Economic Policy Uncertainty and the Primary Market for U.S ... · can have on a crucial market for...

Economic Policy Uncertainty and the PrimaryMarket for U.S. Treasuries

Alessio Ruzza∗

First version: June 8, 2015This version: April 18, 2017

Abstract

U.S. Treasuries represent a shelter from market risks. However, it is unclearhow economic policy uncertainty affects them. I present a comprehensive analysisof the effects of policy uncertainty on yields, demand and bidders’ behavior.

Economic policy uncertainty increases the yield of Treasuries, hence it is harm-ful for public financing. However, this relation is reversed after October 2010because of the higher activism of the Fed. In uncertain times, the aggregated yield-demand schedule shifts upwards. Hedging investors require a premium for policyuncertainty and this higher yield attracts speculators who aim at higher returns.A unique dataset containing the decomposition of bids makes it possible to iden-tify depository institutions, individuals, and pension funds as hedgers and foreigninvestors as speculators.

Keywords: Treasury bond, Treasury auction, Policy uncertainty, TYVIX, VIX, Feargauge, Bidders’ behavior

JEL Classification: D44, G12, H63∗University of California Berkeley - Haas School of Business, Swiss Finance Institute, and Università

della Svizzera Italiana (USI) Institute of FinanceEmail: [email protected] Phone: +41 58 666 4118I am grateful to my research advisor Prof. Antonio Mele. I benefited from insightful conversationwith Nicholas Bloom, Thierry Foucault, Arvind Krishnamurthy, Kjell G. Nyborg, and Annette Vissing-Jørgensen. I thank the discussant at SFI research days 2015 (Gerzensee) and my colleagues at IFin inLugano for their feedbacks. Finally, I acknowledge the support of the office of Treasury Security Servicesat the Bureau of the Fiscal Service which provided part of the data. All errors are my own responsibility.

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1 Introduction

Bond investors are used to factoring things like economic growth and infla-tion into their models. They have less experience with politics, and thesedays political risk is crucial.

Bloomberg Markets1

The excerpt of a financial article reported above remarks the role that uncertainty aboutpolitical choices can have on fixed income markets. Political uncertainty can be a ne-glected pricing factor for bond markets, and we can see that it is becoming more andmore present in advanced markets such as the Eurozone and the USA.

This paper presents an empirical investigation on the impact that policy uncertaintycan have on a crucial market for fixed income investors: the primary market for Trea-suries. The securities sold in this market are among the most liquid, they constitute abenchmark for fixed income pricing, and they are the essential instruments for collater-alized financing and for the implementation of monetary policy.

The first part of this paper focuses on the pricing of political uncertainty. In generalterms, uncertainty pushes firms to delay investment choices (Gilchrist et al., 2014) be-cause they act as if the worst possible outcome will occur (Bernanke, 1983). Suchinvestment depressing effect propagates to two key determinants for debt servicing,namely economic growth and fiscal revenues (Baldacci and Kumar, 2010). The effectsof a deterioration in fiscal sustainability on yields of sovereign securities are straight-forward for emerging economies: the increased default risk leads to larger spreads andvolatility (Cuadra and Sapriza, 2008). The way uncertainty affects the pricing of pub-lic debt securities issued by developed economies is less clear. These securities areappealing for investors building a conservative portfolio during uncertain periods, how-ever government bonds might be subject to political risk and investors can charge apremium.

This analysis reveals that the pricing of political uncertainty in the primary marketfor Treasuries is changing over time. The economic policy uncertainty contributes tohigher bond yields, but this pricing mechanism reverses after October 2010. The eventsoccurred after 2010 would suggest a clearer separation between government generated

1Retrieved from http://bloom.bg/29faC8e on July 5th, 2016

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uncertainty and economic generated uncertainty; moreover, domestic and foreign un-certainty are easy to distinguish after 2010. Nevertheless, neither market turmoils, norforeign policy uncertainty can explain such inverse pricing.

The second part of the paper analyses the effects of uncertainty on the behaviorof the bidders. On the one hand, investors are likely to decrease their participationin a market struck by uncertainty (Dow and Werlang, 1992); on the other, Treasuriesare the safest asset class (Engle et al., 2012) and investors hedging political risk mayincrease their demand. In presence of uncertainty, investors are also more subject to thechampion’s plague, therefore they will rationally reduce their bid price (Keloharju et al.,2005; Nyborg et al., 2002).

Results show a stable direct relation between policy uncertainty and the demand forTreasuries. This finding is at odds not only with the effect of uncertainty on yields,but also with a decreasing aggressiveness in presence of uncertainty. To justify thispuzzling result, I look at the behavior of different investor categories in terms of bothdemand and aggressiveness. This analysis identifies interesting patterns in the behaviorof some investor classes. Depository institutions and pension funds conduct bid shadingin presence of uncertainty. The possibility of a higher yield attracts a group of oppor-tunistic investors represented by foreigners. Finally, primary dealers are the group ofinvestors determining the change in the pricing of uncertainty: after October 2010 theyincrease their demand and their aggressiveness in presence of uncertainty, hence theypush down the yield. The increased activism of the Federal reserve is likely to be thecause of this behavior.

These results have several implications in terms of asset pricing, of market quality,and of fiscal policy. Uncertainty is a priced factor in this market, but further investigationis needed to assess whether the reverse pricing after 2010 is a real structural break orit is just the consequence of the Fed policy. Uncertainty dampens the signal about thetrue value of Treasuries, this is the cause of the heterogeneous behavior of bidders.Among them, we can notice the vital role of primary dealers which sustain the demandwhen compelled by the central bank. Finally the effect of uncertainty on the cost ofpublic financing are relevant if we consider the amount of securities auctioned by thefederal government. Table 1 provides an example of the difference in the cost of publicfinancing for an event unrelated to policy uncertainty (the rating downgrade of the U.S.)and an event that caused a surge in policy uncertainty (the fiscal cliff dispute).

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The paper proceeds as follows: section 2 provides a brief review of related literature,section 3 describes how the federal borrowing and the primary market for Treasurieswork, section 4 describes the data and the empirical models used in the analysis, sections5 and 6 comment the results for the first part of the sample, section 7 investigates on thepossible causes of the inverse pricing, and section 8 provides concluding remarks.

2 Literature review

The link between policy uncertainty and public financing is related to several topics inthe economic literature. Country risk and choices under uncertainty are issues that havebeen tackled since the 1980’s. Applications of these topics to sovereign bond pricingand the focus on policy uncertainty are more recent. An extensive strand of literature isdeveloping around the latter topic.

2.1 Country risk and the pricing of sovereign bonds

Creditors of an insolvent country cannot seize its assets. Hence, traditional corporatefinance concepts, such as solvency and liquidity, cannot explain country risk (Eaton etal., 1986).

The enforcement of debt obligation is an issue related to emerging markets; whenconsidering advanced economies, debt repudiation only plays a marginal role. The de-terminant of country risk for these economies is moral hazard. That is, the difficultyto ensure that the policy of the debtor country does not harm prospects for debt servic-ing. Reinhart and Rogoff, (2009) argue that the decision to honor debt obligations isbased on political choices rather than funds availability. In their view, policy is the keydeterminant of country risk.

One of the first studies linking policy uncertainty to the cost of debt is that of Alesinaand Tabellini, (1989). In their model, uncertainty about future governments generateseconomic uncertainty regarding future policy choices. Investors will demand a premiumto hold government debt because it is subject to political risk.

Recent empirical literature about sovereign yields mostly deals with emerging mar-kets and devote attention to sentiment and risk appetite rather than macro factors. McGuireand Schrijvers, (2003) analyze debt issued by 15 emerging countries and find that a

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common factor, related to investors’ risk appetite, explains most of the covariation inspreads. Eichengreen and Mody, (1998) argue that sentiment is the main force whichmoves market spreads over a short period of time (at least for emerging markets). Theirconclusion is that observable characteristics (fundamentals) "do not provide adequateexplanation for changes over time in the value of new bond issues and launch spreads".

Only very recent papers tackle the issue of pricing sovereign debt of advancedeconomies: the motivation of this research is the deterioration of fiscal efficiency. Angand Longstaff, (2013) find that sovereign risk is not directly caused by economic fun-damentals. It is rather determined by financial markets through capital flows, fundingavailability, risk premia, and liquidity shocks.

D’Agostino and Ehrmann, (2013) perform a long-run analysis of sovereign bondsissued by G7 countries and report "considerable time variation in the pricing of risk".These fluctuations in risk appetite led, for example, to an overpricing of risk during theEuropean sovereign debt crisis.

2.2 Uncertainty in policy choices

Uncertainty in policy choices can spread to the markets both in the Knightian sense, andin form of risk. This section reviews the literature related to these two topics.

Agents facing uncertainty turn pessimistic in their decision-making process. Whenmaking investment decisions, they act as if they were playing a game against a malev-olent nature which is drawing from multiple payoff functions (Gilboa and Marinacci,2013). In this framework, agents make decisions according to the max-min criterion(Gilboa and Schmeidler, 1989).

Uncertainty also affects market participation. According to Dow and Werlang,(1992), in presence of uncertainty about the value of the asset, investors refrain fromtrading. Additionally, Cao et al., (2005) identify an uncertainty premium in asset re-turns.

Policy uncertainty is linked to an increased risk premium (Pástor and Veronesi,2013) and to a negative price reaction in the stock market (Pástor and Veronesi, 2012).Empirical evidence suggests that economic policy uncertainty is an important risk factorfor equities (Brogaard and Detzel, 2015).

Croce et al., (2012) study the effect of fiscal policy uncertainty on economic growth.

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They find that in presence of uncertainty and pessimistic agents, even counter-cyclicalfiscal policies increase long-run risk and depress growth. This harmful effect on growthis likely to propagate to sovereign risk.

Studies about emerging economies deal explicitly with the link between policy un-certainty and public financing issues. In this kind of economy, a link between politicalrisk and debt repudiation is quite straightforward.

