- Email: [email protected]
Gilt auctions and secondary market dynamics
Gilt Auctions and Secondary Market Dynamics
Journal Pre-proof
Gilt Auctions and Secondary Market Dynamics Lucas Marc Fuhrer, Julia Giese PII: DOI: Reference:
S1544-6123(19)30913-4 https://doi.org/10.1016/j.frl.2019.101400 FRL 101400
To appear in:
Finance Research Letters
Received date: Revised date: Accepted date:
29 August 2019 3 December 2019 21 December 2019
Please cite this article as: Lucas Marc Fuhrer, Julia Giese, Gilt Auctions and Secondary Market Dynamics, Finance Research Letters (2019), doi: https://doi.org/10.1016/j.frl.2019.101400
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Highlights • We show how changes in investor demand for UK government bonds affect the shape of the yield curve.
• We find that surprises in investor demand persistently affect yields in particular at the long and short end of the curve and that this effect is more pronounced in volatile market conditions. Moreover, demand shocks transmit across the yield curve. • Our results provide evidence that investors in these maturities tend to be less pricesensitive, consistent with the existence of preferred habitat investors in those maturities.
1
Gilt Auctions and Secondary Market Dynamics∗ Lucas Marc Fuhrer Swiss National Bank
Julia Giese Bank of England
December 2019
Abstract This letter shows how changes in investor demand for United Kingdom government bonds, also called gilts, affect the shape of the yield curve. To clearly identify the impact of changes in investor demand, we analyse gilt auctions and find that surprises in investor demand, measured by deviations in the bid-to-cover ratio from its long-term average, persistently affect yields in particular at the long and short end of the curve and that this effect is more pronounced in volatile market conditions. Moreover, we show that demand shocks transmit across the yield curve, in particular to neighbouring bonds. JEL Classification: E43, E44, E52, G12 Keywords: Auctions, UK, gilts, yield curve
∗
The views expressed in this paper are those of the authors and do not necessarily reflect those of the Bank of England or the Swiss National Bank. We thank an anonymous referee, Nicholas Butt, Nick Govier, Nick Parish, Rhys Phillips, and Samuel Vigne (the editor) for helpful comments. Corresponding author e-mail address: [email protected]
2
1
Introduction
The sovereign yield curve is an important barometer of market sentiment and reflects interest rate expectations as well as different risk premia. Investors can purchase government bonds in the secondary market from other investors but also in the primary market directly from the government. One strand of the recent literature has documented that secondary market yields decline after more successful government bond auctions in several euro area countries and the United States (US) and that this effect is even more pronounced during volatile market conditions (see, e.g., Beetsma, Giuliodori, Hanson, and de Jong (2018) and Gorodnichenko and Ray (2017)). This effect is rationalized by the fact that the bid-to-cover ratio reveals relevant information for market participants about the value of the underlying assets. In this article, we are interested in understanding how changes in investor demand for United Kingdom (UK) government bonds, also called gilts, may affect bond yields, how they transmit across the yield curve and whether the effect differs in volatile and normal market conditions. Analysing market dynamics around gilt auctions is of particular interest because even though the UK gilt market is one of the largest government bond market in the world, there is little recent empirical literature. Moreover, after the financial crisis, the Bank of England purchased government bonds in its asset purchase programmes, which are thought to work partly through a portfolio rebalancing channel (see e.g. Haldane, Roberts-Sklar, Young, and Wieladek (2016)) potentially affecting investors’ demand. To identify the impact of changes in investor demand for gilts, we analyse market prices around gilt auctions. Doing so offers an ideal setup to identify demand shocks due to at least two reasons. First, all characteristics of the bond that will be issued are known by market participants in advance of the auction, in particular the issuance volume. Second, auctions offer an opportunity for investors to buy large quantities in one go. Consequently, price movements directly after the auction reflect information about demand by investors revealed in the auction, and may therefore be interpreted as demand shocks. The impact of a demand shock on bond yields and the transmission across the yield curve may depend on several factors. On the one hand, it may depend on the general risk environment. This would be in line with the literature explaining yield changes in secondary markets using theoretical models with asymmetric information (see e.g. Beetsma, Giuliodori, Hanson, and de Jong (2018)). Thereby, primary dealers use the private information about end-investor demand revealed in the auction process. This information affects secondary market yields during normal market conditions, but is even more valuable during volatile markets. On the other hand, it may depend on the price-sensitivity of different types of investors present in the maturity bucket of the issued bond. In particular, it may matter whether less price-sensitive investors with preferred habitats exist, so-called ”preferred habitat investors”. Preferred habitat investors value bonds with a different duration as not perfectly substitutable and thus require a compensation to adjust their portfolios in case
3
changes in demand occur, causing larger price changes for a given demand shock. The very existence of demand shocks would therefore be suggestive of the presence of preferred habitat investors as documented in the asset pricing literature (see e.g. Vayanos and Vila (2009), Greenwood and Vayanos (2010), and Greenwood and Vissing-Jorgensen (2018)). This literature sees exogenous changes in demand as coming from preferred habitat investors themselves, while nonpreferred habitat investors are seen as (risk-averse) arbitrageurs whose demand is determined endogenously. The key findings from our analysis are as follows. First, we find that demand shocks in gilt auctions, approximated by deviations in the bid-to-cover ratio from its long-term average, have persistent local impacts on yields, in particular at the long and short end of the yield curve. Thus, we provide empirical evidence that investors in longer- and shorter-term gilts tend to be less price-sensitive which could be consistent with the existence of preferred habitat investors in those maturities.1 Second, demand shocks have larger impacts on yields during periods with elevated volatile market conditions. This finding may indicate that during these periods, the information content revealed in the bid-to-cover ratio may be more important for market participants than during normal periods. Third, the impact of demand shocks transmits across the yield curve with neighbouring bonds being most affected, while the transmission to other bonds declines with the difference in the residual maturity. This finding is consistent with the predictions of the Vayanos and Vila (2009) model which suggests that demand effects become more localised the higher arbitrageurs risk aversion. In addition to the paper of Beetsma, Giuliodori, Hanson, and de Jong (2018) and Gorodnichenko and Ray (2017) the most related article to our letter is the analysis by Spindt and Stolz (1992) and Goldreich (2007) which document that the underpricing (i.e. the difference auction price versus the secondary market price) of US Treasury bills and bonds is smaller for auctions with a higher bid-to-cover ratio. For the UK, the literature analysing government bond auctions is relatively small.2 Breedon and Ganley (2000) and Ahmad and Steeley (2008) provide evidence for underpricing in gilt auctions while Ahmad and Steeley (2008) show that underpricˇ s (2018) ing depends on the amount of excess demand in the auction. Finally, Benos and Zikeˇ provide some interesting insight about the liquidity in the secondary gilt market. The remainder of this letter is structured as follows. Section 2 provides the important institution details about the gilt auctions and the gilt market, while Section 3 provides the empirical 1 Note that the effects are most pronounced at the long end of the curve. This could also be consistent with a duration premium channel as highlighted in Vayanos and Vila (2009) and Greenwood and Vayanos (2010). There is little hard data on where preferred habitat investors reside on the UK gilt curve. Pension funds and insurers may have preferences for longer dated gilts (see e.g. Debt Management Office (2012)) and are thus often claimed to be preferred habitat investors. Evidence is also emerging that a larger proportion of gilt holders in the long and short end exhibit preferred habitat characteristics than in the middle segment (Giese, Joyce, Meaning, and Worlidge, 2019). 2 Note that there exists a substantial literature on underpricing in government bond auctions (see e.g. Nyborg and Sundaresan (1996) and Keloharju, Nyborg, and Rydqvist (2005)) as well as on cycles in secondary market prices around auctions (see e.g. Duffie (2010) and Beetsma, Giuliodori, De Jong, and Widijanto (2016)).
