The Fragility of Discretionary Liquidity Provision--Lessons from the Collapse of the Auction Rate Securities Market

Figure 1: Gross Issuance of Municipal Auction Rate Securities.

Figure 1: Gross Issuance of Municipal Auction Rate Securities. The bar chart plots quarterly gross issuance of MARS, in billion dollars, from 1996 to 2008 (left scale). The solid line shows the time series of municipal bond term spread, the difference between long-term and short-term municipal bond yields, in percent (right scale). Issuance peaked between 2002 and 2004, when municipal bond term spread was the widest. Issuance stopped completely in 2008 despite a relatively high term spread.

Figure 2: The Auction Procedure.

Figure 2: The Auction Procedure. This figure illustrates the auction process. Sell orders submitted by current bondholders and buy orders from potential investors are aggregated by broker-dealers, who send these orders, together with their own discretionary orders to a uniform price auction. The auction is used to set the interest rate for the period before the next auction and to transfer bond ownership from current bondholders to potential investors. Note that auctions take place in the secondary market.

Figure 3: Distribution of Maximum Auction Rate by Maximum-Rate Rule.

Figure 3: Distribution of Maximum Auction Rate by Maximum-Rate Rule. This figure plots the distribution of maximum auction rates for MARS with floating maximum-rate rules (left panel) and with fixed maximum-rate rules (right panel). The maximum interest rates for bonds with floating rules are continuously distributed and centered at 7 percent. The maximum interest rates for bonds with fixed rules are clustered at rounded percentages that are greater than 10 percent.

Figure 4: Gross and Net Buys by Auction Dealers in the MARS Auctions.

Figure 4: Gross and Net Buys by Auction Dealers in the MARS Auctions. This figure shows the time series of average gross and net dealer buys per auction. Gross dealer buys equal total customer sells to dealers reported in the MSRB data on the auction dates, and net dealer buys equal total customer sells to dealers minus total customer buys from dealers reported in the MSRB data on the auction dates. Net dealer buys were positive prior to 2008 and peaked on Feb 12, 2008. Almost immediately afterwards, net dealer buys dropped to about zero. This finding suggests that dealers had been liquidity suppliers in the market but withdrew their support in mid-February, 2008.

Figure 5: Extent of Dealer Support in Successful Auctions in the Pre-Crisis Period.

Figure 5: Extent of Dealer Support in Successful Auctions in the Pre-Crisis Period. This figure shows the distribution of the ratio of net dealer buys to gross dealer buys, conditional on non-zero gross dealer buys, among successful auctions in the pre-crisis period between July 1, 2007 and December 31, 2007. In particular, a ratio of one corresponds to zero investor bid in the auction. The chart shows that almost one quarter of all auctions have zero investor bid.

Figure 6: Auction Dealers as Market Makers.

Figure 6: Auction Dealers as Market Makers. This figure shows average values of net dealer buys (in million dollars) per auction on auction dates and in the inter-auction periods after auctions. Net dealer buys on auction dates were positive prior to February 12, 2008. Net dealer buys post auctions were negative and roughly mirrored the movements in net dealer buys on auction dates. This finding suggests that dealers were net buyers in auctions and net sellers post auctions. They effectively acted as market makers prior to the collapse of the market on February 12, 2008. Dealer buys went down sharply to near zero after February 12, 2008.

Figure 7: The Rate of Actual Auction Failures by Major Auction Dealers.

Figure 7: The Rate of Actual Auction Failures by Major Auction Dealers. This chart plots the percentage of MARS auctions in our sample that failed for each of the top 10 auction dealers (ranked by market share) in the week of February 11, 2008. On Monday, February 11, 2008, no auctions managed by any dealers in our sample had failed. On February 12, 2008, over 60% of the auctions managed by Goldman Sachs failed. Citigroup also had a small amount of failed auctions. This finding suggests that Goldman Sachs stopped supporting their auctions and Citigroup did so selectively for some auctions. The Next day, on February 13, 2008, auctions managed by almost all other major dealers experienced failures. They continued to have failed auctions since that day.

Figure 8: The Rate of Investor Demand Shortfall.