In a theoretical model, Amador, (2003) links uncertainty in political choices to theability of a country to save, and therefore honor its debt. He argues that political uncer-tainty is likely to be associated with inefficient fiscal behavior: governments have a lessefficient tax system and more problems controlling spending.

This theoretical framework is extended by Cuadra and Sapriza, (2008). Their em-pirical analysis shows that economies affected by political instability experience higherdefault rates and larger level and volatility of sovereign interest rate spreads.

D’Erasmo, (2008) considers a model of sovereign default where a government tran-sits through different political states unobservable to investors. This uncertainty in-creases default probability and therefore the cost of public borrowing.

Surprisingly, there is a lack of literature about the effects of policy uncertainty onsovereign debt issued by advanced economies. Ulrich, (2013) claims to be the firststudy to document that uncertainty is a priced factor in this bond market. Both real andnominal term structures contain a positive policy premium which is higher the longerthe duration of the bond. Gao and Qi, (2013) find evidence that political uncertaintycreated by gubernatorial elections causes a surge in the cost of public financing for localgovernments. The resolution of uncertainty after voting reverses this increase in yields.

3 Federal borrowing and the auction mechanism

The federal government borrows funds for its operations by auctioning Treasury secu-rities. The primary market for U.S. Treasuries is open to all investors. Primary dealersplay a special role: this type of market participant interact directly with the FederalReserve Bank of New York for its open market transactions. Among their duties, pri-mary dealers have to participate meaningfully in all Treasury auctions. They must alsoprovide the New York Fed’s trading desk with commentaries, market information, andanalyses helpful in the formulation and implementation of monetary policy.

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The department of the Treasury makes the refinancing process as transparent aspossible in order to meet its financing needs in an economical way. In the middle ofeach quarter, the Treasury goes through the quarterly refunding process. First it collectsadvice from primary dealers and the Treasury Borrowing Advisory Committee; thenaccording to these advices it releases a policy statement, an estimate of financing needs,and a tentative auction schedule.

Following the directions of the quarterly refinancing process, the Treasury issuesbills, notes, and bonds which can be fixed rate, floating, or inflation-indexed. Auctionsare conducted in accordance with the tentative schedule and they consist in three steps:announcement, bidding, and issuance.

Auctions are announced via a press release containing the following details: type ofsecurity, amount of the security being offered, auction date, issue date, maturity date,terms and conditions of the offering, non-competitive and competitive bidding closingtimes.

Once the auction is announced, investors can post their bids directly, through ded-icated platforms (Treasurydirect for individuals and TAAPS for institutions), or indi-rectly, through a broker or a dealer. Bids can be competitive or non-competitive: theformer are submissions of quantity-yield schedules, the latter are just a submission ofthe quantity an investor is purchasing at any yield. Competitive bidding is limited to35% of the offering amount for each bidder while non-competitive bidding is limited to5 million of dollars per bidder.

When the auction is closed, the Treasury determines the yield which clears theamount offered (stop-out yield) and awards the securities at the stop-out yield. Bidsbelow the stop-out yield and non-competitive bids are awarded for the whole amount,while those at the stop-out are awarded pro-quota. There is no maximum acceptableyield, and the Treasury does not change the size of the offering according to the strengthof bids.

On the issue day, the Treasury delivers the securities to investors and collects pay-ments. Bills are issued at discount, while notes and bonds are issued at a price close topar2 and pay coupons semiannually.

The uniform price sealed-bid auction mechanism described above became the stan-

2The coupon rate is a multiple of 1/8 of percentage point, and it is chosen in order to have the priceclosest to par but not above

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dard in 1998. Several economists argued that this mechanism could decrease underpric-ing and therefore increase revenues for the Treasury. However, this claim is disputable.

This type of auction mechanism is prone to create incentive to collude and to pushprices down. In fact, bidders have monopsonistic power over their residual supply (Mil-grom, 2004). In addition, bidders rationally adjust for champion’s plague3 by decreasingtheir demand schedule (i.e. bid shading as in Keloharju et al., 2005). This behavior ofbidders is magnified by uncertainty about the true value of the security: investors willreact not only by decreasing their demand schedule but also by increasing the dispersionof their bids (Nyborg et al., 2002).

On the other hand, a uniform price auction allows uninformed investors to partici-pate and therefore decrease the incentive to collude (Back and Zender, 1993). A persis-tent low price may also attract non-colluding bidders to the auction market and decreasethe underpricing.

Another way to reduce auction underpricing is granting the status of primary dealer:these institutions are explicitly required to participate every auction and their activitycan be easily monitored by the Treasury. If a primary dealer constantly pushes pricesdown it may lose the advantages of its status.

The absence of barrier to entry and primary dealership system may be among thereasons of the good performance of uniform price mechanism in the United States. Gol-dreich, (2007) compares discriminatory and uniform price auctions conducted between1991 and 2000 and provides evidence of a lower underpricing for uniform price mech-anism. The estimated underpricing of about 32 basis points is very low compared to thelevel of interest rates in that period. Nevertheless, underpricing is shown to be increas-ing in the dispersion in signal about the value of the security being offered.

3According to Ausubel, (2004) it is a generalization of the winner’s curse in multi-unit auctions: "Ina multiple-item auction with interdependent values, a bidder’s expected value conditional on winning alarger quantity is less than her expected value conditional on winning a smaller quantity".

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4 Empirical analysis

4.1 Data sources

The primary market for sovereign debt securities is a valuable source of data to assessempirically the effect of uncertainty on sovereign yields and demand. In fact, it is amarket open to all buy side investors where prices are determined by an auction. Inaddition, the centralization and the standardization of the allocation procedures providea clear advantage compared to the secondary market (Over-The-Counter) both in termof coverage and detail of data.

Data about primary market for U.S. Treasuries have an official source: the depart-ment of the Treasury releases information on auctions results in a publicly availabledatabase which contains plenty of data for each auction since 1979. I consider onlysecurities with time-to-maturity between 6 months and 30 years because they have anauction schedule and a notice period between the announcement and the auction longenough. I integrate this dataset with data on the amount tendered by and allotted toeach class of investors. These data are provided by the office of the Treasury SecurityServices at the Bureau of the Fiscal Service4 and from the Department of the Treasuryrespectively.

Uncertainty is a latent variable which needs to be proxied. Baker et al., (2012) pro-pose an index of economic policy uncertainty available at daily level. This index is anaggregation of: (i) a measure of newspaper coverage of policy-related economic uncer-tainty news, (ii) the number and the revenue of federal tax code provisions expiring inthe year and (iii) a measure of disagreement among professional forecasters on inflationand federal government purchases. This index is a comprehensive gauge for the diffi-culty with forming expectations about several policy related variables. The news basedcomponent provides a measure of uncertainty about future policy in general. The num-ber of expiring fiscal code provisions captures fiscal policy uncertainty because they areusually updated at the very last moment. The disagreement among forecasters aboutinflation is a measure of monetary policy uncertainty. The same authors also provide asimilar news based index for global policy uncertainty and for equity uncertainty.

In addition to the uncertainty indices, I take into account a standard set of "fear4These data come as response to the Freedom of Information Act requests no. 2015-07-024 and no.

2016-11-144.

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gauges": the C.B.O.E. volatility indices. The VIX index accounts for equity volatilityand the TYVIX for the volatility of the 10 year Treasury note. In the analysis I alsotake into account the volatility risk premium for these indices5. Negative values of thevolatility risk premium mean that realized volatility in a given market is higher thanexpected, hence they are an indicator for distress (see figure 1).

The last set of data used in this analysis consists of time series of swap rates forvarious tenors. The choice of swaps as reference rates is motivated below.

4.2 Empirical modeling

The empirical analysis aims to test whether economic policy uncertainty is perceivedas a risk factor for Treasuries. According to the literature reviewed in section 2, analternative hypothesis is that such uncertainty triggers flights to quality. The analysisdeals with three key aspects of a bond auction: yield, demand, and bidders’ behavior.

The first empirical model aims to explain the yield paid on Treasury issues as afunction of policy uncertainty, equity uncertainty and volatility indices. A positive signof the coefficient of policy uncertainty means that policy uncertainty increases the riskof Treasuries, a negative one supports the flight to quality hypothesis. For each auction,I pair the security offered with the swap having the most similar tenor, if the differencein time-to-maturity is higher than 6 months the observation is discarded.

To control for the level of interest rates prevalent at the auction date, I take as depen-dent variable the difference between the yield of Treasuries determined by the auctionmechanism and the swap rate with comparable tenor as identified above. Among differ-ent proxies for the prevalent level of interest rate, swap rates have two useful properties:they are available for a large set of tenors, and they appear to be uncorrelated with theproxies for uncertainty and volatility I use6. One issue of swap rates is the availabilityof data before 1998: only tenors going from 2 to 10 years have a long time series (start-ing in 1987). To obtain more general results and to exploit the larger number of swaptime series (from 1 to 30 years), I take auctions conducted between January, 1st 2003

5Volatility risk premium is the difference between the expected volatility, as measured by the volatilityindex, and the one under the objective probability proxied by the realized volatility. The latter is computedaccording to Bollerslev et al., (2009). See the appendix for details.

6I compute correlations of both levels and first differences for all maturities. In addition, the regressionof swap rates on uncertainty and volatility proxies shows no joint significance of all coefficients.

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and March, 15th 2017 as standard sample. This time horizon starts with the inceptionof TYVIX index. Since this time horizon is characterized by several economic eventswhich may affect the estimates, I run the regressions both for the whole sample and forseveral subsamples.

To include policy uncertainty and market volatility in the model, I take the averagelevel of the aforementioned indices during the period going from the announcement tothe auction of Treasuries. In the regression I use the logarithm of these averages to takenon-linearity into account.

In order to disentangle the effect of liquidity, I include the on vs off-the-run spreadfor the 10 Year note7. Also the interventions of the Federal Reserve affected the levelof liquidity, therefore I include dummies for the three Quantitative Easing measures(QE) and the Term Auction Facility (TAF). More details about the construction of thesevariables are given in the appendix.