4
analysis. Finally, Section 4 provides a summary and discusses potential policy implications.
2
Institutional background and data
The UK debt management office (DMO) regularly sells gilts on behalf of Her Majestys Treasury. This is done either by issuing new securities or increasing the outstanding volume of existing securities. The DMO issues two types of securities, conventional gilts and index-linked gilts, both of which are mostly issued through auctions (syndications are used on a less frequent basis). As of 2018, the largest investors in gilts by holdings are insurance companies and pension funds with about 30%, followed by overseas investors with about 28% and the Bank of England with 25%. This differs considerably from pre-crisis holdings, when insurance companies and pension funds held about 75% and overseas investors about 25%.3 For the purpose of this analysis, our focus is entirely on regular auctions for conventional gilts between 2003 and 2018, which represent the largest part of outstanding UK debt and new issuance.4 In gilt auctions, so-called Gilt-Edged Market Makers (GEMMs) can bid for an auctioned bond based on their own interest or on behalf of clients. GEMMs need to submit competitive bids and successful bidders are allotted gilts on a bid-price basis, paying the price they bid. The highest, lowest and average price of accepted bids are published immediately following the auction.5 Comparable to US Treasury auctions, all of the relevant information for the auctioned gilt, in particular the issuance volume is known by market participants several days in advance of the auction. For the empirical analysis, auction data are obtained from the DMO’s homepage while daily secondary market prices for all gilts are obtained from Bloomberg LTD. A widely used measure to approximate investor demand in government bond auctions is the auction’s bid-to-cover ratio (see e.g. Beetsma, Giuliodori, Hanson, and de Jong (2018)) which is also regularly used in the financial press.6 As revealed in Figure 1, investor demand in gilt auctions, measured by the bid-to-cover ratio, has been reasonably stable over time with some deviations from its long-term average in certain periods such as during 2015 and 2016. While the bid-to-cover ratio averages around 1.95 in short and medium gilt auctions, demand in long gilt auctions is lower with a ratio of around 1.77 (see Table 1).
3
Empirical analysis
The key objective of the analysis is to identify changes in demand for gilts and their implications for the yield curve. The best indicator to measure the impact of changes in investor demand for 3
See: https://www.dmo.gov.uk/data/gilt-market For example, in the financial year 2016/2017, the DMO sold 129.4 billion gilts of which the amount of conventional bonds issued via auctions amounted to 81.9 billion. 5 For more details see: https://www.dmo.gov.uk/media/15031/opnot010917.pdf 6 See Reuters article: ”Benchmark 5-year gilt sale finds strong demand”, 2 June 2010. 4
5
gilts is the yield change following the auction. When demand is strong (weak), the price for the bond will increase (decrease) and yields fall (increase). To measure yield changes we use daily yield changes of the auctioned gilt.7 To approximate investor demand in gilt auctions, we use the z-normalised bid-to-cover ratio, adjusting for the long-term mean and standard deviation of the respective maturity bucket, which we also call the bid-to-cover surprise. To analyse whether surprise in investor demand affects yields, we regress the daily yield change in the issued gilt (Y Ct ) on the bid-to-cover surprise variable (BtCtsurprise ). Y Ct = α + β1 ∗ BtCtsurprise + t
(1)
Table 2 Column 1 shows that surprises in the bid-to-cover ratio go along with corresponding yield changes.8 A positive (negative) surprise in the bid-to-cover ratio, i.e. deviations in the bidto-cover ratio from their respective long-term mean, is associated with falling (increasing) gilt yields. Moreover, Column 2–4 show that the results are particularly pronounced for long gilts, and to a lesser extent for short gilts, indicating that investors in these parts of the curve are less price sensitive than in the medium maturity. This fits with the presumption that investors in the long end of the curve, potentially preferred habitat investors, are less price-sensitive. We also analyse whether changes in demand have different effects during normal and volatile market conditions. To identify volatile market conditions, we use the CBOE Volatility Index (VIX) exceeding a level of 20 percentage points. Table 2 Column 5 and 6 suggests that yield changes due to demand shocks are more pronounced during volatile market conditions, a fact which is also documented for the European sovereign debt market (Beetsma, Giuliodori, Hanson, and de Jong, 2018). As a second analysis, we test whether yield effects resulting from surprises in investor demand are persistent or temporary. Table 3 illustrates the relationship between surprises in the bidto-cover ratio and gilt yields over different time windows, starting from a one-day change up to a five-day change. The table illustrates that the effects documented above are persistent in short and long gilts, while results for medium gilts diminish already after two days and become statistically insignificant. Besides having local effects, Gorodnichenko and Ray (2017) also find that changes in demand at one specific part of the curve transmit to the entire yield curve. Figure 2 illustrates the correlation of the gilt curve at different benchmark points to local demand shocks in a short, medium or long gilt auction. We find that demand shocks transmit to benchmark gilts with similar maturities almost one-to-one, whereas the transmission to other rates is smaller. For 7 Note that this differs compared to the analysis by Gorodnichenko and Ray (2017) which use intraday price changes in US Treasury futures with a comparable duration. We use daily price changes for the issued gilt as they are available from Bloomberg LTD for the entire sample period and gilt futures are only available for the 10 year maturity. We look at intraday prices for a sub-sample in a robustness check as discussed in the next section. 8 Note that our regression specification provides the same results as running a fixed effect panel regression using daily yield changes for all gilts (not reported in the article but available from the author upon request).
