Price pressure

Price Pressure in the Government Bond Market:
Long-term Impact of Short-term Advice*
Luis Ceballos„
Damian Romero…
This version: January 08, 2020
Abstract
We analyze the impact of portfolio flows reallocation triggered by switching recommendations of
a financial advisory firm in the domestic government bond market of an inflation-targeting economy.
We document a significant price pressure in both nominal and inflation-linked bonds after portfolio
switching recommendations. We then analyze the main channels by which government yields are
affected (the expectation and the term premium components) and trace their consequences over inflation
expectations and relevant yields on cost of financing for households and firms. Our results suggest
persistent changes in government yields, particularly in inflation-linked bonds yields, triggered by
changes in term premium component. Moreover, whereas we find no effect on inflation expectations,
we report an asymmetric and substantial impact on funding costs for firms and households.
JEL Codes: E43, G12, G14, G23
Keywords: Price pressure, Government bonds, Credit spreads
*
We thank the comments and suggestions from Diego Gianelli, Anh Le, Tim Simin, Joel Vanden, Mihail Velikov, Lu
Yang and seminar participants at the Macroeconomic Analysis Area at the Central Bank of Chile. Also, we are grateful to
RiskAmerica (https://www.riskamerica.com/) and Central Bank of Chile for providing access to the market transactions
for the domestic bond market. Finally, we would like to thank Tobias Adrian for sharing the code used in Abrahams et al.
(2016).
„
Pennsylvania State University. Email: luc532@psu.edu
…
Pompeu Fabra University. Email: damian.romero@upf.edu
Electronic copy available at: https://ssrn.com/abstract=3513739

1 Introduction
Recent literature has focused on the role of large investors affecting prices in several markets through
relevant fund flow reallocations inducing notorious price pressure. The empirical evidence has been widely
documented in several markets: government bond markets by pension funds (Greenwood and Vayanos,
2010), equity markets by mutual funds (Ben-Rephael et al., 2011; Khan et al., 2012), and corporate bond
markets (Ellul et al., 2011; Goldstein et al., 2017). While most of the empirical evidence on price pressure
is focused on asset price reactions, secondary order effects into other relevant financial instruments are
poorly understood. For example, there is little evidence of the effects of price pressure in government bond
markets on inflation expectations, or the transmission to relevant cost of funding rates. Both effects are
key for the conduct of monetary policy. First, changes in inflation expectations not based on fundamentals
may distort relative prices in the economy. Similarly, changes in benchmark rates can cause tremendous
price pressure on the cost of financing for private agents (e.g. households and firms), which in turn can
have real effects on investment, consumption and activity in general.
In this paper, we exploit a large and coordinated portfolio reallocation of pension funds in Chile and
trace the consequences of such reallocations over inflation expectations and cost of long-term financing
for firms and households. Chile provides an ideal setting to investigate the effects on these variables
for three reasons. First, as an inflation-targeting economy, Chile has a well-defined inflation target of
3% for a horizon of two years ahead. Therefore, inflation expectations are constantly monitored by the
central bank through both implied-market expectations (captured by the difference between nominal and
inflation-linked bonds, also known as breakeven inflation) at horizon from two-years to ten-years ahead,
as well as explicit-market expectations by domestic market participants at the relevant monetary policy
horizon of two-years ahead. For implied-market expectations, the composition of these instruments is
very different from other economies, in which the inflation-linked bonds are relatively small compared to
the nominal government bonds. Such differences in size make it difficult to rely on inflation expectations
based on these instruments, given the relevant liquidity premium component embedded in the inflation-
linked bonds. However, in the case of Chile, we observe the opposite phenomena: the inflation-linked
bonds correspond to 65% of the total domestic government bond market.1 2 Therefore, this helps to
alleviate concerns about liquidity premia embedded in inflation expectations from government bonds.
In the domestic bond market, the main bondholders of government bonds are Pension Funds (with
the Spanish acronym, AFP). The Pension Fund system in Chile works through the defined contribution
1
This is considering nominal and real government debt with maturity of two years of higher.
2
Chile exhibits a high indexation to inflation where most financial contracts are linked to the consumer price index (CPI),
such as corporate bonds and mortgages, in which cases more than 95% of new issuance are denominated by inflation-linked
bonds.