The set of controls includes time-to-maturity and a dummy for recession periodsas defined by the National Bureau of Economic Research. The model is estimatedseparately for nominal Treasury securities and for inflation linked securities (TIPS). Theestimates are computed by weighting each auction by the amount of securities offered.

Demand is another relevant variable concerning a bond auction. To investigate on itsdeterminants, I compute the regressions of the bid-to-cover ratio on uncertainty proxies,volatility indices, and the same set of controls. In this case, a negative sign of the policyuncertainty coefficient supports the increasing risk hypothesis, while a positive one iscompatible with flights to quality.

The last analysis regards bidders’ strategy. To estimate the model, I take an approxi-mation of bid skewness8 as left hand side variables. In particular I focus on determinants

7The on vs off-the-run spread is the difference between the on-the-run 10 year constant maturity yieldprovided by the department of the Treasury and the 10 year off-the-run yield computed according toGürkaynak et al., (2007). The higher this spread, the higher the liquidity in the market.

8For each auction the department of the Treasury reports the median bid, the low bid (which is the5th percentile of bids), and the high bid (which is the maximum accepted and awarded bid). The last twoare proxy for the minimum and the maximum bid. I compute standard deviation and Pearson’s skewnesscoefficient assuming that the density on the left and on the right of the median is uniformly distributed.The density function is therefore

f(x) =1

2(median−min)1[min,median)(x) +

1

2(max−median)1[median,max](x)

In case the median is equal to the minimum (maximum), the first (second) term is replaced by half the

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of bidders’ aggressiveness at auction. Skewness is a variable related to aggressivenessbecause a lower skewness means that the density of yields is concentrated at highervalues, hence investors’ aggressiveness in terms of price is low (and the converse). Tomeasure the aggressiveness of different categories of investors, I compute the ratio be-tween the securities awarded and the offered tendered by each type of investors. I thenregress this award ratio on the same variables. For both the skewness and the awardratio, a negative sign is a consequence of increasing risk, while a negative one is aconsequence of flights to quality.

Table 2 provides summary statistics for the variables introduced here. Time seriesvariables are averaged on a 7 days horizon to provide an hypothetical average levelduring the notice period of an auction. A brief technical description of the variablesused in this analysis can be found in table 10.

5 The pricing of uncertainty in the primary market forTreasuries

The first step of this analysis is to establish a relation between economic policy un-certainty and the yield of the Treasuries at the auction. During the time frame of thissample, we can observe several economic events which may affect the relation betweeneconomic policy uncertainty and the price of Treasuries: these include the great reces-sion, the quantitative easing, and the rating downgrade of the federal debt. For suchreason, the regression performed on the whole sample does not yield any significant re-sults. However, splitting the samples before and after the aforementioned events revealsa break in the relation between uncertainty and the pricing of Treasuries. To identifythe breakpoint, I run the regressions after splitting the sample at different points, and Iconsider as breakpoint the moment at which the relation between policy uncertainty andthe auctioned spread is the strongest. The breakpoint occurs on October 27th, 2010.

Table 3 and 4 summarize the results of estimations before and after the break, whilethe remainder of this section provides detailed comment.

Dirac mass in the minimum (maximum).

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5.1 Policy uncertainty and nominal securities

The United States’ federal government is regarded as the most reliable issuer of sovereigndebt securities. However, the primary market appears to price the level of policy uncer-tainty in the early part of the sample. Investors cannot diversify policy risk and thereforethey demand a premium for it (Pástor and Veronesi, 2013). This claim appears to holdalso when considering a security regarded as a hedge for other risks.

Figure 1 shows that uncertainty is anticipative: it increases before a negative eventand drops once it is realized. This happens irrespectively of the positive or negativeresolution of the event. Looking at the type of events causing a surge of uncertaintyhelps to find an explanation for increasing yields.

The first type of events may be related to moral hazard: it is difficult for a lender tomonitor government’s actions, it is even more difficult to enforce a policy not harmfulfor debt servicing prospects. Bailouts are an example of moral hazard events: the federalgovernment allocated a large sum of money to finance the TARP program. An investorwill anticipate the prospect of a government running a high deficit and demand a higheryield.

The second event which triggered a surge in uncertainty is related to monetary pol-icy. In 2007, the Fed cut the overnight rate twice in a very short time horizon. On theone hand, this policy was beneficial for the distressed financial market, on the other itfueled fears of high inflation.

In addition to these events, we have to consider the effect of fiscal efficiency onthe supply of Treasuries. Amador, (2003) suggests a relation between uncertainty andfiscal efficiency which holds also for the United States. Figure 2 depict a direct relationbetween policy uncertainty and monthly changes in outstanding Treasury securities9.According to Greenwood and Vayanos, (2014) an increase in supply should raise bondyields and expected returns. Supply effect might play a role on price formation in theprimary market.

All these findings are consistent with the pessimistic behavior predicted by the the-oretical models of choices under uncertainty: agents anticipate harmful events and de-mand a higher premium.

To rule out the effects of the liquidity level in the Treasury market, I include the

9Data on outstanding debt securities are contained in the Monthly Treasury Statement.

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on vs. off-the-run spread in the regressions10 as a proxy for liquidity (Fleming, 2003).We can notice that liquidity in the secondary market is not affecting the auctions, sincerecently auctioned securities are always the most liquid. Another proxy for liquiditylevel is represented by Fed policy measures aimed to provide relief to financial markets(QE and TAF). We can notice the opposite effect of these policies on yields. At thepeak of subprime crisis the TAF measure led to lower yields. QE instead increasedthem because of inflation fears and the erosion of the liquidity premium of Treasuries(Krishnamurthy and Vissing-Jorgensen, 2011). Despite the relevance of these controlvariables the effect of uncertainty remains present.

The effects of uncertainty are expected to be magnified when considering longertime horizons (Baker et al., 2012). I test this hypothesis by allowing for the policyuncertainty coefficient to be different among 3 time-to-maturities windows. For eachpossible combination of time-to-maturity windows, I compare the estimates with thebenchmark model with constant coefficients using the F-test: for all combination theresidual standard deviation is not significantly different. Uncertainty appears to movethe whole Treasury yield curve in a similar way.

The literature reviewed in section 2 suggests a relation between bond spreads andeconomic sentiment. To take this variable into account, I extend the model includingthe Michigan Consumer Sentiment index among the explanatory variables. Estimatesare not changing after the inclusion of this control and the sentiment variable itself doesnot have a significant effect.

According to the dynamics described above, also the events occurring in the secondpart of the sample are likely to increase Treasury yields. Nevertheless, the relationbetween uncertainty and yields changes after October 2010.

5.2 Policy uncertainty and TIPS

In a seminal paper (Roll, 1996), Treasury inflation-linked bonds are defined as one ofthe least risky assets, virtually immune to both default and inflation risk.

The department of the Treasury adopts the strategy of bearing the inflation risk todecrease long-term financing costs. A central government is more suited to assume

10It is not possible to control for liquidity when taking into consideration volatility indices because ofthe high correlation between them.

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inflation risk: first, it has not a finite life (as an ordinary investor has), hence it canspread inflation risk on a much longer time horizon (Anderson, 1999); second, fiscalrevenues are correlated with inflation, issuing TIPS is therefore a way to match assetand liabilities of the federal government (Brinjolfsson, 2005). For these reasons TIPSare not a mere risk transfer but an effective re-distribution of risk.

According to results in table 4, TIPS appear to be more sensitive to policy uncer-tainty in the first part of the sample. This seems a counter-intuitive finding: why shoulda safer security be more sensitive to policy risk?

Sovereign inflation-linked securities are a powerful signal about future policy: anissuance of TIPS should imply a commitment to keep inflation and public deficit undercontrol. However, this kind of commitment must be credible. In fact, there are someissues which may harm investors’ trust in this signal.

Inflation-linked securities have the real interest rate as reference for their pricing.Uncertainty about this macro variable is likely to affect their yield. We can see in figure1 that peaks in uncertainty often coincide with disputes about choices which might haveled to a higher fiscal deficit, such as the TARP. Consistent with Blanchard, (1984), theexpectation of high deficit is likely to affect the real interest rate curve. TIPS are likely tobe severely affected by high deficit expectations because of their long time-to-maturity.

Another determinant of the higher yield is real rate risk determined by monetarypolicy uncertainty. When the policy is inconsistent (e.g. the central bank takes a toodovish stance) real interest rates are likely to increase (Stulz, 1986). Investors anticipatea possible loss on bonds linked to the real interest rate and therefore demand a higherpremium. Estimates are coherent with this fact: one of the components of EconomicPolicy Uncertainty index is the dispersion in inflation forecasts, hence it captures un-certainty in monetary policy. Another example of the effects of an incoherent monetarypolicy is related to the quantitative easing. This measure alimented worries of inflation-ary pressure despite the declared goal of boosting the recovery process. The perceptionof QE measures as misfitted led to a higher yield quantifiable between 51 and 88 bps.Instead, the Term Auction Facility was not perceived as incoherent and led to a savingin the cost of financing.

Indexed securities have an unfavorable tax treatment in the U.S. because the adjust-ment of the principal is taxed on the year it occurs, while its payment is done only at

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maturity, it is the so called "phantom income" effect11. To illustrate, we can make thefollowing example: consider an inflation-linked bond issued at par with face value of 1,real rate of return R and suppose constant inflation I . Every time period j the bond willgenerate R(1+ I)j as coupon cash flow (we consider the compounding of inflation overj periods) and (1 + I)j − 1 as phantom income due to the adjustment of the principal.With a tax rate of τ the net cash flow in period j will be (1−τ)R(1+I)j−τ [(1+I)j−1].Consider now a nominal bond equivalent in terms of yield to maturity and issued at parwith nominal rate N . In this case we won’t observe any phantom income and net cashflow will be (i − τ)N . It is straightforward that anticipated taxation on TIPS is un-favourable for any non-zero discount rate. In addition, if we have uncertainty aboutfuture tax rate12, it will be more harmful for TIPS than nominal bonds because its effectis magnified by taxation on phantom income. In presence of a high level of fiscal uncer-tainty, investors will demand a higher premium and therefore yields on inflation-linkedbonds will increase more than those on nominal bonds13.