6
example, a one standard deviation shock in the bid-to-cover ratio in a long gilt auction affects the long end of the curve back to the 15-year benchmark by about -1.5 bps, while the transmission to short maturities gradually declines.9 A shock in a medium gilt auction affects the short as well as the long end of the gilt curve, while a shock in the short end only transmits up to the 10-year benchmark bond.
4
Robustness
Table 4 provides evidence that our findings are robust for a variety of regression specification. Column 1 shows that our findings from the baseline regression remain valid when using intraday yield changes from a 1.5 hours window around the gilt auction using a shorter observation period between 2011 and 2016.10 In Column 2 we included the auctions’ yield tail11 as an additional explanatory variable, while in Column 3 we control for yield changes in maturity matched German government bonds and changes in the GBPEUR exchange rate on the same day. For both regression specifications, the coefficient for the bid-to-cover surprise remains negative and statistically significant. Moreover, in Column 4 we show that our baseline findings remain also statistically significant when we include lagged dependent variables. Finally, we used two additional methods to identify surprises in the bid-to-cover ratio. First, we calculated the measure using a rolling (i.e. increasing) sample, using only historical data. Second, we calculated the measure without dividing by its standard deviation. Column 5 depicts regression results for the rolling specification, which are close to the finding in our baseline regression. For the specification based on the absolute deviation from the mean, the coefficient is statistically significant and negative (see Column 6), consistent with the findings in the baseline regression. It indicates that an increase in the bid-to-cover ratio (normalised by its mean) by one unit causes a 3.8 bps decline in yields.12
5
Conclusion and policy implications
In this article we show that changes in demand for gilts, affect the shape of the yield curve. We provide evidence that changes in the demand for gilts have persistent local effects on the yield curve, in particular at the long and short end of the curve. These effects transmit across the yield curve, with neighbouring bonds being most affected, while the transmission to other bonds declines with the difference in the residual maturity. 9
Note that the impact on issued bond is about -1.685 bps. For this specification, we make use of transaction level data derived from the ZEN dataset (see Benos and ˇ s (2018) for more details) and calculate yield changes for +/- 1.5 hours window around the auction close at Zikeˇ 10.30 a.m. 11 The yield tail is defined as the spread between the yield at the weighted average accepted price and the yield at the lowest accepted price at an auction. See DMO glossary: https://www.dmo.gov.uk/help/glossary 12 To put this into perspective: a change in the bid-to-cover ratio by one unit corresponds to about a three times change in the standard deviation (see Table 1). 10
7
From a policy perspective, our results are interesting at least for the following two reasons. First, the analysis contributes to our understanding of price dynamics in government bond markets, in particular around UK government bond auctions. We show that auctions may reveal new information about investor demand, and demand shocks can have a significant effect on gilt yields in particular during volatile market conditions which supports recent findings for other European markets by Beetsma, Giuliodori, Hanson, and de Jong (2018). Second, central banks’ asset purchase programmes are thought to work partly through a portfolio rebalancing channel (see e.g. Haldane, Roberts-Sklar, Young, and Wieladek (2016)). This channel relies on the existence of preferred habitat investors as they require compensation for substituting their portfolio. Our findings are consistent with the existence of preferred habitat investors in particular at the long and short end of the yield curve, as these parts of the curve tend to be less price sensitive.
8
References Ahmad, F., and J. M. Steeley, 2008, “Secondary market pricing behaviour around UK bond auctions”, Applied Financial Economics, 18(9), 691–699. Beetsma, R., M. Giuliodori, F. De Jong, and D. Widijanto, 2016, “Price effects of sovereign debt auctions in the euro-zone: The role of the crisis”, Journal of Financial Intermediation, 25, 30–53. Beetsma, R., M. Giuliodori, J. Hanson, and F. de Jong, 2018, “Bid-to-cover and yield changes around public debt auctions in the euro area”, Journal of Banking & Finance, 87, 118–134. ˇ s, 2018, “Funding constraints and liquidity in two-tiered OTC markets”, Benos, E., and F. Zikeˇ Journal of Financial Markets, 39, 24–43. Breedon, F., and J. Ganley, 2000, “Bidding and Information: Evidence from Gilt-edged Auctions”, The Economic Journal, 110(466), 963–984. Debt Management Office, 2012, “Launch of a consultation on the potential issuance of ’superlong’ and/or perpetual gilts”, Press notice: 25 May 2012. Duffie, D., 2010, “Presidential address: Asset price dynamics with slow-moving capital”, The Journal of Finance, 65(4), 1237–1267. Giese, J., M. Joyce, J. Meaning, and J. Worlidge, 2019, “Preferred habitat investors in the UK government bond market”, Mimeo. Goldreich, D., 2007, “Underpricing in discriminatory and uniform-price Treasury auctions”, Journal of Financial and Quantitative Analysis, 42(2), 443–466. Gorodnichenko, Y., and W. Ray, 2017, “The effects of quantitative easing: Taking a cue from treasury auctions”, NBER Working Paper, No. 24122. Greenwood, R., and D. Vayanos, 2010, “Price pressure in the government bond market”, American Economic Review, 100(2), 585–90. Greenwood, R. M., and A. Vissing-Jorgensen, 2018, “The impact of pensions and insurance on global yield curves”, Harvard Business School Working Paper, No. 18-109. Haldane, A., M. Roberts-Sklar, C. Young, and T. Wieladek, 2016, “QE: the story so far”, Bank of England Working Paper, No. 624. Keloharju, M., K. G. Nyborg, and K. Rydqvist, 2005, “Strategic behavior and underpricing in uniform price auctions: Evidence from Finnish treasury auctions”, The Journal of Finance, 60(4), 1865–1902. Nyborg, K. G., and S. Sundaresan, 1996, “Discriminatory versus uniform Treasury auctions: Evidence from when-issued transactions”, Journal of Financial Economics, 42(1), 63–104. Spindt, P. A., and R. W. Stolz, 1992, “Are US Treasury bills underpriced in the primary market?”, Journal of Banking & Finance, 16(5), 891–908. Vayanos, D., and J.-L. Vila, 2009, “A preferred-habitat model of the term structure of interest rates”, NBER Working Paper, No. 15487. 9
A
Appendix Figure 1: Bid-to-cover ratio
Figure 1 depicts the bid-to-cover ratio of gilt auctions. Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. The sample period lasts from 1.1.2003 to 31.12.2018.
10
Figure 2: Transmission of demand shocks
Figure 2 shows the reaction of the gilt curve (in basis points) to a one standard deviation shock in the bid-to-cover ratio of a short, medium and long gilts auction. 95% confidence intervals (Newey-West) are depicted with dotted lines. Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. The sample period lasts from 1.1.2003 to 31.12.2018.