Figure 8: The Rate of Investor Demand Shortfall. This figure shows the time series of the fraction of auctions with investor demand shortfall (IDS). An IDS occurs when the total amount of buy orders by potential investors is less than the total amount of sell orders by existing bondholders. IDS can be inferred from either actual failure or pseudo failure. Pseudo failure refers to the case when the auction would have failed, had the dealer not placed supporting bids in the auction. Because there was no actual failure before February 12, 2008, the IDS line coincided with the line for pseudo failure during this period, and they hovered between 40 and 80 percent. Actual failure went up on February 12, 2008 and peaked on February 13, 2008, causing IDS to peak around the same time. Actual failure remained high at 50 to 60 percent after mid-February, while pseudo failure gradually went down to less than 20 percent.

Figure 9: The Rate of Investor Demand Shortfall by Maximum Auction Rate.

Figure 9: The Rate of Investor Demand Shortfall by Maximum Auction Rate. This figure shows the time series of the fraction of auctions with investor demand shortfall by maximum auction rates. A maximum rate is classified as high if it is greater than 10 percent, otherwise it is low. The cutoff point, 10 percent, is about the median of maximum auction rates for all ARS in our sample. The time series of IDS for bonds with high and low maximum rates followed each other closely before February 12, 2008. After February 12, 2008, bonds with low maximum rates had IDS as high as 90 to 100 percent. In contrast, bonds with high maximum rates had much lower IDS, which were 80% right after the crisis but came down to around 40% one month after.

Figure 10: Selected Results from Weekly Probit Regressions of Investor Demand Shortfall.

Figure 10: Selected Results from Weekly Probit Regressions of Investor Demand Shortfall. This figure shows the time series of pseudo-$R^2$ and the coefficients of maximum auction rates from the weekly Probit regressions of investor demand shortfall. The empirical model is the same as in Table 4 except that the market condition variables, namely the one-year municipal bond rate, municipal yield term spread, and interest rate volatility, are excluded in the weekly regressions. The coefficients of other independent variables are available upon request. The upper panel shows the time series of pseudo-$R^2$. It stayed low at around 5 percent before climbing up to above 10% on February 12, 2008, and then rose sharply to around 50 percent in mid-March. The lower panel shows the time series of coefficients of maximum rate from Probit regressions. The coefficients were not significantly different from zero before February 12, 2008, but turned sharply negative since then. The coefficients were as low as -0.25 by mid-March 2008. During the period when the coefficients were negative, they were also statistically significant.

Figure 11: The Ratios of Short-Term Municipal Bond Yields to Libor.

Figure 11: The Ratios of Short-Term Municipal Bond Yields to Libor. This figure shows the time series of the ratios of short-term municipal bond rates to one-month Libor. The short-term municipal bond rates include average auction clearing rates on ARS, the SIFMA 7-day swap rate index, and the one-year municipal bond yield from Municipal Market Advisor. Neither the SIFMA 7-day swap rate nor one year municipal yield went up relative to Libor in mid-February, 2008, when ARS rates rose sharply to over 3 times Libor rates. The contrast remained in the month after February 12, 2008. This finding suggests that the sharp increase in ARS rates was not due to the deterioration in municipalities' credit quality, but caused by a sudden change in the liquidity of ARS.

Figure 12: The ARS Clearing Rates by Maximum Auction Rate.

Figure 12: The ARS Clearing Rates by Maximum Auction Rate. This figure shows the time series of ARS auction clearing rates on successful auctions by maximum auction rate. A maximum rate is classified as high if it is greater than 10 percent, otherwise it is low. The cutoff point, 10 percent, is about the median of maximum auction rates for all ARS in our sample. The time series of average clearing rates for bonds with high and low maximum rates followed each other closely at around 4 percent before February 12, 2008. After February 12, 2008, the clearing rates of bonds with high maximum rates went to as high as 10 percent. In contrast, bonds with low maximum rates have much lower auction clearing rates, which were around 4 percent right after the crisis and remained around 5 to 6 percent one month after.

Figure 13: Cumulative Inventory of All Auction Dealers.

Figure 13: Cumulative Inventory of All Auction Dealers. This figure shows the total cumulative changes in all auction dealers' inventory since the beginning of 2007. Daily changes in dealers' inventories equal total "customer sell to dealers" minus total "customer buy from dealers" reported in the MSRB transactions data. During 2007, dealers significantly reduced their inventories when their credit default swap spreads-a measure of their costs of funds-rose sharply. Since the end of 2007 to mid February of 2008, dealers' inventories rose sharply as investors became reluctant to invest in such products and dealers were forced to provide liquidity by buying ARS into their inventories. Starting from February 12 2008, dealers' inventories abruptly stopped rising, suggesting that dealers stopped providing liquidity to the market.