One last explanation of the higher sensitivity of TIPS to uncertainty is provided byFleckenstein et al., (2014). They argue that the U.S. Treasury cannot honor its inflation-linked debt by just printing money because this will generate inflation and increase theprincipal of TIPS. In the end TIPS should behave as foreign currency-denominated debtwhich cannot be "inflated away".

After October 2010, we observe the inverse pricing also for TIPS. Such result issimilar to the one regarding nominal securities.

5.3 Fear gauges and auctioned yields

As pointed out before, perceived market risk differs from uncertainty. This part of theanalysis aims to disentangle the effect of risk using well known indicators for marketvolatility: VIX and TYVIX indices. Table 3 reports estimates of four regressions of

11Details about taxation of TIPS are available at the following URL:http://www.treasurydirect.gov/indiv/research/indepth/tips/res_tips_tax.htm

12Policy uncertainty index includes the number of fiscal code provision set to expire.13One might argue that when TIPS are held in form of tax exempt investment vehicles (such as pension

funds and 401(k) accounts) the effect of phantom income will disappear. Unfortunately I could not findany data about TIPS ownership to prove this point. Roll, (2004) argue that tax exemption of TIPS isunlikely, otherwise there would not be plausible explanation for decline of holding period returns of thesesecurities.

16

spread on volatility indices for nominal bonds.Estimates capture the well known flight-to-safety from equity to bonds in the first

part of the sample. A distress in the equity market, represented by a low volatility riskpremium for VIX, will decrease yields paid on Treasury bonds. The same effect is notpresent for the level of the VIX index. This might suggest that the primary market isparticularly sensitive to extremely events, which are well captured by the volatility riskpremium.

Equity is an asset class representing a typical risky commitment and a surge involatility will shift investor preferences towards Treasuries. This is particularly true forrecently auctioned securities (on-the-run) which are the most liquid. This result is alsosupported by recent literature. Krishnamurthy and Vissing-Jorgensen, (2012) provideevidence that liquidity and safety are two priced factors in bond markets (they providea risk based explanation). Caballero and Krishnamurthy, (2008) document that agentsfacing adverse events synchronously turn conservative in their allocation of risk capital(lower yields are caused by a demand shock). In addition, Zhou, (2010) reports that lowvariance risk premium predicts low returns on equity markets, therefore investors aremotivated to hedge and shift their preferences towards safer assets. Revision of expec-tations may also play a role: agents observing a higher than expected variance may turnmore pessimistic and choose a conservative asset allocation.

TYVIX can be considered a fear gauge too: in presence of bad expectations the levelof this index increases. Yields should be driven down by flights-to-quality and recessionfears (Mele and Obayashi, 2016). Nevertheless, the results are not highlighting a robustrelation between these two variables in the primary market, neither before nor after thebreakpoint.

Table 4 reports equivalent results for TIPS: the peculiarity of inflation-linked se-curities is the sensitivity of their spread to the level of VIX index. Volatility of thestock market and inflation-linked yields may appear unrelated, however equity volatil-ity propagates to inflation expectations through monetary policy. Rigobon and Sack,(2003) report that monetary policies are driven by the stock market. In particular, targetinterest rates and stock prices move in the same direction. Higher volatility in the stockmarket may lead to uncertainty in monetary policy choices which increase the auctionedspread. In fact, this result is stronger during the first part of the sample, when monetarypolicy has more relevant implications. In addition, the level of VIX index is positively

17

related to future real rates (Bekaert et al., 2013), hence auctioned yields are likely to behigher for securities linked to real interest rate.

6 Demand and bidding strategy

The analysis is now focused on the effects of economic policy uncertainty on the de-mand and on the behavior of the bidders. Table 5 reports the estimates for the bid-to-cover ratio before and after the breakpoint identified in the previous section. Unlike forspreads, the results looks quite stable in the two subsamples.

The domestic policy uncertainty appears to increase the demand for Treasuries.While this result is justifiable in presence of decreasing yields (after October 2010),it is quite at odds with the pricing of uncertainty found in the first part of the sample.Another puzzling result is the depressing effect of global policy uncertainty on the de-mand: one would expect that higher global uncertainty would trigger a flight to safety.

The effect of volatility indices is more straightforward. The positive coefficient ofthe VIX index is compatible with a flight to quality from the equity market. In fact,this relation is weaker in the "new normal" period, when we observe less turmoil in theequity market. The negative relation between TYVIX and the demand for Treasuries isalready documented in the literature (C.B.O.E. Futures Exchange, 2015), but it appearsto be noisier in the second part of the sample.

Alternative investment opportunities may affect the demand. To rule out their effect,I compute regressions including the swap rate among the explanatory variables: thisdoes not lead to significant results.

To fully understand the mechanism driving the demand, we need to analyze thebidding strategy of investors, in particular their aggressiveness. One of the possibleproxies for aggressiveness is the skewness of the bid distribution: a lower skewnessmeans that the density of the distribution of yields is more concentrated on high values,hence the bidders are less aggressive. Table 6 reports the estimates of the regression ofthe Pearson skewness coefficient (remapped on the real line) on the same explanatoryvariables used before, and on the prevalent swap rate to proxy for alternative investmentopportunities.

As one would expect, the sign of economic policy uncertainty coefficient has a neg-ative sign. This suggest that investors are less aggressive because they conduct bid

18

shading to avoid the champion’s plague. However, such results are not very robust.Moreover, the negative effect of uncertainty on aggressiveness persists in the secondpart of the sample, in which it is not backed by a coherent movement of yields. Lookingthen at the volatility indices, we see that their effect on aggressiveness is the opposite ofthe one on demand. TYVIX increases the aggressiveness, while VIX decreases it.

The results reviewed above may suggest an antithetic relation between demand andaggressiveness. I test this implication with an instrumental variable regression to takeinto account simultaneity between demand and skewness. I regress the bid-to-cover ratioon the same variables reported in table 5, plus skewness to proxy for aggressiveness. Iuse both 2SLS regression and Limited Information Maximum Likelihood (LIML) toaccount for possible weak instruments.

Results in table 7 highlight the inverse relation between bid aggressiveness and de-mand: after including skewness among the explanatory variables, the magnitude andsignificance of uncertainty and volatility proxies almost vanish. In addition, such rela-tion is almost constant in the two sub-periods, in evident opposition with results aboutthe pricing of Treasuries.

The relation between uncertainty and fiscal efficiency (Amador, 2003) could ex-plained this link between demand and aggressiveness. The higher amount of Treasuriesissued during periods of high uncertainty (see figure 2) gives investors higher chancesof being awarded at the auction. Therefore, they will have an incentive to be less ag-gressive when bidding. The higher yield determined by the auction mechanism attractsa group of yield-oriented investors, which I label as speculators. On the other handinvestors hedging volatility, as in Litterman et al., (1991), aim at a guaranteed return.They will therefore bid for a higher price to increase their chances of being awarded thesecurities. I label this category of investors as hedgers.

To test the presence of hedgers and speculators in the auction market, I rely on aunique dataset containing the decomposition of the offering amount by investor class.The combination of this dataset with the Investor Class Auction Allotment dataset notonly allows to see what factors determine the demand of each category of investors, butit also allows to assess the aggressiveness of each investor class. To do so, I computethe ratio between the securities awarded to, and the offers tendered by each class ofinvestors. Although it cannot be computed market-wide, this award ratio is a bettermeasure of aggressiveness because it is does not rely on an approximate distribution of

19

bids and it is more robust to outliers in the bid distribution compared to the skewness.The first step to investigate the behavior of different investors is to look at their

contribution to the demand. Figure 8 shows the historical composition of bids tenderedby each investor class defined in table 11. The main contributors to the demand forTreasuries are dealers (both primary and non), followed by foreigners and funds.

The upper panel of table 8 reports the estimates of the effect of the variables dis-cussed above on the demand of different investor classes. The effect of domestic policyuncertainty on demand is mostly imputable to foreign investors. The lower panel ofthe same table reports the impact of the same variables on the aggressiveness. We cannotice that depository institutions, pension funds, and foreigners bid less aggressivelyin presence of uncertainty.

Foreigners appear to behave like the speculators described above: their participationis higher and their award ratio is lower during uncertain times. Foreign investors arelikely to purchase Treasuries to profit from their yield and from exchange rate move-ments rather than to hedge risks. Both of these quantities are likely to vary in their favorwhen economic policy uncertainty is high: not only yields tend to be higher (as shownin section 5), but also the exchange rate is advantageous for foreigners since the U.S.dollar is likely to appreciate after resolution of uncertainty (Blomberg and Hess, 1997).

Depository institutions and pension funds instead appear to be hedgers. Their bidsare awarded less because they pretend an uncertainty premium.

Finally, primary dealers are neither changing their bidding nor asking for higheryields. Being close collaborators with the Federal Reserve, they cannot take advantagefrom uncertain situations. This result is coherent with their duty to participate meaning-fully at auctions and to "mop up" residual supply.

The behavior of depository institutions, pension funds, and foreigners does notchange in the second part of the sample. Such behavior is likely to produce the stableeffect of policy uncertainty on the skewness. Primary dealers instead, are more activein the auction market after October 2010. The next section is focused on the causes ofthis structural change.

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7 The structural break of 2010

The results suggest that economic policy uncertainty becomes reversely priced afterOctober 2010. Unlike those in the early part of the sample, all peaks of uncertaintyoccurring after 2010 have a pure political nature. One would therefore expect a strongerrelation between political risk and the yield of Treasuries, because there are no economicevent which may trigger flights to quality.