11
Table 1: Descriptive statistics
mean
p50
sd
min
max
YC
-0.193
0.000
5.044
-14.000
21.000
count 430
BtC
1.885
1.850
0.368
0.930
3.220
439
BtC surprise
-0.000
-0.087
0.998
-2.955
2.924
439
BtC short
1.948
1.940
0.444
1.070
3.220
148
BtC medium
1.949
1.930
0.329
1.190
2.910
137
BtC long
1.766
1.725
0.283
0.930
2.510
154
Table 1 provides descriptive statistics for our key variables. Yield changes (in basis points) are calculated as the difference between the secondary market yields on the auction day minus the yields of the previous day. The bidto-cover ratio surprise variable is the z-normalised bid-to-cover ratio for the specific maturity bucket (i.e. normalization to zero mean and unit standard deviation). Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. The sample period lasts from 1.1.2003 to 31.12.2018.
Table 2: Regression analysis
BtC surprise Constant
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC
YC
YC
YC
YC
-1.39∗∗∗
-1.47∗∗∗
-0.96∗∗
-1.68∗∗∗
-0.76∗∗∗
-2.39∗∗∗
(0.25)
(0.38)
(0.49)
(0.36)
(0.26)
(0.45)
-0.20
0.24
-0.71
-0.19
0.03
-0.64
(0.24)
(0.41)
(0.44)
(0.39)
(0.27)
(0.43)
Observations
430
147
129
154
282
148
Adjusted R2
0.073
0.079
0.028
0.106
0.025
0.163
VIX level
all
all
all
all
low
high
Maturity bucket
all
short
medium
long
all
all
Table 2 shows our regression results. The independent variable is the daily yield change in the auctioneered gilt (in basis points). Yield changes are calculated as the difference between the secondary market yields on the auction day minus the yields of the previous day. The bid-to-cover ratio surprise variable is the z-normalised bid-to-cover ratio for the specific maturity bucket (i.e. normalization to zero mean and unit standard deviation). Volatile market conditions are defined as days with a VIX level above 20 percentage points. Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. Heteroscedasticity and autocorrelation robust standard errors are reported (in parentheses), using the Newey and West (1987) correction. ***, ** and * denote statistical significance (two-tailed) at the 1%, 5%, and 10% significance levels, respectively. The sample period lasts from 1.1.2003 to 31.12.2018.
12
Table 3: Persistency of yield changes
BtC surprise Constant
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC 1 day
YC 2 days
YC 3 days
YC 4 days
YC 5 days
-1.47∗∗∗
-1.91∗∗∗
-2.46∗∗∗
-2.22∗∗∗
-2.03∗∗
-2.17∗∗∗
(0.38)
(0.53)
(0.66)
(0.79)
(0.87)
(0.83)
0.24
-0.14
-0.82
-2.08∗∗
-2.38∗∗
-1.97∗
(0.41)
(0.67)
(0.81)
(0.99)
(1.16)
(1.18)
Observations
147
147
147
147
147
147
Adjusted R2
0.079
0.056
0.063
0.035
0.022
0.025
BtC surprise Constant
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC 1 day
YC 2 days
YC 3 days
YC 4 days
YC 5 days
-0.96∗∗
-1.61∗∗∗
-1.40∗∗
-0.81
-0.88
-0.08
(0.49)
(0.62)
(0.64)
(0.75)
(0.86)
(0.89)
-0.71
-1.03∗
-1.33∗∗
-1.48∗∗
-1.76∗∗
-1.99∗∗ (0.93)
(0.44)
(0.53)
(0.63)
(0.73)
(0.83)
Observations
129
129
129
129
129
129
Adjusted R2
0.028
0.052
0.023
0.000
-0.001
-0.008
BtC surprise Constant
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC 1 day
YC 2 days
YC 3 days
YC 4 days
YC 5 days
-1.68∗∗∗
-1.76∗∗∗
-1.86∗∗∗
-1.84∗∗∗
-1.50∗∗
-1.49∗
(0.36)
(0.42)
(0.61)
(0.67)
(0.68)
(0.81)
-0.19
-0.51
-1.22∗∗
-1.45∗∗
-1.40∗∗
-1.79∗∗ (0.77)
(0.39)
(0.44)
(0.59)
(0.68)
(0.70)
Observations
154
154
154
154
154
154
Adjusted R2
0.106
0.086
0.052
0.043
0.022
0.016
Table 3 provides the response of short, medium and long gilt yields to a one standard deviation change in the bid-to-cover ratio using different window sizes. Yield changes are calculated as the difference between the secondary market yields on the day t (t=0 corresponds to the auction day) minus the yields of the day prior to the gilt auction. Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. Heteroscedasticity and autocorrelation robust standard errors are reported (in parentheses), using the Newey and West (1987) correction. ***, ** and * denote statistical significance (two-tailed) at the 1%, 5%, and 10% significance levels, respectively. The sample period lasts from 1.1.2003 to 31.12.2018.
13
Table 4: Alternative regression specifications
BtC surprise
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC
YC
YC
YC
YC
-0.72∗∗∗
-1.46∗∗∗
-1.40∗∗∗
-1.31∗∗∗
(0.11)
(0.30)
(0.25)
(0.25)
Tail
-0.25 (0.38)
YC German gov. bond
0.13∗∗
GBPEUR (change in %)
1.39∗∗∗
(0.05) (0.51) YC lag 1 day
-0.04 (0.06) -1.32∗∗∗
BtC surprise (rolling)
(0.21) -3.78∗∗∗
BtC surprise (abs deviation)
(0.70) Constant
-0.18
-0.05
-0.24
-0.17
-0.41∗
-0.20
(0.11)
(0.35)
(0.23)
(0.24)
(0.23)
(0.24)
Observations
183
430
430
420
424
430
Adjusted R2
0.181
0.073
0.107
0.066
0.086
0.070
Table 4 shows the regression results using alternative specifications. Column 1 depicts the regression results using intraday yield changes (1.5 hours window around the gilt auction) for a shorter observation period between 2011 and 2016. The yield tail is defined as the spread (in bps) between the yield at the weighted average accepted price and the yield at the lowest accepted price at an auction. Yield changes in maturity matched German government bonds are in bps and changes GBPEUR are in %. The BtC surprise (rolling) uses only historical data for the normalisation of the variable, while the BtC surprise (abs deviation) is normalised only by subtracting the sample mean. Heteroscedasticity and autocorrelation robust standard errors are reported (in parentheses), using the Newey and West (1987) correction. ***, ** and * denote statistical significance (two-tailed) at the 1%, 5%, and 10% significance levels, respectively. The sample period lasts from 1.1.2003 to 31.12.2018.
14
Author Statement Both authors contributed equally to the article.