1

system, that is, all workers and employees must pay mandatory contributions corresponding to 10% of the
monthly working income. Since 2003, each AFP offers five different types of funds namely Funds A, B, C,
D, and E, with varying degrees of risk. Fund A is the most risky and allocates up to 80 percent of its assets
in international equities, while fund E is the most safe, with the majority of its portfolio in domestic fixed
income instruments, in particular, government bonds. The system allows any contributor to allocate his
personal savings into different funds types (within a particular AFP) in any allocation they want. That
is, the contributor can allocate 100% on risky fund (A), safe (E), or a mix with any other intermediate
funds (B, C, or D). Importantly, AFP’s concentrate almost 70% of total government bond holdings. Such
concentration makes domestic yields particularly sensitive to any change in AFPs’ portfolio reallocation.
That is, any systematic change in the behavior of AFPs would generate price pressure on government
yields.
After 2011, the financial advisory firm “Felices y Forrados” (translated as “Happy and Loaded”, FyF
hereinafter) started to provide switching recommendations from fund A through fund E (or vice versa)
to pension funds contributors aiming to time the pension fund performance. The main goal of FyF
is to outperform the average returns exhibited by different fund types by timing the market through
allocating resources actively between risky and safe fund types. Figure 1 shows the historical pattern of
inflows/outflows of contributors in funds A and E for the period from January 2003 to December 2010
(panel A). In this period, we highlight the low demand for fund E before August 2008, and a completely
reversal during the global financial crisis of 2008 (starting from September 2008) which was characterized
by a flight-to-safety demand for safer assets in which contributors reallocated their funds to fund E by
switching from riskier funds (e.g. fund type A). After 2009, contributors normalized their investments
by allocating funds at the historical pattern observed prior to September of 2008. After 2011 (panel B)
there are significant changes in portfolio reallocation patterns, which are exhibited by important peaks of
inflows (outflows) between funds A and E in the same month and with a similar magnitude but in opposite
directions. In the months that the financial advisory firm FyF sent out switching recommendation among
these funds, which are denoted by the vertical lines, these recommendations match most of the abnormal
episodes of reallocation funds during the sample.
2

Figure 1: Pension fund contributor net flows in funds A and E.
Panel A depicts the net flows of pension fund contributors in aggregate position in funds A (in red) and E (in blue). A positive
(negative) value indicates an inflow (outflow) of the fund in any particular month denoted by thousands of contributor. We
split the sample in two: from January 2003 to December 2011 (Panel A) and from January 2011 to December 2017 (Panel
B). The dashed vertical lines denote the months in which FyF sent out recommendations to its customers to switch between
funds A and E. The x-axis are in years and y-axis are in thousands of pension fund contributors.
We analyze the price pressure impact of these reallocation recommendations in the domestic gov-
ernment bond market. Because pension funds concentrate almost 65% of the total government bond
holding, these coordinated portfolio reallocations have important effects on the government bonds prices.
In particular, we focus on the impact of FyF recommendations on government yields which are relevant
to capture inflation expectation at the monetary policy horizon (two-years) and act as benchmark instru-
ments in setting funding costs for households (through mortgages rates) and firms (through corporate
bonds yields). To evaluate how the portfolio reallocation affects government yields, we rely on an affine
term structure decomposition to identify the expectation component (risk-neutral) and the term premium
of nominal, inflation-linked and break-even yields. We focus on horizons relevant to the monetary policy
(two years) and for the financing costs of households and firms (ten years). For this decomposition, we
follow the methodology proposed by Abrahams et al. (2016). This allows us to determine whether the
portfolio reallocation effect comes from the expectation component or the term premia. In an additional
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test, and to avoid model dependence, we focus on changes in yields and inflation expectations reported
by the Economic Expectations Survey (EES) conducted by the Central Bank of Chile, which captures the
expectation from main market agents in the domestic market. Based on micro data from the EES, we
are able to identify changes in expectations in the months in which FyF sent out switching recommenda-
tions. We estimate the impact on different yields at different maturities as well as the duration impact by
using local projection methods (Jordà, 2005). Finally, we evaluate how changes in FyF recommendations
impact financing costs for firms and households.
