SUMMER 2007

ber 24, 2007.³⁷ Second, a three-state Markov regime-switching model (Hamilton and Susmel, 1994) for the volatility of term spreads is imple- mented, and is used to compare regime transi- tions with central bank intervention dates.³⁸

To proxy the amount of “extra” liquidity injections used as intervention variables in the GARCH model, differences in operational frameworks among central banks have to be taken into account. Conceptually, the measures should aim at capturing injections over and above the neutral level needed to just fulfi ll reserve requirements. Also important are opera- tions that provide extraordinary liquidity to deal with market stress and, more broadly, the surprise element of a particular intervention.

For the ECB, we fi rst employ a variable quan- tifying liquidity injections through longer-term refi nancing operations (LTROs) that had been carried out in addition to those implemented regularly on a monthly schedule. Second, for main refi nancing operations (MROs), a variable based on the MRO allotment exceeding the ECB’s benchmark allotment is used.³⁹

For the Fed, we use the difference between actual repurchase agreements outstanding and estimates of the amount of repurchase agree- ments that would have been necessary to achieve

37A GARCH framework is used to disentangle level and volatility effects of LIBOR spreads, as both can have an impact on fi nancial institutions’ funding conditions.

At the same time, it is worth mentioning that the results are only indicative, as our approach does not take into account that intervention amounts themselves are likely to be determined endogenously. In particular, central banks might react to an expected increase in money mar- ket stress by raising their respective interventions, which then occur jointly with a potentially strong increase in the observed spreads.

38To capture the fact that during a crisis central bank operations may encompass multiple objectives (steering very short-term interest rates and supporting the smooth functioning of markets more broadly), a range of depen- dent variables were considered, all based on changes in the spread between LIBOR and overnight interest rate swaps for different maturities and currencies.

39The benchmark allotment is the ECB’s projection of the liquidity provision needed to smoothly fulfi ll reserve requirements.

0.1 0.2 0.3 0.4 0.5

–0.4 –0.3 –0.2 –0.1 0 0.1

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

–0.25 –0.20 –0.15 –0.10 –0.05 0

0.50 0.55 0.60 0.65 0.70 0.75 0.80

Figure 3.15. Emerging Markets Model: Implied Correlations from Dynamic Conditional Correlation GARCH Specification

Correlation

(LIBOR,¹ Mexico: bond spread²)

Correlation

(ABCP³, Russia: bond spread⁴)

Correlation

(ABCP, Brazil: stock market return)

Correlation (ABCP, Russia:

stock market return) Correlation

(Brazil: bond spread,⁵ Russia: bond spread)

Correlation

(LIBOR, Mexico: stock market return)

Bond Market Stock Market

2006 2007

Sources: Bloomberg L.P.; Datastream; and IMF staff estimates.

Note: The horizontal lines represent the arithmetic average of the correlations before and after the break in late July 2007.

1Spread between yields on three-month U.S. dollar LIBOR and on the three-month U.S. dollar overnight index swap.

2JPMorgan’s EMBI+ Mexico sovereign spread.

3Spread between yields on 90-day U.S. asset-backed commercial paper (ABCP) and on three-month U.S. Treasury bills.

4JPMorgan’s EMBI+ Russia sovereign spread.

5JPMorgan’s EMBI+ Brazil sovereign spread.

–0.25 –0.20 –0.15 –0.10 –0.05 0 0.05

2006 2007

2006 2007 2006 2007

2006 2007 2006 2007

0.05

0.10

neutrality with respect to fulfi lling banks’ needs over a reserve maintenance period.⁴⁰

The explanatory variables only proxy certain aspects of the responses of the ECB and the Fed. For example, the GARCH model cannot be expected to fully capture changes in the ECB’s broader strategy of communication and liquid- ity provision through the maintenance period, factors that have been perceived to have contrib- uted to the leveling off of euro LIBOR spreads between August and November 2007. Similarly, for the Fed, the choice of approach and sample period implies that the GARCH estimation takes into account neither TAF operations nor other operational adjustments. To address these short- comings, the results from the GARCH model are complemented with a more heuristic approach based on a Markov regime-switching Autoregres- sive Conditional Heteroskedasticity (ARCH) specifi cation. This model is used to determine the probability of being in a low-, medium-, or high-volatility state.⁴¹ Changes in these prob- abilities are then compared with those of major central bank announcements or interventions.

Figures 3.11 and 3.12 and Table 3.1 summa- rize the results. As explained in the main text, the fi ndings indicate that additional term lend- ing, the joint central bank response announced on December 12, as well as the actual implemen- tation of the TAF, were instrumental in reducing stress levels, conceptualized as a combination of spread levels and spread volatility.

References

Adrian, Tobias, and Hyun Song Shin, 2007, “Liquidity and Leverage” (New York: Federal Reserve Bank of New York). Available via the Internet: http://www.

ny.frb.org/research/economists/adrian/Liquidity Leverage25Sep2007.pdf.

Bank of England, 2007, “Markets and Operations,”

Bank of England Quarterly Bulletin - Q4, Vol. 47, No. 4.

40The estimates were provided by Wrightson ICAP.

41Smoothed probabilities exceeding 0.5 indicate that the data-generating process is in that respective volatility regime.

Basel Committee on Banking Supervision (BCBS), 1992, “A Framework for Measuring and Managing Liquidity” (Basel: Bank for International Settle- ments, September). Available via the Internet:

http://www.bis.org/publ/bcbs10b.pdf.

———, 2000, “Sound Practices for Managing Liquid- ity in Banking Organizations” (Basel, Bank for International Settlements, February). Available via the Internet: http://www.bis.org/publ/bcbs69.pdf.

———, 2006, “Core Principles for Effective Bank- ing Supervision” (Basel, Bank for International Settlements, October). Available via the Internet:

http://www.bis.org/publ/bcbs129.pdf.

———, 2008, “Liquidity Risk: Management and Supervisory Challenges” (Basel, Bank for Inter- national Settlements, February). Available via the Internet: http://www.bis.org/publ/bcbs136.htm.

Bech, Morton L., 2007, “The Federal Home Loan Bank System: The Lender of Next to Last Resort?” New York Federal Reserve Board Conference Presentation, December 13. Avail- able via the Internet: http://www.newyorkfed.

org/research/ conference/2007/liquidity/FHLB_

LiquidityConference.pdf.

Bernardo, Antonio E., and Ivo Welch, 2004, “Liquid- ity and Financial Market Runs,” Quarterly Journal of Economics, Vol. 119 (February), pp. 135–58.

Table 3.1. Impact of Central Bank Interventions on LIBOR-OIS Spreads

Three-Month LIBOR-OIS Spreads Level Volatility ECB supplementary LTRO (L1) –0.54 –12.27***

ECB supplementary LTRO (L2) 0.10 –8.66*

Fed repurchase agreements exceeding neutral

–0.16** –2.69***

Fed repurchase agreements exceeding neutral (L1)

0.33*** 1.18 Fed repurchase agreements

exceeding neutral (L2)

0.06 –0.01

Source: IMF staff estimates.

Note: The table reports coefficient signs of intervention variables from a GARCH specification. *** indicates significance at the 1 percent level; **(*) indicates significance at the 5 (10) percent level. The model is computed using Bollerslev-Wooldridge robust standard errors. L1 and L2 refer to lag lengths. The sample is from July 26 to December 24, 2007. OIS = overnight index swap; ECB = European Central Bank; LTRO = long-term refinancing operation.

REFERENCES

Im Dokument Global Financial Stability Report Global Financial Stability Report (Seite 127-142)