15
Journal Pre-proof
Gilt Auctions and Secondary Market Dynamics Lucas Marc Fuhrer, Julia Giese PII: DOI: Reference:
S1544-6123(19)30913-4 https://doi.org/10.1016/j.frl.2019.101400 FRL 101400
To appear in:
Finance Research Letters
Received date: Revised date: Accepted date:
29 August 2019 3 December 2019 21 December 2019
Please cite this article as: Lucas Marc Fuhrer, Julia Giese, Gilt Auctions and Secondary Market Dynamics, Finance Research Letters (2019), doi: https://doi.org/10.1016/j.frl.2019.101400
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Highlights • We show how changes in investor demand for UK government bonds affect the shape of the yield curve.
• We find that surprises in investor demand persistently affect yields in particular at the long and short end of the curve and that this effect is more pronounced in volatile market conditions. Moreover, demand shocks transmit across the yield curve. • Our results provide evidence that investors in these maturities tend to be less pricesensitive, consistent with the existence of preferred habitat investors in those maturities.
1
Gilt Auctions and Secondary Market Dynamics∗ Lucas Marc Fuhrer Swiss National Bank
Julia Giese Bank of England
December 2019
Abstract This letter shows how changes in investor demand for United Kingdom government bonds, also called gilts, affect the shape of the yield curve. To clearly identify the impact of changes in investor demand, we analyse gilt auctions and find that surprises in investor demand, measured by deviations in the bid-to-cover ratio from its long-term average, persistently affect yields in particular at the long and short end of the curve and that this effect is more pronounced in volatile market conditions. Moreover, we show that demand shocks transmit across the yield curve, in particular to neighbouring bonds. JEL Classification: E43, E44, E52, G12 Keywords: Auctions, UK, gilts, yield curve
∗
The views expressed in this paper are those of the authors and do not necessarily reflect those of the Bank of England or the Swiss National Bank. We thank an anonymous referee, Nicholas Butt, Nick Govier, Nick Parish, Rhys Phillips, and Samuel Vigne (the editor) for helpful comments. Corresponding author e-mail address: [email protected]
2
1
Introduction
The sovereign yield curve is an important barometer of market sentiment and reflects interest rate expectations as well as different risk premia. Investors can purchase government bonds in the secondary market from other investors but also in the primary market directly from the government. One strand of the recent literature has documented that secondary market yields decline after more successful government bond auctions in several euro area countries and the United States (US) and that this effect is even more pronounced during volatile market conditions (see, e.g., Beetsma, Giuliodori, Hanson, and de Jong (2018) and Gorodnichenko and Ray (2017)). This effect is rationalized by the fact that the bid-to-cover ratio reveals relevant information for market participants about the value of the underlying assets. In this article, we are interested in understanding how changes in investor demand for United Kingdom (UK) government bonds, also called gilts, may affect bond yields, how they transmit across the yield curve and whether the effect differs in volatile and normal market conditions. Analysing market dynamics around gilt auctions is of particular interest because even though the UK gilt market is one of the largest government bond market in the world, there is little recent empirical literature. Moreover, after the financial crisis, the Bank of England purchased government bonds in its asset purchase programmes, which are thought to work partly through a portfolio rebalancing channel (see e.g. Haldane, Roberts-Sklar, Young, and Wieladek (2016)) potentially affecting investors’ demand. To identify the impact of changes in investor demand for gilts, we analyse market prices around gilt auctions. Doing so offers an ideal setup to identify demand shocks due to at least two reasons. First, all characteristics of the bond that will be issued are known by market participants in advance of the auction, in particular the issuance volume. Second, auctions offer an opportunity for investors to buy large quantities in one go. Consequently, price movements directly after the auction reflect information about demand by investors revealed in the auction, and may therefore be interpreted as demand shocks. The impact of a demand shock on bond yields and the transmission across the yield curve may depend on several factors. On the one hand, it may depend on the general risk environment. This would be in line with the literature explaining yield changes in secondary markets using theoretical models with asymmetric information (see e.g. Beetsma, Giuliodori, Hanson, and de Jong (2018)). Thereby, primary dealers use the private information about end-investor demand revealed in the auction process. This information affects secondary market yields during normal market conditions, but is even more valuable during volatile markets. On the other hand, it may depend on the price-sensitivity of different types of investors present in the maturity bucket of the issued bond. In particular, it may matter whether less price-sensitive investors with preferred habitats exist, so-called ”preferred habitat investors”. Preferred habitat investors value bonds with a different duration as not perfectly substitutable and thus require a compensation to adjust their portfolios in case
3
changes in demand occur, causing larger price changes for a given demand shock. The very existence of demand shocks would therefore be suggestive of the presence of preferred habitat investors as documented in the asset pricing literature (see e.g. Vayanos and Vila (2009), Greenwood and Vayanos (2010), and Greenwood and Vissing-Jorgensen (2018)). This literature sees exogenous changes in demand as coming from preferred habitat investors themselves, while nonpreferred habitat investors are seen as (risk-averse) arbitrageurs whose demand is determined endogenously. The key findings from our analysis are as follows. First, we find that demand shocks in gilt auctions, approximated by deviations in the bid-to-cover ratio from its long-term average, have persistent local impacts on yields, in particular at the long and short end of the yield curve. Thus, we provide empirical evidence that investors in longer- and shorter-term gilts tend to be less price-sensitive which could be consistent with the existence of preferred habitat investors in those maturities.1 Second, demand shocks have larger impacts on yields during periods with elevated volatile market conditions. This finding may indicate that during these periods, the information content revealed in the bid-to-cover ratio may be more important for market participants than during normal periods. Third, the impact of demand shocks transmits across the yield curve with neighbouring bonds being most affected, while the transmission to other bonds declines with the difference in the residual maturity. This finding is consistent with the predictions of the Vayanos and Vila (2009) model which suggests that demand effects become more localised the higher arbitrageurs risk aversion. In addition to the paper of Beetsma, Giuliodori, Hanson, and de Jong (2018) and Gorodnichenko and Ray (2017) the most related article to our letter is the analysis by Spindt and Stolz (1992) and Goldreich (2007) which document that the underpricing (i.e. the difference auction price versus the secondary market price) of US Treasury bills and bonds is smaller for auctions with a higher bid-to-cover ratio. For the UK, the literature analysing government bond auctions is relatively small.2 Breedon and Ganley (2000) and Ahmad and Steeley (2008) provide evidence for underpricing in gilt auctions while Ahmad and Steeley (2008) show that underpricˇ s (2018) ing depends on the amount of excess demand in the auction. Finally, Benos and Zikeˇ provide some interesting insight about the liquidity in the secondary gilt market. The remainder of this letter is structured as follows. Section 2 provides the important institution details about the gilt auctions and the gilt market, while Section 3 provides the empirical 1 Note that the effects are most pronounced at the long end of the curve. This could also be consistent with a duration premium channel as highlighted in Vayanos and Vila (2009) and Greenwood and Vayanos (2010). There is little hard data on where preferred habitat investors reside on the UK gilt curve. Pension funds and insurers may have preferences for longer dated gilts (see e.g. Debt Management Office (2012)) and are thus often claimed to be preferred habitat investors. Evidence is also emerging that a larger proportion of gilt holders in the long and short end exhibit preferred habitat characteristics than in the middle segment (Giese, Joyce, Meaning, and Worlidge, 2019). 2 Note that there exists a substantial literature on underpricing in government bond auctions (see e.g. Nyborg and Sundaresan (1996) and Keloharju, Nyborg, and Rydqvist (2005)) as well as on cycles in secondary market prices around auctions (see e.g. Duffie (2010) and Beetsma, Giuliodori, De Jong, and Widijanto (2016)).