We emphasize four main results. First, we document important reallocation flows, focused in the
government bond market and not in other similar assets (e.g. corporate bonds) once FyF started to
operate in 2011. For this comparison, we analyze changes in the net dollar flows in the domestic equity
(the most relevant asset to fund A) and corporate/government bonds (the most relevant assets to fund
E) to put in context the changes in portfolio trades after 2011 (coincidentally at the same time of FyF’s
switching recommendations). Before the global financial crisis in 2008, equity in fund A registered a
monthly average inflow of $72M, representing 0.4% of fund A. In contrast, for fund E, the average outflow
for government (corporate) bonds was $15M ($1.5M), which are small (less than 0.1% of the fund)
compared to the net dollar changes in fund A. Between 2008 and 2010, flows in the opposite direction
was observed, reflecting the safe-to-quality effect triggered by the global financial crisis of 2008, with
significant portfolio reallocations from risky (fund A) to safe pension fund types (fund E). This period
exhibited a large increase in volatility of flows in all assets/funds, especially in government bonds. In
the domestic equity, volatility in portfolio flows increased by 2.5 times ($220M) compared to the pre-
crisis. For the domestic fixed income, the portfolio flows volatility increased 7-8 times higher than in
the pre-crisis period, in which the domestic government bond market reached a volatility similar to that
observed in the domestic equity flows ($191M). Posterior to 2011, the domestic equity in fund A exhibited
positive average flows ($21M) with lower volatility than in the 2008-2010 period. The average inflow and
volatility of the corporate bonds in fund E remained in higher levels than previous sample measured in
dollars ($30M and $79M respectively), but when adjusted by total assets, those values are similar to the
pre-crisis period. However, we observe an important change in the domestic government bonds in fund E.
Both average flows and volatility increased substantially ($102M and $657M, respectively), representing
0.8% and 5.4% of the total fund, respectively. Thus, the portfolio reallocation pattern exhibited in Figure
1 affects mostly domestic government bonds.
Second, we document important changes in both nominal and inflation-linked government bond yields
around the days in that FyF sent out switching recommendations. By focusing on the day of the switching
recommendation, we compute the cumulative changes in the nominal and real government bond yields
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from a day before the event (t − 1) up to eight days after (t + 8) the event. Following Feldhütter and
Lando (2008), who report that the size of the convenience for holding government bonds is by far the
largest contributing factor to the swap spread, we compare the government bond yield behavior with
changes in interest rate swaps in the switching recommendations days. Our evidence suggests that in the
days when FyF send out switching recommendations that induce a buying pressure in the government
bond market (switching from fund A to fund E) or alternatively, recommendations that induce a selling
pressure in the government bond market (switching from fund E to fund A), government yields exhibit
changes that correspond to these recommendations: their yields decrease (increase) in the direction of the
recommendation in a magnitude of 8 basis points, and moreover, the magnitude of the change in yield is
higher (in absolute terms) compared to other similar yields (interest rate swaps). Alternatively, we focus
the analysis in abnormal returns based on government bond prices around the FyF recommendations
days. Our results show that there is a highly significant increase of 60 basis points in cumulative abnormal
returns 30 days after the event. Our evidence does not suggest any abnormal return 10 days before the
event, indicating that such recommendation was not expected by the market. Importantly, we do not
observe any reversion in the short-term horizon.
Third, our previous evidence raises the question of the mechanism in which government bond yields
are affected (expectations or term premia). In the context of an inflation-targeting economy, changes in
government bond yields can trigger changes in other assets that use government bonds as a benchmark or
cause changes in inflation expectations at the monetary policy horizon. Thus, we proceed to decompose
nominal, inflation-linked and the breakeven inflation rates (the difference between nominal and inflation-
linked yields) into an expectation component (risk-neutral) and a term premium component based on
the approach by Abrahams et al. (2016). We separate our analysis into two horizons: the horizon
relevant for monetary policy (two years) and the horizon relevant for setting funding costs for other
financial instruments (ten years). Our main specification regresses changes in government bond yields
on the nominal, inflation-linked and breakeven inflation yields at two- and ten-year horizons and by
components (risk-neutral and term premia) on days in which FyF sent out switching recommendations.