Another peculiarity of the second part of the sample is the low correlation betweendomestic and global political uncertainty: before 2010, the correlation between the twoseries was around 80%, while after it drops to 35%. International events like the Eu-ropean sovereign debt crisis and Brexit are likely to push investor to purchase safersecurities. However, this does not explain the inverse pricing of policy uncertainty after2010.

The breakpoint in the relation between uncertainty and spreads is right before thesecond wave of quantitative easing. One can hypothesize that the activism of the Fedprovided protection against the consequences of political uncertainty. In fact, QE2 in-volved a large purchase of Treasury securities, and the "operation twist" targeted thelong end of the Treasury curve.

We can notice from table 3 and 4 that both the domestic and the global policy un-certainty have the opposite effect on spreads. Moreover, this is true for both nominalsecurities and TIPS. Another result worth noticing is that the second and the third waveof quantitative easing are not harmful for the yields (See the non-significant coefficientin table 3, and the negative coefficient in table 4), unlike in the first part of the sample.This may suggest that the central bank is among the causes of such structural break.

Looking at the demand and the aggressiveness, we cannot see any particular dif-ference among the two subsamples. Therefore, we are not observing a change in thebehavior of the agents, at least in general terms. Instead, when we look at the behaviorof each category of bidders, we can notice a different behavior of the primary dealers.In presence of uncertainty dealers are more active in the primary market, and their bidsare more aggressive. The activism of the Fed is likely to cause this behavior becauseprimary dealers are the counterparties of the Fed for its open market operations. Theirhigher demand and the lower yield of their bids is pushing down the stop-out yield atwhich the securities are issued.

21

We can see that, in the second part of the sample, depository institutions, individuals,and pension funds behave as hedgers, while foreign investors behave as speculators,as we assessed in section 6. We can notice that the last category is less active at theauction when global uncertainty is high. This happens because the foreign investorsgroup includes central banks as well, which are likely to be more active in their domesticmarket rather than in the US market.

In summary, starting with the second wave of quantitative easing, primary dealerspush treasury yields down as a consequence of their increasing purchases to fulfill theFed’s open market operations. Being the most important bidders, their actions offset theupward pressure caused by the speculators.

8 Conclusions

This paper investigates on the impact of economic policy uncertainty on the primarymarket for Treasuries. Uncertainty is a relevant factor which causes yields to increase.Despite the safety and the liquidity of these securities, the investors act in a pessimisticway and require a higher premium. However, this effect is not stable over time neitherfor nominal securities, nor for TIPS.

The demand for Treasuries is increasing in uncertainty. The cause is an opportunis-tic behavior of foreign investors, which are attracted by the better yield present whenuncertainty is high. After October 2010, the increased activism of the Federal Reservepushed primary dealers to be more active on the primary market. Their increased de-mand and aggressiveness is the cause of the inverse pricing we observe in the secondpart of the sample.

From an asset pricing point of view, this paper contributes to assess the role of eco-nomic policy uncertainty on the pricing of Treasuries, and on how the effects of thisfactor can be altered by the central bank. In terms of the functioning of the primarymarket, this paper highlights the vital role of primary dealers for an economic alloca-tion of the federal debt. Lastly, from a public finance perspective, this study arguesin favor of reducing the government generating uncertainty, since a government cannotpermanently rely on central bank interventions.

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Table 1: Comparison of public financing costs before and after rating downgrade and fiscalcliff dispute resolution. Spread is calculated using swap rate with same tenor as reference rate.Saving is calculated by reducing the coupon payments by the difference in spread.

US rating downgrade (August 5th 2011)

Cusip Security type Auction dateOffering Avg policy Y.T.M. Spread Saving(Mln $) uncertainty (%) (in bps) (Mln $)

912828QX1 5 Year Note 07-27-2011 35000 264.16 1.730 -3.3912828RF9 5 Year Note 08-24-2011 35000 236.20 1.231 -7.9 -81.11912828QW3 2 Year Note 07-26-2011 35000 264.56 0.512 -10.4912828RD4 2 Year Note 08-23-2011 35000 243.11 0.285 -25.9 -108.37

Fiscal cliff dispute (December 31st 2012)

Cusip Security type Auction dateOffering Avg policy Y.T.M. Spread Saving(Mln $) uncertainty (%) (in bps) (Mln $)

912828UE8 5 Year Note 12-18-2012- 35000 197.45 0.918 3.5912828UJ7 5 Year Note 01-29-2013 35000 141.37 0.976 -4.2 -134.12912828UD0 2 Year Note 12-17-2012 35000 209.62 0.308 -6.2912828UK4 2 Year Note 01-28-2013 35000 141.54 0.351 -9.4 -22.40

Table 2: Summary statistics for selected variables

Variable Min. 1st Qu. Median Mean 3rd Qu. Max. Std. Dev.

Policy uncertainty* 18.69 63.7 87.05 103.8 131.5 361.4 55.48

Equity uncertainty* 7.54 24.63 38.48 54.01 60.89 572.5 53.57

Global uncertainty 50.26 81.69 107.40 116.60 143.60 281.40 46.96

TYVIX* 3.79 5.07 6.05 6.57 7.79 14.21 1.99

VIX* 10.15 13.52 16.61 19.25 21.92 70.92 8.78

TYVIX Volatility risk premium* -8.25 0.19 0.95 0.86 1.64 4.69 1.32

VIX Volatility risk premium* -44.6 2.356 4.554 4.039 6.97 27.31 6.01

On vs. off-the-run spread (in bps)* -81.57 -27.54 -11.97 -18.23 -6.99 -1.05 14.8

Spread at auction − Nominal bonds (in bps) -413.5 -47.28 -28.14 -38.98 -17.87 58.8 45.7

Spread at auction − TIPS (in bps) -320.5 -264.6 -219.6 -216.6 -177.8 -14.9 62.09

Bid-to-cover ratio − Nominal bonds 1.11 2.49 3.19 3.332 4.17 10.72 1.016

Skewness − Nominal bonds -0.929 -0.593 -0.415 -0.306 -0.061 0.929 0.369

Note: summary statistics are computed for the time-horizon of the standard sample (2003-2017).Summary statistics for variables denoted with a star are computed for a non-centered moving average of order 7.

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Table 3: Estimates of the regression for spread of nominal securities. Observations are weighted by the offering amount of the auc-tion. Control for time to maturity included in the regression. log(avg global EPU) and onoffspread are omitted in presence ofcollinearity with log(avg policy) and log(avg V IX), respectively. Standard errors (in parenthesis) are robust to heteroskedasticity andautocorrelation.

Dependent variable: Spread (in bps)

Before October 2010 After October 2010

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

log(avg policy) 13.019∗∗∗ 7.731∗ 9.051∗∗ 7.482∗∗ −12.090∗∗∗ −9.628∗∗∗ −13.808∗∗∗ −10.276∗∗∗

(4.393) (4.644) (4.149) (3.485) (3.267) (3.175) (3.045) (2.866)

log(avg equity) −12.792∗∗∗ −3.168 −6.342∗∗ −3.562∗∗ 1.948 3.587∗∗∗ 2.131 3.550∗∗∗

(4.675) (1.928) (2.825) (1.771) (1.526) (1.228) (1.455) (1.251)

log(avg global EPU) 7.609∗ 8.415∗∗ 11.919∗∗∗ 9.642∗∗∗

(4.489) (3.281) (4.375) (3.580)

onoffspread −19.512 26.726 −8.081 −5.481(17.683) (27.941) (15.311) (13.579)

log(avg TY V IX) −4.487 0.102 0.414 5.001 −7.321 −4.035 −7.505 −3.758(7.055) (7.286) (6.715) (6.480) (5.787) (4.962) (5.714) (5.128)

log(avg V IX) 15.024∗∗ 8.250 8.543 3.175 −11.657∗ −12.491∗ −13.367∗∗ −11.817∗∗

(7.264) (9.255) (7.654) (8.304) (6.405) (6.368) (5.843) (5.837)

avg TY V IX V RP −0.394 −0.451 0.085 −0.005 −0.782 −0.914 −0.985 −0.859(1.459) (1.461) (1.370) (1.359) (0.683) (0.669) (0.725) (0.677)

avg V IX V RP 2.100∗∗∗ 2.012∗∗∗ 1.690∗∗∗ 1.629∗∗∗ −0.234∗ −0.066 −0.222 −0.063(0.426) (0.439) (0.481) (0.488) (0.133) (0.127) (0.143) (0.123)

Recession dummy −48.686∗∗∗ −42.246∗∗∗ −42.526∗∗∗ −24.087∗∗∗ −31.113∗∗∗ −31.365∗∗∗

(8.335) (5.541) (5.845) (6.292) (5.379) (5.334)

QE dummy 48.645∗∗∗ 20.731∗∗∗ 19.853∗∗∗ 4.226 −3.253 1.213(11.758) (5.296) (5.060) (2.693) (2.198) (2.164)

TAF dummy −39.283∗∗∗ −23.286∗∗∗ −23.667∗∗∗

(8.302) (5.337) (5.093)

Constant −55.418∗∗ −84.887∗∗∗ −96.146∗∗∗ −51.733∗∗∗ −72.569∗∗∗ −85.113∗∗∗ −10.493 25.735∗∗ 12.180 −26.089 32.499∗∗∗ 6.285(21.753) (15.169) (17.027) (19.786) (17.009) (17.570) (22.437) (12.082) (18.278) (21.162) (11.781) (19.298)

Observations 393 393 393 393 393 393 541 541 541 541 541 541R2 0.451 0.659 0.664 0.580 0.682 0.687 0.587 0.581 0.627 0.597 0.589 0.628Adjusted R2 0.443 0.653 0.657 0.573 0.675 0.679 0.583 0.577 0.621 0.593 0.585 0.621

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 4: Estimates of the regression for spread of inflation linked securities. Observations are weighted by the offering amount of theauction. Control for time to maturity included in the regression. log(avg global EPU) and onoffspread are omitted in presence ofcollinearity with log(avg policy) and log(avg V IX), respectively. Standard errors (in parenthesis) are robust to heteroskedasticity andautocorrelation.