4
analysis. Finally, Section 4 provides a summary and discusses potential policy implications.
2
Institutional background and data
The UK debt management office (DMO) regularly sells gilts on behalf of Her Majestys Treasury. This is done either by issuing new securities or increasing the outstanding volume of existing securities. The DMO issues two types of securities, conventional gilts and index-linked gilts, both of which are mostly issued through auctions (syndications are used on a less frequent basis). As of 2018, the largest investors in gilts by holdings are insurance companies and pension funds with about 30%, followed by overseas investors with about 28% and the Bank of England with 25%. This differs considerably from pre-crisis holdings, when insurance companies and pension funds held about 75% and overseas investors about 25%.3 For the purpose of this analysis, our focus is entirely on regular auctions for conventional gilts between 2003 and 2018, which represent the largest part of outstanding UK debt and new issuance.4 In gilt auctions, so-called Gilt-Edged Market Makers (GEMMs) can bid for an auctioned bond based on their own interest or on behalf of clients. GEMMs need to submit competitive bids and successful bidders are allotted gilts on a bid-price basis, paying the price they bid. The highest, lowest and average price of accepted bids are published immediately following the auction.5 Comparable to US Treasury auctions, all of the relevant information for the auctioned gilt, in particular the issuance volume is known by market participants several days in advance of the auction. For the empirical analysis, auction data are obtained from the DMO’s homepage while daily secondary market prices for all gilts are obtained from Bloomberg LTD. A widely used measure to approximate investor demand in government bond auctions is the auction’s bid-to-cover ratio (see e.g. Beetsma, Giuliodori, Hanson, and de Jong (2018)) which is also regularly used in the financial press.6 As revealed in Figure 1, investor demand in gilt auctions, measured by the bid-to-cover ratio, has been reasonably stable over time with some deviations from its long-term average in certain periods such as during 2015 and 2016. While the bid-to-cover ratio averages around 1.95 in short and medium gilt auctions, demand in long gilt auctions is lower with a ratio of around 1.77 (see Table 1).
3
Empirical analysis
The key objective of the analysis is to identify changes in demand for gilts and their implications for the yield curve. The best indicator to measure the impact of changes in investor demand for 3
See: https://www.dmo.gov.uk/data/gilt-market For example, in the financial year 2016/2017, the DMO sold 129.4 billion gilts of which the amount of conventional bonds issued via auctions amounted to 81.9 billion. 5 For more details see: https://www.dmo.gov.uk/media/15031/opnot010917.pdf 6 See Reuters article: ”Benchmark 5-year gilt sale finds strong demand”, 2 June 2010. 4
5
gilts is the yield change following the auction. When demand is strong (weak), the price for the bond will increase (decrease) and yields fall (increase). To measure yield changes we use daily yield changes of the auctioned gilt.7 To approximate investor demand in gilt auctions, we use the z-normalised bid-to-cover ratio, adjusting for the long-term mean and standard deviation of the respective maturity bucket, which we also call the bid-to-cover surprise. To analyse whether surprise in investor demand affects yields, we regress the daily yield change in the issued gilt (Y Ct ) on the bid-to-cover surprise variable (BtCtsurprise ). Y Ct = α + β1 ∗ BtCtsurprise + t
(1)
Table 2 Column 1 shows that surprises in the bid-to-cover ratio go along with corresponding yield changes.8 A positive (negative) surprise in the bid-to-cover ratio, i.e. deviations in the bidto-cover ratio from their respective long-term mean, is associated with falling (increasing) gilt yields. Moreover, Column 2–4 show that the results are particularly pronounced for long gilts, and to a lesser extent for short gilts, indicating that investors in these parts of the curve are less price sensitive than in the medium maturity. This fits with the presumption that investors in the long end of the curve, potentially preferred habitat investors, are less price-sensitive. We also analyse whether changes in demand have different effects during normal and volatile market conditions. To identify volatile market conditions, we use the CBOE Volatility Index (VIX) exceeding a level of 20 percentage points. Table 2 Column 5 and 6 suggests that yield changes due to demand shocks are more pronounced during volatile market conditions, a fact which is also documented for the European sovereign debt market (Beetsma, Giuliodori, Hanson, and de Jong, 2018). As a second analysis, we test whether yield effects resulting from surprises in investor demand are persistent or temporary. Table 3 illustrates the relationship between surprises in the bidto-cover ratio and gilt yields over different time windows, starting from a one-day change up to a five-day change. The table illustrates that the effects documented above are persistent in short and long gilts, while results for medium gilts diminish already after two days and become statistically insignificant. Besides having local effects, Gorodnichenko and Ray (2017) also find that changes in demand at one specific part of the curve transmit to the entire yield curve. Figure 2 illustrates the correlation of the gilt curve at different benchmark points to local demand shocks in a short, medium or long gilt auction. We find that demand shocks transmit to benchmark gilts with similar maturities almost one-to-one, whereas the transmission to other rates is smaller. For 7 Note that this differs compared to the analysis by Gorodnichenko and Ray (2017) which use intraday price changes in US Treasury futures with a comparable duration. We use daily price changes for the issued gilt as they are available from Bloomberg LTD for the entire sample period and gilt futures are only available for the 10 year maturity. We look at intraday prices for a sub-sample in a robustness check as discussed in the next section. 8 Note that our regression specification provides the same results as running a fixed effect panel regression using daily yield changes for all gilts (not reported in the article but available from the author upon request).
6
example, a one standard deviation shock in the bid-to-cover ratio in a long gilt auction affects the long end of the curve back to the 15-year benchmark by about -1.5 bps, while the transmission to short maturities gradually declines.9 A shock in a medium gilt auction affects the short as well as the long end of the gilt curve, while a shock in the short end only transmits up to the 10-year benchmark bond.