To alleviate endogeneity concerns (that is, changes in yields related to macroeconomic conditions and not
FyF recommendations) we control by domestic economic surprises and international risk factors that may
affect government yields.3 We find small impact on the nominal, real and breakeven yields–in both effective
yields and by their components–at the two-year horizon. However, at longer horizons, we document a
relevant impact on real yields affected by changes in term premia. We observe a similar effect on nominal
yields, but with a smaller impact size. Furthermore, the effect is persistent and do not revert in the
3
This approach is similar to the one proposed by Albagli et al. (2019).
5

60-day horizon, consistent with the evidence based on the abnormal returns. To avoid model dependence,
we extend our analysis to the Economic Expectation Survey (EES) conducted by the Central Bank of
Chile, which report expectations on yields and inflation at different horizons and provide an alternative
approach to analyze effects on market expectations. Consistent with our previous evidence, we find no
significant changes in market inflation expectations, but we do report effect on expected nominal and
real yields. Therefore, we conclude that, while FyF induces price pressure over the Chilean market, such
pressure does not extend to inflation expectations, which remain stable.
Finally, we study the impact of government yields on funding cost for firms and households. In
particular, we focus on credit risk, that is, changes in corporate bond yields in excess of government
bond yields that are affected by FyF switching recommendations. By following a similar approach to
our main specification, we document an asymmetric impact of FyF’s price pressure on the credit risk
market. In events of switching recommendations that induce buying price pressure (switching from fund
A to E), corporate credit spreads increase up to 12 basis points (depending on the credit rating) and do
not revert in 30-day horizon after the FyF event. In contrast, in days of switching recommendation that
induce selling pressure (switching from fund E to fund A), we find no significant changes in credit risk.
In addition, we find a stronger impact in more risky debt. For high-quality corporate bonds (AAA), we
find neither asymmetric nor significant effects. However, the effect on credit spread is stronger as credit-
quality deteriorates (in particular, from AA to BBB). Thus, our results support the idea that changes in
credit risks are driven by local supply/demand shocks, independent of common credit-risk factors (Collin-
Dufresn et al., 2002). Similarly, we report similar patterns in the mortgage loans spreads, in particular,
in the endorsable mortgage loans which is the largest mortgage product in Chile. We document an
asymmetric impact of FyF’s price pressure on the mortgage loan market, that is, an increase of 17 basis
points in FyF’s switching recommendations that induce buying price pressure. On the contrary, we do
not observe a statistically significant impact of FyF’s selling recommendations. At longer horizons, we
find similar asymmetric pattern; buying pressure recommendations increase mortgage spreads by up to
28 basis points and for selling recommendations generate no significant estimates. The effect is consistent
and larger for non-endorsable mortgage loans.
Our paper contributes to three strands of the literature related to institutional investors and price
pressure impact. First, we add evidence on the relevance of financial advisors exerting influence on
their customers’ asset allocation (Gennaioli et al., 2015; Foerster et al., 2017). We document that FyF’s
switching recommendations induce abnormal portfolio reallocation in pension fund portfolios affecting
primarily government bond instruments. We also contribute to the literature on the role of institutional
investors and asset prices (Gompers and Metrick, 2001; Basak and Pavlova, 2013; Cai et al., 2019). We
6

document that large institutional investors (pension funds) can induce significant portfolio reallocation,
producing significant price pressure in the domestic government bond market. Also, our paper shed light
on the importance of the term premia transmission mechanism in the government bond yields (Buraschi
and Jiltsov, 2005; Ang et al., 2008; Bekaert and Wang, 2010). We report that the term premium of
nominal and real bonds as well as the inflation risk premia are the main drivers explaining yield changes
around the days that FyF send out switching recommendations. Furthermore, we trace the impact on
other relevant funding rates for firms and households.