Dependent variable: Spread (in bps)


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

log(avg policy) 40.387∗∗ 19.892 46.455∗∗∗ 27.041∗∗ −70.423∗∗∗ −81.930∗∗∗ −52.920∗∗∗ −63.258∗∗∗

(16.201) (15.593) (14.808) (13.114) (13.327) (12.084) (10.920) (11.684)

log(avg equity) 0.899 −5.283 −2.681 −11.418 12.841 7.723 13.903 10.944(11.048) (11.121) (10.599) (10.083) (10.588) (9.341) (10.094) (9.561)

log(avg global EPU) 72.487∗∗∗ 73.738∗∗∗ 24.825 36.713∗∗

(18.915) (11.709) (22.974) (16.199)

onoffspread −269.604∗∗∗ −240.989∗∗∗ 6.513 3.560(55.871) (81.828) (134.472) (99.183)

log(avg TY V IX) 24.322 22.725 18.345 18.160 −2.295 −12.036 −22.875 −21.539(30.137) (32.910) (25.903) (31.059) (56.268) (26.114) (40.657) (25.785)

log(avg V IX) 165.997∗∗∗ 147.460∗∗∗ 152.581∗∗∗ 133.434∗∗∗ 26.020 72.109∗∗∗ 2.525 47.130∗∗

(27.413) (32.193) (26.605) (31.381) (35.117) (26.311) (26.554) (22.227)

avg TY V IX V RP 1.399 0.950 1.776 0.968 1.560 −0.001 −1.859 −1.376(3.982) (4.207) (3.337) (3.321) (2.746) (3.606) (2.741) (3.209)

avg V IX V RP 0.643 0.435 −0.402 −0.663 −1.034 0.735 −0.776 0.481(0.989) (1.111) (0.926) (1.032) (0.698) (0.687) (0.962) (0.718)

Recession dummy −33.646 −59.876∗∗∗ −58.014∗∗ 13.210 −9.351 −0.545(23.027) (21.370) (22.211) (23.501) (17.336) (18.926)

QE dummy 51.412∗∗ 85.455∗∗∗ 87.967∗∗∗ −40.960∗∗∗ −53.698∗∗∗ −31.789∗∗∗

(25.385) (16.899) (18.810) (11.838) (10.315) (11.362)

TAF dummy −62.194∗∗∗ −73.174∗∗∗ −82.943∗∗∗

(20.572) (15.488) (17.259)

Constant −470.287∗∗∗ −755.630∗∗∗ −764.716∗∗∗ −473.480∗∗∗ −698.642∗∗∗ −714.821∗∗∗ −277.705∗∗∗ −259.643∗∗ −396.372∗∗∗ −109.411 −136.074∗ −209.116∗∗∗

(66.859) (57.938) (60.513) (62.598) (58.685) (61.426) (83.770) (120.062) (67.978) (95.443) (73.921) (74.227)

Observations 53 53 53 53 53 53 74 74 74 74 74 74R2 0.699 0.716 0.725 0.741 0.795 0.814 0.384 0.052 0.510 0.542 0.413 0.578Adjusted R2 0.666 0.679 0.675 0.701 0.758 0.769 0.339 −0.018 0.450 0.501 0.360 0.519

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

29

Table 5: Estimates of the regression for the bid-to-cover of nominal securities. Observations are weighted by the offering amount ofthe auction. Control for time to maturity included in the regression. log(avg global EPU) and onoffspread are omitted in presenceof collinearity with log(avg policy) and log(avg V IX), respectively. Standard errors (in parenthesis) are robust to heteroskedasticityand autocorrelation.

Dependent variable: Bid-to-cover ratio


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

log(avg policy) 0.555∗∗∗ 0.338∗∗∗ 0.538∗∗∗ 0.334∗∗∗ 0.517∗∗∗ 0.435∗∗∗ 0.493∗∗∗ 0.381∗∗

(0.095) (0.085) (0.101) (0.088) (0.174) (0.160) (0.176) (0.159)

log(avg equity) −0.306∗∗∗ −0.316∗∗∗ −0.244∗∗∗ −0.296∗∗∗ −0.058 −0.084 −0.055 −0.087(0.065) (0.073) (0.062) (0.072) (0.061) (0.072) (0.060) (0.071)

log(avg global EPU) −0.549∗∗∗ −0.463∗∗∗ −0.488∗∗∗ −0.360∗∗

(0.197) (0.169) (0.184) (0.162)

onoffspread −0.256 0.286 1.272 1.309(0.303) (0.360) (1.407) (1.430)

log(avg TY V IX) −0.788∗∗∗ −0.552∗∗∗ −0.820∗∗∗ −0.583∗∗∗ −0.321 −0.447 −0.306 −0.424(0.234) (0.211) (0.211) (0.202) (0.405) (0.394) (0.396) (0.401)

log(avg V IX) 0.957∗∗∗ 0.824∗∗∗ 0.857∗∗∗ 0.779∗∗∗ 0.451 0.415∗ 0.595∗∗ 0.472∗∗

(0.190) (0.191) (0.237) (0.212) (0.279) (0.228) (0.256) (0.199)

avg TY V IX V RP −0.020 −0.026 −0.017 −0.025 0.009 0.017 0.026 0.021(0.019) (0.018) (0.019) (0.018) (0.034) (0.036) (0.037) (0.038)

avg V IX V RP 0.015∗∗∗ 0.009∗∗ 0.012∗∗ 0.009∗ 0.016∗∗∗ 0.010∗ 0.015∗∗ 0.010∗∗

(0.004) (0.004) (0.005) (0.005) (0.006) (0.005) (0.006) (0.005)

Recession dummy −0.408∗∗∗ −0.441∗∗∗ −0.488∗∗∗ −0.418∗∗∗ −0.517∗∗∗ −0.544∗∗∗

(0.106) (0.144) (0.122) (0.132) (0.149) (0.136)

QE dummy 0.156 0.056 −0.019 0.060 0.274∗∗ 0.102(0.116) (0.148) (0.122) (0.148) (0.123) (0.135)

TAF dummy 0.115 0.183 0.135(0.151) (0.145) (0.138)

Constant 1.365∗∗∗ 1.418∗∗∗ 1.049∗∗∗ 1.271∗∗∗ 1.716∗∗∗ 1.154∗∗∗ 4.050∗∗∗ 2.624∗∗∗ 3.526∗∗∗ 3.827∗∗∗ 2.054∗∗∗ 3.031∗∗∗

(0.355) (0.366) (0.337) (0.349) (0.491) (0.443) (1.036) (0.674) (1.068) (0.963) (0.637) (0.982)


Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

30

Table 6: Estimates of the regression for bid aggressiveness (bid skewness) for nominal securities. Observations are weighted by theoffering amount of the auction. Control for time to maturity included in the regression. log(avg global EPU) and onoffspread areomitted in presence of collinearity with log(avg policy) and log(avg V IX), respectively. Standard errors (in parenthesis) are robust toheteroskedasticity and autocorrelation.

Dependent variable: Transformed skewness


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

log(avg policy) −0.090 0.095 −0.085 0.101 −0.158∗ −0.141 −0.175∗ −0.185∗

(0.120) (0.131) (0.120) (0.131) (0.094) (0.088) (0.103) (0.103)

log(avg equity) 0.036 0.035 −0.015 0.024 −0.012 −0.009 −0.011 −0.013(0.072) (0.071) (0.077) (0.073) (0.065) (0.069) (0.065) (0.070)

log(avg global EPU) −0.003 −0.076 0.037 0.007(0.139) (0.125) (0.161) (0.146)

onoffspread −0.336 −0.774 −0.721 −0.698(0.386) (0.507) (0.609) (0.601)

log(avg TY V IX) 0.837∗∗∗ 0.872∗∗∗ 0.799∗∗∗ 0.837∗∗∗ 0.324∗ 0.330∗ 0.334∗ 0.360∗

(0.243) (0.255) (0.249) (0.259) (0.192) (0.184) (0.192) (0.189)

log(avg V IX) −0.557∗∗∗ −0.665∗∗∗ −0.471∗∗ −0.587∗∗ −0.263∗ −0.118 −0.251 −0.074(0.195) (0.221) (0.208) (0.238) (0.154) (0.160) (0.155) (0.164)

avg TY V IX V RP −0.032 −0.032 −0.037 −0.037 0.024 0.025 0.027 0.029(0.030) (0.030) (0.030) (0.030) (0.030) (0.027) (0.030) (0.028)

avg V IX V RP 0.002 0.003 0.007 0.007 −0.005 −0.004 −0.005 −0.004(0.006) (0.007) (0.007) (0.007) (0.007) (0.006) (0.007) (0.006)

Swap 0.042 0.029 0.045 0.031 0.020 0.036 0.122∗∗ 0.128∗∗ 0.120∗∗ 0.122∗ 0.124∗∗ 0.114∗

(0.043) (0.038) (0.044) (0.045) (0.040) (0.046) (0.062) (0.050) (0.058) (0.062) (0.053) (0.060)

Recession dummy −0.015 −0.022 −0.027 −0.087 −0.068 −0.086(0.125) (0.120) (0.123) (0.142) (0.135) (0.136)

QE dummy −0.290∗ −0.232∗ −0.227 0.040 0.031 0.084(0.160) (0.140) (0.143) (0.076) (0.064) (0.087)

TAF dummy 0.075 0.114 0.136(0.142) (0.135) (0.135)

Constant −0.808 −0.893 −1.247∗ −0.670 −1.045∗ −1.371∗ −0.411 −0.960∗∗∗ −0.316 −0.549 −1.022∗∗∗ −0.715(0.557) (0.593) (0.709) (0.563) (0.614) (0.706) (0.616) (0.341) (0.605) (0.645) (0.358) (0.632)


Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

31

Table 7: Estimates of the regression for bid-to-cover ratio for nominal treasuries. Exogenousinstrumental variables are the explanatory variables used in the regression for skewness cor-responding to the column of table 6 indicated in "Instruments" row. Control for time to ma-turity included in the regression. onoffspread is omitted in presence of collinearity withlog(avg V IX). Standard error (in parenthesis) are robust to heteroskedasticity and autocor-relation for 2 stage least squares regressions.