4
Robustness
Table 4 provides evidence that our findings are robust for a variety of regression specification. Column 1 shows that our findings from the baseline regression remain valid when using intraday yield changes from a 1.5 hours window around the gilt auction using a shorter observation period between 2011 and 2016.10 In Column 2 we included the auctions’ yield tail11 as an additional explanatory variable, while in Column 3 we control for yield changes in maturity matched German government bonds and changes in the GBPEUR exchange rate on the same day. For both regression specifications, the coefficient for the bid-to-cover surprise remains negative and statistically significant. Moreover, in Column 4 we show that our baseline findings remain also statistically significant when we include lagged dependent variables. Finally, we used two additional methods to identify surprises in the bid-to-cover ratio. First, we calculated the measure using a rolling (i.e. increasing) sample, using only historical data. Second, we calculated the measure without dividing by its standard deviation. Column 5 depicts regression results for the rolling specification, which are close to the finding in our baseline regression. For the specification based on the absolute deviation from the mean, the coefficient is statistically significant and negative (see Column 6), consistent with the findings in the baseline regression. It indicates that an increase in the bid-to-cover ratio (normalised by its mean) by one unit causes a 3.8 bps decline in yields.12
5
Conclusion and policy implications
In this article we show that changes in demand for gilts, affect the shape of the yield curve. We provide evidence that changes in the demand for gilts have persistent local effects on the yield curve, in particular at the long and short end of the curve. These effects transmit across the yield curve, with neighbouring bonds being most affected, while the transmission to other bonds declines with the difference in the residual maturity. 9
Note that the impact on issued bond is about -1.685 bps. For this specification, we make use of transaction level data derived from the ZEN dataset (see Benos and ˇ s (2018) for more details) and calculate yield changes for +/- 1.5 hours window around the auction close at Zikeˇ 10.30 a.m. 11 The yield tail is defined as the spread between the yield at the weighted average accepted price and the yield at the lowest accepted price at an auction. See DMO glossary: https://www.dmo.gov.uk/help/glossary 12 To put this into perspective: a change in the bid-to-cover ratio by one unit corresponds to about a three times change in the standard deviation (see Table 1). 10
7
From a policy perspective, our results are interesting at least for the following two reasons. First, the analysis contributes to our understanding of price dynamics in government bond markets, in particular around UK government bond auctions. We show that auctions may reveal new information about investor demand, and demand shocks can have a significant effect on gilt yields in particular during volatile market conditions which supports recent findings for other European markets by Beetsma, Giuliodori, Hanson, and de Jong (2018). Second, central banks’ asset purchase programmes are thought to work partly through a portfolio rebalancing channel (see e.g. Haldane, Roberts-Sklar, Young, and Wieladek (2016)). This channel relies on the existence of preferred habitat investors as they require compensation for substituting their portfolio. Our findings are consistent with the existence of preferred habitat investors in particular at the long and short end of the yield curve, as these parts of the curve tend to be less price sensitive.
8
References Ahmad, F., and J. M. Steeley, 2008, “Secondary market pricing behaviour around UK bond auctions”, Applied Financial Economics, 18(9), 691–699. Beetsma, R., M. Giuliodori, F. De Jong, and D. Widijanto, 2016, “Price effects of sovereign debt auctions in the euro-zone: The role of the crisis”, Journal of Financial Intermediation, 25, 30–53. Beetsma, R., M. Giuliodori, J. Hanson, and F. de Jong, 2018, “Bid-to-cover and yield changes around public debt auctions in the euro area”, Journal of Banking & Finance, 87, 118–134. ˇ s, 2018, “Funding constraints and liquidity in two-tiered OTC markets”, Benos, E., and F. Zikeˇ Journal of Financial Markets, 39, 24–43. Breedon, F., and J. Ganley, 2000, “Bidding and Information: Evidence from Gilt-edged Auctions”, The Economic Journal, 110(466), 963–984. Debt Management Office, 2012, “Launch of a consultation on the potential issuance of ’superlong’ and/or perpetual gilts”, Press notice: 25 May 2012. Duffie, D., 2010, “Presidential address: Asset price dynamics with slow-moving capital”, The Journal of Finance, 65(4), 1237–1267. Giese, J., M. Joyce, J. Meaning, and J. Worlidge, 2019, “Preferred habitat investors in the UK government bond market”, Mimeo. Goldreich, D., 2007, “Underpricing in discriminatory and uniform-price Treasury auctions”, Journal of Financial and Quantitative Analysis, 42(2), 443–466. Gorodnichenko, Y., and W. Ray, 2017, “The effects of quantitative easing: Taking a cue from treasury auctions”, NBER Working Paper, No. 24122. Greenwood, R., and D. Vayanos, 2010, “Price pressure in the government bond market”, American Economic Review, 100(2), 585–90. Greenwood, R. M., and A. Vissing-Jorgensen, 2018, “The impact of pensions and insurance on global yield curves”, Harvard Business School Working Paper, No. 18-109. Haldane, A., M. Roberts-Sklar, C. Young, and T. Wieladek, 2016, “QE: the story so far”, Bank of England Working Paper, No. 624. Keloharju, M., K. G. Nyborg, and K. Rydqvist, 2005, “Strategic behavior and underpricing in uniform price auctions: Evidence from Finnish treasury auctions”, The Journal of Finance, 60(4), 1865–1902. Nyborg, K. G., and S. Sundaresan, 1996, “Discriminatory versus uniform Treasury auctions: Evidence from when-issued transactions”, Journal of Financial Economics, 42(1), 63–104. Spindt, P. A., and R. W. Stolz, 1992, “Are US Treasury bills underpriced in the primary market?”, Journal of Banking & Finance, 16(5), 891–908. Vayanos, D., and J.-L. Vila, 2009, “A preferred-habitat model of the term structure of interest rates”, NBER Working Paper, No. 15487. 9
A
Appendix Figure 1: Bid-to-cover ratio
Figure 1 depicts the bid-to-cover ratio of gilt auctions. Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. The sample period lasts from 1.1.2003 to 31.12.2018.
10
Figure 2: Transmission of demand shocks
Figure 2 shows the reaction of the gilt curve (in basis points) to a one standard deviation shock in the bid-to-cover ratio of a short, medium and long gilts auction. 95% confidence intervals (Newey-West) are depicted with dotted lines. Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. The sample period lasts from 1.1.2003 to 31.12.2018.