The most related paper to our work is by Da et al. (2018) which documents important fund flow
reallocations in the Chilean equity domestic market triggered by FyF’s switching recommendations. The
authors find that this coordinated trading leads to significant price pressure of around 1% in the Chilean
equity market that reverts after 10 trading days. The authors also document that there is no price pressure
in the domestic government bond market based on the Dow Jones LATixx Chile Government Bond Index
produced by LVA Indices. The main difference with our paper is that Da et al. (2018) analyze domestic
equity market, whereas we focus on the analysis of the domestic government bond market. Specifically,
we focus on traded government bonds, taking into account both nominal and inflation-linked bonds, and
not on aggregate bond indexes. Furthermore, we rely on both bond-maturity (two and ten years) and
government yield decompositions (expectation and term premia) to present our findings. Importantly, we
show evidence of significant price pressures that does not revert in the short-term and persist for several
days. Finally, we trace the consequences for firms’ funding costs by analyzing the impact of switching
recommendations on corporate credit spreads and mortgage loan spreads.
The organization of the paper is as follows. Section 2 details the institutional setting in which Pen-
sion Funds operate in Chile. Section 3 provides evidence of the price pressure in the government bond
market. In Section 4 we explain the data used and our empirical approach. In this section, we decompose
government yields into the expectation and term premia components and we also report an alternative
approach based on expectation derived from economic survey (EES). In Section 5 we report our main
results while Section 6 document the impact of switching recommendations on funding costs for firms and
households. Section 7 concludes.
2 Institutional setting
2.1 Inflation-targeting framework in Chile
Since 1999, with the implementation of a floating exchange rate regime and gradual improvements to
institutions, fostered a fully-inflation targeting scheme in the monetary policy framework in Chile. The
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inflation target is defined for the annual 12-month change in the Consumer Price Index (CPI), prepared
by the National Statistics Bureau. The Central Bank of Chile has set the midpoint of the inflation target
range at 3% annually. That is, monetary policy focuses on keeping the average and expected CPI variation
at around 3% annually. The width of the target range is set at plus or minus one percentage point.4 The
CPI is the price index used most broadly in the country. It is the unit of reference for index prices, wages
and financial contracts, and to compute the inflation-indexed accounting unit (Unidad de Fomento, UF)
based on the previous months CPI.
The operative objective of the monetary policy is to keep annual inflation expectations at around
3% annually over a horizon of about two years. This is the maximum period for which the Central
Bank normally attempts to bring inflation back to 3%. It reflects the average lag between changes in
the policy instrument and their impact on output and prices. The two-year horizon mitigates concerns
about the volatility of output and other variables, as well as the presence of temporary shifts in some
CPI components. Given the relevance of expectation at the monetary policy horizon, the Central Bank
of Chile constantly monitors the expected inflation from different sources (financial instruments such as
government bonds or economic surveys). The Central Bank carries out its monetary policy by influencing
the daily interbank interest rate, that is, the rate at which commercial banks grant credit to each other
on a daily basis (overnight). The Central Bank of Chile conducts monetary policy by controlling the
supply of liquidity or monetary base, so that the resulting interest rate is close to the monetary policy
rate (MPR). For longer horizon, the Central Bank controls the supply of government bonds (nominal and
inflation-linked bonds).
The Central Bank has auctioned off discount promissory notes due in 30 to 90 days, nominal bonds
(BCP) of two-, five- and ten-year maturity, and bonds indexed to inflation (BCU) with similar maturities.
There is an annual calendar establishing issuances of debt due in more than one year. In practice, the
Central Bank’s debt policy is to maintain a stable size and structure over time. Renewing and issuing new
debt (promissory notes and bonds) make possible to gradual adjustments of the net supply of funds in
the interbank market. Regulating amounts or quotas for new debt issues, whose calendar for short-term
documents is announced one working day in advance of the reserve period, allows the Bank to adjust for
the overall monthly liquidity in a way that is consistent with its goal of keeping the interbank rate around
the monetary policy rate.
4
This range sends three main signals: tolerance to temporary deviations of actual inflation away from the 3% target;
symmetrical concern about deviations below target and above target; and the level of normal variability of inflation along
the business cycle.
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2.2 Inflation-linked government bonds
For the conduct of monetary policy, the Central Bank constantly monitors inflation market expectations
derived from surveys and financial instruments. Breakeven inflation expectation defined as the difference
between nominal bonds (BCP) and inflation-linked bonds (BCU) is one of the main source that gauges
inflation expectations in an inflation-targeting economy. However, several studies have reported that the
low liquidity of inflation-indexed bonds relative to nominal bonds makes it hard to relate changes in
the difference of the nominal and real yields as a reliable proxy of inflation expectations, in particular,
several embedded premiums in yields such as liquidity premiums. For the US market, D’Amico et al.