2 stage least squares LIML

(1) (2) (3) (4) (5) (6)

−Transformed skewness -3.467*** -3.876*** -3.583*** -4.272*** -4.981*** -4.767***(0.856) (0.92) (0.922) (1.14) (1.397) (1.442)

log(avg policy) 0.169 0.19 0.105 0.141(0.19) (0.195) (0.205) (0.223)

log(avg equity) -0.253** -0.219* -0.234* -0.171(0.109) (0.116) (0.123) (0.144)

onoffspread -0.89 -0.674(0.804) (0.869)

log(avg TY V IX) 1.049* 0.93* 1.369* 1.292*(0.553) (0.527) (0.766) (0.764)

log(avg V IX) -0.545 -0.369 -0.948 -0.812(0.421) (0.397) (0.587) (0.584)

avg TY V IX V RP -0.024 -0.023 -0.002 -0.002(0.054) (0.05) (0.063) (0.06)

avg V IX V RP 0.008 0.005 0.005 0.003(0.013) (0.012) (0.014) (0.014)

Recession dummy -0.399 -0.448 -0.448(0.285) (0.306) (0.283)

QE dummy 0.144 0.197 0.167 0.197 0.227 0.2(0.133) (0.14) (0.143) (0.151) (0.174) (0.171)

TAF dummy -0.186 -0.066 -0.097 -0.327 -0.211 -0.237(0.236) (0.225) (0.215) (0.264) (0.278) (0.271)

Constant -5.005*** -6.339*** -5.989*** -6.63*** -8.357*** -8.102***(1.629) (2.087) (1.965) (2.26) (3.119) (3.09)

Observations 934 934 934 934 934 934Instruments (4) (5) (6) (4) (5) (6)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

32

Table 8: Upper panel: estimates of the regression for bid-to-cover ratio for nominal securities decomposed by investorclass. Observations are weighted by the offering amount of the auction. Control for time to maturity included in theregression. Standard errors (in parenthesis) are clustered by year.Lower panel: estimates of the regression for award ratio (ratio between the amount accepted and the amount tendered)for nominal securities decomposed by investor class. Observations are weighted by the offering amount of the auction.Control for time to maturity included in the regression. Standard errors (in parenthesis) are robust to heteroskedasticityand autocorrelation.Due to data availability the sample period is April 2008- October 2010


Total Depository Inst. Individuals Pension Funds Investment Funds Foreigners Primary Dealers Oth. Dealers

(1) (2) (3) (4) (5) (6) (7) (8) (9)

log(avg policy) 0.132∗∗∗ 0.115∗∗ −0.009∗ −0.003 0.0002 −0.020 0.120∗∗∗ 0.004 0.041(0.038) (0.057) (0.005) (0.002) (0.001) (0.046) (0.039) (0.106) (0.026)

log(avg equity) −0.198∗∗ −0.171∗∗ −0.003 0.0004 0.0001 −0.005 0.017 −0.116∗ −0.027∗

(0.081) (0.080) (0.003) (0.001) (0.001) (0.027) (0.020) (0.059) (0.015)

log(avg TY V IX) −0.263 0.057 −0.019 0.004 0.001 0.067 0.191∗ −0.011 −0.086∗

(0.273) (0.264) (0.015) (0.003) (0.001) (0.082) (0.103) (0.208) (0.051)

(log(avg V IX) 0.312 0.113 −0.005 −0.006∗∗ 0.0005 0.117 −0.149∗ 0.095 0.011(0.217) (0.220) (0.014) (0.003) (0.001) (0.076) (0.080) (0.173) (0.041)

avg TY V IX V RP 0.0001 0.008 0.0001 0.0002 −0.0001 −0.004 0.008∗ 0.004 −0.001(0.015) (0.014) (0.001) (0.0002) (0.0001) (0.005) (0.004) (0.011) (0.003)

avg V IX V RP 0.002 −0.003 −0.001 −0.00002 0.0001 −0.001 −0.001 −0.002 −0.0003(0.003) (0.004) (0.001) (0.00004) (0.00004) (0.002) (0.002) (0.003) (0.001)

Recession dummy −0.576∗∗∗ −0.379∗∗∗ 0.013 0.002 −0.002∗∗ −0.123∗∗∗ −0.114∗∗ −0.162∗ −0.036(0.127) (0.130) (0.013) (0.002) (0.001) (0.035) (0.047) (0.094) (0.023)

QE dummy 0.179 0.035∗ −0.003∗∗ −0.001 0.028 0.078 0.116 0.068∗∗∗

(0.112) (0.020) (0.001) (0.001) (0.041) (0.053) (0.087) (0.024)

TAF dummy −0.504∗∗∗ −0.041∗∗ 0.001 0.0005 −0.021 −0.047 −0.311∗∗∗ −0.236∗∗∗

(0.153) (0.019) (0.002) (0.0005) (0.045) (0.063) (0.112) (0.030)

Constant 2.807∗∗∗ 3.551∗∗∗ 0.127∗ 0.029∗∗ −0.004 −0.130 0.480∗∗ 2.463∗∗∗ 0.400∗∗∗

(0.601) (0.630) (0.066) (0.012) (0.005) (0.211) (0.233) (0.499) (0.120)

Observations 196 196 196 196 196 196 196 196 196R2 0.445 0.496 0.260 0.214 0.066 0.282 0.387 0.402 0.770Adjusted R2 0.421 0.469 0.220 0.171 0.016 0.243 0.354 0.370 0.757

Dependent variable: Award ratio

Depository Inst. Individuals Pension Funds Investment Funds Foreigners Primary Dealers Oth. Dealers

(1) (2) (3) (4) (5) (6) (7)

log(avg policy) −0.293∗∗ 0.098 −0.282∗ −0.024 −0.136∗∗∗ 0.007 0.021(0.121) (0.064) (0.161) (0.055) (0.044) (0.018) (0.044)

log(avg equity) 0.048 −0.044 0.012 0.036 −0.011 0.024∗ 0.010(0.075) (0.038) (0.122) (0.037) (0.024) (0.014) (0.032)

log(avg TY V IX) 0.029 0.068 −0.244 −0.003 −0.134 −0.035 0.182(0.207) (0.054) (0.432) (0.118) (0.085) (0.038) (0.111)

(log(avg V IX) −0.349∗ −0.055 0.375 −0.110 0.091 0.013 −0.108(0.196) (0.046) (0.375) (0.101) (0.074) (0.032) (0.103)

avg TY V IX V RP 0.013 0.017∗ −0.019 −0.007 0.011 −0.003 0.004(0.016) (0.010) (0.026) (0.008) (0.007) (0.003) (0.006)

avg V IX V RP 0.004 0.0005 0.002 −0.0003 0.0002 0.001 0.002(0.004) (0.002) (0.009) (0.002) (0.002) (0.001) (0.002)

Recession dummy 0.123 0.025 −0.319 0.012 0.132∗∗∗ 0.083∗∗∗ 0.056(0.097) (0.028) (0.192) (0.052) (0.038) (0.021) (0.058)

QE dummy 0.031 −0.016 0.343∗ −0.015 0.028 −0.060∗∗∗ −0.261∗∗∗

(0.109) (0.026) (0.181) (0.062) (0.035) (0.020) (0.062)

TAF dummy 0.188∗ −0.018 −0.481∗∗ −0.009 −0.016 0.082∗∗∗ 0.316∗∗∗

(0.113) (0.033) (0.197) (0.070) (0.045) (0.023) (0.070)

Constant −0.066 0.710∗∗∗ 1.657 0.973∗∗∗ 0.970∗∗∗ 0.086 0.151(0.559) (0.224) (1.119) (0.303) (0.219) (0.097) (0.264)

Observations 168 195 74 196 196 196 196R2 0.187 0.153 0.311 0.077 0.322 0.571 0.489Adjusted R2 0.135 0.107 0.201 0.027 0.285 0.548 0.461

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

33

Table 9: Upper panel: estimates of the regression for bid-to-cover ratio for nominal securities decomposed by investorclass. Observations are weighted by the offering amount of the auction. Control for time to maturity included in theregression. Standard errors (in parenthesis) are clustered by year.Lower panel: estimates of the regression for award ratio (ratio between the amount accepted and the amount tendered)for nominal securities decomposed by investor class. Observations are weighted by the offering amount of the auction.Control for time to maturity included in the regression. Standard errors (in parenthesis) are robust to heteroskedasticityand autocorrelation.Due to data availability the sample period is October 2010- November 2016


Total Depository Inst. Individuals Pension Funds Investment Funds Foreigners Primary Dealers Oth. Dealers

(1) (2) (3) (4) (5) (6) (7) (8) (9)

log(avg policy) 0.304∗∗∗ 0.247∗∗ 0.003 0.0002 0.0001 −0.069∗∗∗ 0.042∗∗∗ 0.170∗∗∗ 0.141∗∗∗

(0.111) (0.123) (0.002) (0.001) (0.0004) (0.024) (0.013) (0.086) (0.015)

log(avg equity) −0.038 −0.043 −0.0005 −0.0003 −0.00001 0.010 −0.030∗∗∗ −0.013 0.0003(0.064) (0.063) (0.001) (0.0003) (0.0002) (0.016) (0.011) (0.069) (0.010)

log(avg global EPU) −0.443∗∗ −0.345∗∗ −0.006∗∗∗ −0.001 −0.001 0.084∗∗∗ −0.079∗∗∗ −0.387∗ −0.115∗∗∗

(0.174) (0.163) (0.002) (0.001) (0.0005) (0.031) (0.019) (0.201) (0.026)

log(avg TY V IX) −0.294 −0.271 −0.012∗ −0.001 −0.001 −0.104∗∗ 0.142∗∗∗ −0.451 0.015(0.282) (0.287) (0.007) (0.001) (0.001) (0.049) (0.042) (0.325) (0.033)