11
Table 1: Descriptive statistics
mean
p50
sd
min
max
YC
-0.193
0.000
5.044
-14.000
21.000
count 430
BtC
1.885
1.850
0.368
0.930
3.220
439
BtC surprise
-0.000
-0.087
0.998
-2.955
2.924
439
BtC short
1.948
1.940
0.444
1.070
3.220
148
BtC medium
1.949
1.930
0.329
1.190
2.910
137
BtC long
1.766
1.725
0.283
0.930
2.510
154
Table 1 provides descriptive statistics for our key variables. Yield changes (in basis points) are calculated as the difference between the secondary market yields on the auction day minus the yields of the previous day. The bidto-cover ratio surprise variable is the z-normalised bid-to-cover ratio for the specific maturity bucket (i.e. normalization to zero mean and unit standard deviation). Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. The sample period lasts from 1.1.2003 to 31.12.2018.
Table 2: Regression analysis
BtC surprise Constant
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC
YC
YC
YC
YC
-1.39∗∗∗
-1.47∗∗∗
-0.96∗∗
-1.68∗∗∗
-0.76∗∗∗
-2.39∗∗∗
(0.25)
(0.38)
(0.49)
(0.36)
(0.26)
(0.45)
-0.20
0.24
-0.71
-0.19
0.03
-0.64
(0.24)
(0.41)
(0.44)
(0.39)
(0.27)
(0.43)
Observations
430
147
129
154
282
148
Adjusted R2
0.073
0.079
0.028
0.106
0.025
0.163
VIX level
all
all
all
all
low
high
Maturity bucket
all
short
medium
long
all
all
Table 2 shows our regression results. The independent variable is the daily yield change in the auctioneered gilt (in basis points). Yield changes are calculated as the difference between the secondary market yields on the auction day minus the yields of the previous day. The bid-to-cover ratio surprise variable is the z-normalised bid-to-cover ratio for the specific maturity bucket (i.e. normalization to zero mean and unit standard deviation). Volatile market conditions are defined as days with a VIX level above 20 percentage points. Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. Heteroscedasticity and autocorrelation robust standard errors are reported (in parentheses), using the Newey and West (1987) correction. ***, ** and * denote statistical significance (two-tailed) at the 1%, 5%, and 10% significance levels, respectively. The sample period lasts from 1.1.2003 to 31.12.2018.
12
Table 3: Persistency of yield changes
BtC surprise Constant
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC 1 day
YC 2 days
YC 3 days
YC 4 days
YC 5 days
-1.47∗∗∗
-1.91∗∗∗
-2.46∗∗∗
-2.22∗∗∗
-2.03∗∗
-2.17∗∗∗
(0.38)
(0.53)
(0.66)
(0.79)
(0.87)
(0.83)
0.24
-0.14
-0.82
-2.08∗∗
-2.38∗∗
-1.97∗
(0.41)
(0.67)
(0.81)
(0.99)
(1.16)
(1.18)
Observations
147
147
147
147
147
147
Adjusted R2
0.079
0.056
0.063
0.035
0.022
0.025
BtC surprise Constant
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC 1 day
YC 2 days
YC 3 days
YC 4 days
YC 5 days
-0.96∗∗
-1.61∗∗∗
-1.40∗∗
-0.81
-0.88
-0.08
(0.49)
(0.62)
(0.64)
(0.75)
(0.86)
(0.89)
-0.71
-1.03∗
-1.33∗∗
-1.48∗∗
-1.76∗∗
-1.99∗∗ (0.93)
(0.44)
(0.53)
(0.63)
(0.73)
(0.83)
Observations
129
129
129
129
129
129
Adjusted R2
0.028
0.052
0.023
0.000
-0.001
-0.008
BtC surprise Constant
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC 1 day
YC 2 days
YC 3 days
YC 4 days
YC 5 days
-1.68∗∗∗
-1.76∗∗∗
-1.86∗∗∗
-1.84∗∗∗
-1.50∗∗
-1.49∗
(0.36)
(0.42)
(0.61)
(0.67)
(0.68)
(0.81)
-0.19
-0.51
-1.22∗∗
-1.45∗∗
-1.40∗∗
-1.79∗∗ (0.77)
(0.39)
(0.44)
(0.59)
(0.68)
(0.70)
Observations
154
154
154
154
154
154
Adjusted R2
0.106
0.086
0.052
0.043
0.022
0.016
Table 3 provides the response of short, medium and long gilt yields to a one standard deviation change in the bid-to-cover ratio using different window sizes. Yield changes are calculated as the difference between the secondary market yields on the day t (t=0 corresponds to the auction day) minus the yields of the day prior to the gilt auction. Auctions are classified according to maturity buckets: short: 0-7 years; medium: 7-15 years; long: 15 years or more. Heteroscedasticity and autocorrelation robust standard errors are reported (in parentheses), using the Newey and West (1987) correction. ***, ** and * denote statistical significance (two-tailed) at the 1%, 5%, and 10% significance levels, respectively. The sample period lasts from 1.1.2003 to 31.12.2018.
13
Table 4: Alternative regression specifications
BtC surprise
(1)
(2)
(3)
(4)
(5)
(6)
YC
YC
YC
YC
YC
YC
-0.72∗∗∗
-1.46∗∗∗
-1.40∗∗∗
-1.31∗∗∗
(0.11)
(0.30)
(0.25)
(0.25)
Tail
-0.25 (0.38)
YC German gov. bond
0.13∗∗
GBPEUR (change in %)
1.39∗∗∗
(0.05) (0.51) YC lag 1 day
-0.04 (0.06) -1.32∗∗∗
BtC surprise (rolling)
(0.21) -3.78∗∗∗
BtC surprise (abs deviation)
(0.70) Constant
-0.18
-0.05
-0.24
-0.17
-0.41∗
-0.20
(0.11)
(0.35)
(0.23)
(0.24)
(0.23)
(0.24)
Observations
183
430
430
420
424
430
Adjusted R2
0.181
0.073
0.107
0.066
0.086
0.070
Table 4 shows the regression results using alternative specifications. Column 1 depicts the regression results using intraday yield changes (1.5 hours window around the gilt auction) for a shorter observation period between 2011 and 2016. The yield tail is defined as the spread (in bps) between the yield at the weighted average accepted price and the yield at the lowest accepted price at an auction. Yield changes in maturity matched German government bonds are in bps and changes GBPEUR are in %. The BtC surprise (rolling) uses only historical data for the normalisation of the variable, while the BtC surprise (abs deviation) is normalised only by subtracting the sample mean. Heteroscedasticity and autocorrelation robust standard errors are reported (in parentheses), using the Newey and West (1987) correction. ***, ** and * denote statistical significance (two-tailed) at the 1%, 5%, and 10% significance levels, respectively. The sample period lasts from 1.1.2003 to 31.12.2018.
14
Author Statement Both authors contributed equally to the article.
15