(2008) report that Treasury Inflation-Protected Securities (TIPS) may not be efficiently priced, due to
its lower liquidity or the relative newness of the TIPS market (originated in 1997). Since then, several
researchers attempt to document the relevance of the liquidity premium embedded in the inflation-linked
bonds. For example, Hördahl and Tristani (2014) document the inflation and liquidity risk premium for
US and Euro Area by using observable proxies for liquidity (trading volume and yield spreads). D’Amico
et al. (2008) provide an extensive discussion on the illiquidity of the TIPS market in the early years and
argue that it result in severe distortions in TIPS yields. Additional evidence have been documented by
several researchers (Gürkaynak et al., 2010; Pflueger and Viceira, 2011; Grishchenko and Huang, 2013)
that provide estimates of liquidity premia before and during the financial crisis.
In the case of Chile, we observe the opposite effect. Table 1 reports a higher presence of inflation-
linked bonds compared to nominal bonds. In Panel A we report the total outstanding bonds and turnover
transactions in nominal and inflation-bonds in domestic government market at the end of 2010 and
2017. We document a higher relative presence of inflation-linked bonds compared to nominal bonds. In
particular, we report a three times larger inflation-linked bond market (8.5% of GDP) compared with
the 2.9% of GDP in nominal bonds at the end of 2010. At the end of 2017, the difference is lower but
is still 3.4% larger (in terms of GDP) for inflation-linked bonds. The last row (Turnover) exhibits the
average monthly transaction over the total outstanding bond in both nominal and inflation-linked bonds.
Similarly, we document that the relative liquidity in terms of bond outstanding and transaction is higher
for inflation-linked bonds.
In addition, Panel B we document market transactions in the secondary market for difference maturity
ranges. For shorter maturities (less than two years),we observe that both nominal and inflation-linked
bonds represents a small fraction of 12.8% and 10.4% of total transactions in 2017, respectively. For
medium-term bonds (bonds with maturities between two and five years) we exhibit that around 40% of
total transactions were made in 2010 which increased to 46.8% and 51.9% at the end of 2017 for nominal
and inflation-linked bonds respectively. Finally, transactions in long-term bonds (five years or higher)
9

account for 40.4% and 37.7% for nominal and inflation-linked bonds, respectively. Panel C exhibits the
main bond-holders for both nominal and inflation-linked bonds. In 2010, pension funds held 12.9% of
the total nominal government bonds and 9.2% for the inflation-linked government bonds, which increased
substantially by 2017, constituting a total holding of 49.8% and 73.8% for nominal and inflation-linked
bonds, respectively. The evidence of government bonds in the Chilean market suggests a higher presence
of inflation-linked bonds for maturities higher than two years. Therefore, we do not expect to have a
relevant liquidity premium component embedded in inflation-linked bonds; this exhibits an ideal setting
to extract inflation expectations from these government bonds.
Table 1: Domestic government bond market.
This table reports the outstanding amount, transactions and main bond-holder in the government bond market. For each
nominal and inflation-linked bonds reported in the first and second columns we report the main variables at the end of 2010
and 2017, respectively. In Panel A, we report the outstanding bonds and turnover transactions in nominal and inflation-bonds
in the domestic government bond market. The first row reports the total outstanding amount in millions of dollar, the second
row (% of GDP) reports the outstanding amount as a fraction of the total GDP (in percent), and last row (Turnover) reports
the average monthly turnover (total transaction divided by total outstanding amount) in both nominal and inflation-linked
bond. Panel B exhibits the distribution of transactions by different maturities in the secondary market for shorter maturities
(less than two years), medium-term bonds (bonds with maturities between two and five years) and long-term bonds (five
years or higher). Panel C exhibits the main bond-holders for both nominal and inflation-linked bonds.