(log(avg V IX) 0.293 0.358∗ 0.010∗∗ 0.001 0.0004 −0.132∗∗∗ 0.108∗∗∗ 0.301 0.121∗∗∗

(0.206) (0.194) (0.004) (0.001) (0.001) (0.046) (0.031) (0.217) (0.027)

avg TY V IX V RP 0.026 0.030 −0.001 0.00003 −0.0003∗ −0.013∗∗ 0.017∗∗∗ 0.014 0.001(0.029) (0.031) (0.001) (0.0001) (0.0001) (0.006) (0.005) (0.036) (0.004)

avg V IX V RP 0.006 0.006 0.0002∗∗ −0.00001 0.00005∗ −0.003∗ 0.001 −0.0002 0.004∗∗∗

(0.005) (0.005) (0.0001) (0.00003) (0.00002) (0.002) (0.001) (0.006) (0.001)

QE dummy 0.102 0.003∗∗ 0.00004 0.001∗ −0.032 0.0002 0.077 0.073∗∗∗

(0.109) (0.001) (0.001) (0.0003) (0.022) (0.014) (0.138) (0.015)

Constant 3.899∗∗∗ 3.418∗∗∗ 0.020 0.008 0.004∗ 0.765∗∗∗ −0.029 3.511∗∗∗ −0.226∗∗

(0.996) (0.871) (0.014) (0.006) (0.002) (0.168) (0.109) (1.102) (0.115)

Observations 508 508 508 508 508 508 508 508 508R2 0.346 0.350 0.051 0.124 0.119 0.341 0.317 0.329 0.581Adjusted R2 0.336 0.338 0.034 0.109 0.103 0.329 0.305 0.317 0.573

Dependent variable: Award ratio

Depository Inst. Individuals Pension Funds Investment Funds Foreigners Primary Dealers Oth. Dealers

(1) (2) (3) (4) (5) (6) (7)

log(avg policy) −0.084∗∗ −0.013∗∗ −0.093 −0.096∗∗∗ −0.178∗∗∗ 0.025∗∗∗ −0.012(0.038) (0.007) (0.068) (0.033) (0.025) (0.008) (0.017)

log(avg equity) 0.038 0.015∗∗∗ 0.023 0.047∗∗∗ 0.021 0.003 0.006(0.026) (0.005) (0.036) (0.017) (0.017) (0.005) (0.009)

log(avg global EPU) −0.044 −0.002 −0.006 0.089∗ 0.086∗∗ −0.081∗ 0.050(0.067) (0.009) (0.091) (0.049) (0.035) (0.043) (0.039)

log(avg TY V IX) 0.020 0.015 0.051 0.008 −0.080 0.026 0.035(0.090) (0.024) (0.145) (0.066) (0.059) (0.017) (0.037)

(log(avg V IX) −0.192∗∗ −0.026 0.034 −0.122∗ −0.100∗∗ 0.006 −0.022(0.088) (0.016) (0.156) (0.064) (0.048) (0.014) (0.035)

avg TY V IX V RP −0.005 0.001 0.025 −0.003 −0.020∗∗ −0.003 0.004(0.013) (0.002) (0.021) (0.008) (0.008) (0.002) (0.004)

avg V IX V RP −0.009∗∗∗ −0.0002 −0.006 −0.003 −0.003 0.001 −0.001(0.002) (0.0004) (0.006) (0.002) (0.002) (0.0005) (0.001)

QE dummy −0.053∗ −0.004 0.073 −0.054∗ 0.004 0.010∗ 0.028∗∗

(0.030) (0.007) (0.050) (0.028) (0.019) (0.006) (0.013)

Constant 1.924∗∗∗ 1.073∗∗∗ 1.169∗∗∗ 0.947∗∗∗ 1.614∗∗∗ 0.043 0.034(0.316) (0.056) (0.435) (0.233) (0.164) (0.050) (0.139)

Observations 410 507 250 508 508 508 508R2 0.100 0.040 0.215 0.167 0.355 0.294 0.026Adjusted R2 0.080 0.023 0.185 0.152 0.343 0.281 0.008

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

34

Table 10: Description of variables used in the regressions

Variable Description

average policy Average level of policy uncertainty index for a given auction

during the period from the announcement to the auction

average equity Average level of equity uncertainty index for a given auction

during the period from the announcement to the auction

avg global EPU Average level of global policy uncertainty index for a given

auction during the month of the auction

average TY V IX Average level of TYVIX index for a given auction during the

period from the announcement to the auction

average V IX Average level of VIX index for a given auction during the pe-

riod from the announcement to the auction

avg TY V IX V RP Average level of volatility risk premium for TYVIX during the

period from the announcement to the auction. Volatility risk

premium is the difference between the realized volatility of

TY1 future prices during the time span [t, t − 30days] com-

puted as in Bollerslev et al., (2009) and the level of TYVIX

index at time t − 30. The formula is TY V IX V RPt =

TY V IXt −∑30

i=1(logTY 1t−i+1 − logTY 1t−i)2

avg V IX V RP Average level of volatility risk premium for VIX during the

period from the announcement to the auction. Volatility risk

premium is the difference between the realized volatility of

S&P500 during the time span [t, t − 30days] computed as

in Bollerslev et al., (2009) and the level of VIX index at

time t − 30. The formula is V IX V RPt = V IXt −∑30i=1(log SP500t−i+1 − log SP500t−i)

2

onoffspread Difference between the 10 year on-the-run yield and the 10

year off-the-run yield

QE dummy Indicator variable for an auction conducted during the three

waves of quantitative easing

35

Table 10: Description of variables used in the regressions

Variable Description

TAF dummy Indicator variable for an auction conducted during the period

in which FED carried out the term auction facility

Recession dummy Indicator variable for an auction conducted during periods of

recession (as identified by NBER)

Swap Swap rate of comparable tenor (max 6 month difference in

time-to-maturity) on the day of auction

Spread Difference in basis points between the auctioned yield and the

swap rate of comparable tenor (max 6 month difference in

time-to-maturity) on the auction day

Bid− to− cover ratio The demand indicator as reported by the department of the

Treasury

Transformed skewness The result of the transformation −log(

2Skewness+1 − 1

)(to

map Pearson’s skewness coefficient of bid distribution on the

real line)

Award ratio The ratio between the amount of bid accepted and awarded and

the total amount tendered

36

Construction of monetary policy dummies (QE and TAF)

Periods of time affected by a policy measure are delimited by the announcement dateand the declared conclusion date. The table below report the beginning and ending dateof quantitative easing and term auction facility.

Policy measure Beginning/ending date FED Press Release

TAF August 10, 2007 FOMC statement: The Federal Reserve is pro-viding liquidity to facilitate the orderly function-ing of financial markets (August 10, 2007)

March 8, 2010 Federal Reserve offers $25 billion in 28-daycredit through its Term Auction Facility (March8, 2010)

QE1 November 25, 2008 Federal Reserve announces it will initiate aprogram to purchase the direct obligationsof housing-related government-sponsored enter-prises and mortgage-backed securities backedby Fannie Mae, Freddie Mac, and Ginnie Mae(November 25, 2008)

March 31, 2010 FOMC Statement (March 16, 2010)

QE2 November 3, 2010 FOMC Statement (November 3, 2010)June 30, 2011 FOMC Statement (June 22, 2011)

QE3 September 13, 2012 FOMC Statement (September 13, 2012)October 31, 2014 FOMC Statement (October 29, 2014)

37

Table 11: Description of investor classes

Investor class Description

Depository Institutions Includes banks, savings and loan associations, credit unions,

and commercial bank investment accounts.

Individuals Includes individuals, partnerships, personal trusts, estates,

non-profit and taxexempt organizations, and foundations.

Pension Funds Includes pension and retirement funds, state & local pension

funds, life insurance companies, casualty and liability insur-

ance companies, and other insurance companies.

Investment Funds Includes mutual funds, money market funds, hedge funds,

money managers, and investment advisors.

Foreigners Includes private foreign entities, non-private foreign entities

placing tenders external of the Federal Reserve Bank of New

York (FRBNY), and official foreign entities placing tenders

through FRBNY.

Primary Dealers Includes institutions interacting directly with the FRBNY for

its open market operations.

Other Dealers Includes commercial bank dealer departments (non-primary),

and other non-bank dealers and brokers.

38

Figure 1: Time series of 7 day moving average (non-centered) of policy uncertainty index, TYVIX index, VIX index and volatility risk premium for VIX index.Events are taken from Baker et al., (2012) except those denoted with a star.

2005 2010 2015

010

020

030

0

Pol

icy

unce

rtai

nty

1 2 3 4 5 6 7 8 9

2005 2010 2015

05

1015

TY

VIX

1 2 3 4 5 6 7 8 9

2005 2010 2015

020

4060

VIX

1 2 3 4 5 6 7 8 9

2005 2010 2015−

40−

200

20

Vol

atili

ty r

isk

prem

ium

for

VIX

1 2 3 4 5 6 7 8 9

1. Gulf war II

2. Interest rate cut by FED

3. Lehman brothers collapse and TARP

4. Midterm election (2010)

5. Debt ceiling crisis (2010)

6. Fiscal cliff dispute*

7. Debt ceiling crisis (2013)*

8. Brexit*

9. Donald Trump election*

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Figure 2: Scatterplot of average level of policy uncertainty and net change in outstanding debtsecurities (issuances minus redemption). Trend line is computed via least squares.

50 100 150 200 250

−10

00

100

200

300

400

Monthly level of policy uncertainty

Mon

thly

cha

nge

in o

utst

andi

ng tr

easu

ry s

ecur

ities

(

in b

illio

ns o

f dol

lars

)

40

Figure 3: Cumulative amount tendered by each investor class

2008 2010 2012 2014 2016

500

1000

1500

2000

2500

3000

3500

Am

ount

tend

ered

(in

Bill

ions

)

Primary Dealers Foreigners Other dealers Funds Other bidders

41

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