Nominal bonds Inflation-linked bonds
2010 2017 2010 2017
Panel A: Outstanding amount and turnover
MM USD 6,417 27,752 18,470 37,150
As % of GDP 2.9 10.0 8.5 13.4
Turnover (percent) 15.8 8.0 24.2 10.6
Panel B: Transactions by maturity (percent)
Less than 2 years 16.5 12.8 25.1 10.4
Between 2 and 5 years 40.4 46.8 38.3 51.9
Higher than 5 years 43.1 40.4 36.6 37.7
Panel C: Main Bondholders (percent)
Pension Funds 9.8 49.8 49.9 73.8
Insurance companies 1.5 0.5 9.2 2.5
Mutual funds 12.9 9.4 5.9 7.6
Banks 66.2 26.0 29.4 11.4
The importance of inflation-linked bonds can be categorized in two main objectives. First, as an
inflation targeting economy, the information embedded in the nominal and inflation-linked bonds allow
financial authorities (e.g. Central Bank) to monitor implied market inflation expectations by focusing on
the difference between these bonds at similar maturities. Thus, the government bond market is one of the
main sources to gauge inflation expectation. Second, inflation-linked bonds serve as a market benchmark
for funding costs for firms and households at longer horizons. For firms, corporate bond issuance by non-
financial firms represents a 27% of GDP, from which one half is issued in the domestic market (BCCh,
2017). Corporate bonds in the local market are issued mainly in U.F. (linked to effective CPI), and
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thus, are identical to inflation-linked bond issued by the government. For households, the inflation-linked
bonds are the main benchmark to set mortgages rates. The mortgage credit in Chile exhibits three main
mortgages types; mortgage bonds, endorsable and non-endorsable mortgage loans. Similar to corporate
bonds, 95% of total mortgages loans are originated in U.F. by which the inflation-linked bonds serve as
the main benchmark in setting the baseline cost of credit. In section 6, we discuss in detail each market.
In sum, inflation-linked bonds are relevant in gauging inflation expectations at the monetary policy
horizon (two years) and act as an important benchmark in setting financing costs for firms (corporate
bonds) and households (mortgages loans) at longer horizons (ten years).
2.3 Pension fund system
As shown in Table 1 the main bondholders of government bonds in Chile are the Pension Funds. In this
section we briefly describe how the pension funds system in Chile operates, its main features, and how
asset allocation is performed by AFP through pension contributors by choosing and allocating resources
in different pension fund types.
All workers and employees must contribute to the system. Mandatory contributions amount to 10%
of monthly income5. Self-employed individuals may contribute voluntarily, and salaried workers can also
enhance their pension through additional voluntary contributions. Worker contributions are collected by
private companies called Pension Fund Administrators (AFP), whose functions are limited to managing
pension funds and providing and administering certain pension benefits. Workers are free to choose
any AFP and may change from one AFP to another at any time. Workers may also make voluntary
contributions to either their individual accounts or separate and voluntary retirement savings accounts.
Employers are not required to contribute to their employees’ accounts, and participation is voluntary for
the self-employed. Nowadays, AFPs offer five different fund types that vary in degree of risk, mainly
determined by how a fund allocates in equity and fixed income.
Until 2002, an AFP could offer only one account to each contributor. The multi-fund law implemented
in August 2002 requires each AFP to offer four different types of funds, namely funds B, C, D, and E,
with varying degrees of risk. Each AFP may also offer a Fund A with up to 80 percent of its assets in
equities. The 2002 law permits each account holder to allocate his contribution between two different
funds within one AFP, in whatever proportion he chooses6. Table 2 shows the average portfolio allocation
5
The fraction of monthly income that exceeds US$2,800 (corresponding to 60 UF) is non-contributory.
6
Every fund (Funds A-E) managed by an AFP must maintain a minimum and a maximum rate of return calculated to
reflect the average performance of that fund category among all the other AFPs in a 3-year period. At the same time, each
AFP fund must keep 1 percent of the value of its pension fund as a separate reserve fund whose investments are subject to
the same rules as those for the pension funds. If any AFP’s fund performance falls below the minimum, it must make up the
difference from its reserve fund. If an AFP fund exhausts its reserve fund, the government makes up the difference, dissolves
the AFP, and transfers the accounts to another AFP (Law 3500).
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for all Pension funds differentiated by funds type (A to E). The first row reports the average asset under
management (AUM) at the end of 2017 for each fund. All funds represent about 80% of GDP. In addition,
the riskier fund A concentrates its portfolio heavily on international equity, while the safer fund E allocates
most of its fund in domestic fixed income, especially government bonds.
Table 2: Asset under management (AUM) and asset classes by Pension Fund
This table reports the assets under management (AUM) held by all Pensions Funds and portfolio weights invested in each
fund type (A, B, C, D and E) for different assets classes. The first row reports the total AUM at the end of December 2017.
Panels A and B report the investments located in international and domestic portfolio in different asset classes. In domestic
investment, we refer Government debt as investment in nominal and inflation linked bonds with residual maturity greater
than two years. Panel C reports the structural limits of investment ruled by the Central Bank of Chile and the Pension
Supervisory Agency. All values (except AUM) are reported in percent.
Asset classes
Fund types
Total
A B C D E
Total AUM (MM USD) 32,641 34,476 77,005 35,009 31,330 210,461
Panel A: International assets
Total 76.3 57.7 42.8 28.2 8.7 42.9
Equity 62.2 42.7 27.2 14.4 2.6 29.4
Fixed Income 14.1 15.0 15.5 13.9 6.1 13.6
Panel B: Domestic assets
Total 23.7 42.3 57.2 71.8 91.3 57.1
Equity 18.1 17.0 12.6 5.4 2.2 11.5
Government debt 0.7 8.2 20.5 32.0 31.0 18.9
Corporate bonds 0.9 3.3 6.6 8.4 10.8 6.1
Bank bonds 0.9 7.6 13.2 19.9 27.8 13.7
Others fixed income 3.0 6.1 4.3 6.0 19.4 6.9
Panel C: Structural limits
Equity 80 60 40 20 5
Fixed income 40 40 50 70 80
In Panel A, we report the fraction of total asset under management (AUM) for international assets.
We observe a decreasing relevance of international investments from fund A to fund E which allocate
76.3% and 8.7% of the total fund respectively. Also, the composition of the investment varies; fund A
invests more on equity, and fund E allocates more to fixed income assets in international markets. In
Panel B, we report the opposite allocation, that is, an increasing allocation in domestic markets from
fund A to fund E. In particular, fund A allocates 18.1% of the fund to domestic equity whereas fund E
allocates 31% of the fund to domestic government bonds (e.g. nominal and inflation-linked bonds) and
other fixed income securities (e.g. corporate bonds and bank bonds). Finally, in Panel C, we report the
structural limits defined by law by which different pension fund types may allocate investments.
12

3 Price pressure in the government bond market
In this section, we describe the Chilean financial advisory firm, as well as the class of events that we
exploit to identify the price pressure in government bond market.
3.1 The financial advisory firm
In 2011, the financial advisory firm “Felices y Forrados” (FyF) started to operate, providing services to
contributors of pension funds that consists of switching recommendations between different pension fund
types to achieve higher returns by timing the market. By charging a monthly fee of about $3 US dollars,
FyF provides switching recommendations through emails that are issued after the close of a trading day.
The typical recommendation is reallocating 100% or 50% of total wealth between the riskiest (fund A) and
the safest fund (fund E). That is, the FyF strategy consists of having a more active portfolio allocation
that would outperform passive strategies. Given that all AFPs in Chile have exhibited similar returns
and similar portfolios historically, the FyF strategy aims to generate higher returns by allocating savings
among different fund types, and not by changing savings among different AFPs.
Between July 2011 to December 2017, FyF sent out 32 recommendations to switch between funds A
and E7. Table 3 reports all switching recommendation dates (first column) sent by FyF between July
2011 to December 2017. In the second column, ‘Weight‘ captures the magnitude and direction of the
recommendation 8. For example, a recommendation of reallocate 100% in fund E from fund A, will
induce a buying pressure in the government bond market, in which case we assign a value of +1 to
capture the direction and magnitude of the recommendation. Similarly, a recommendation to reallocate
100% of savings to fund A from fund E, will induce a selling pressure in the government bond market
which can be represented by a weight of −1 to indicate the magnitude and direction of the trade. In the
case that the recommendation is partial, that is, reallocate 50% to fund A (or fund E), we assign a −0.5
(+0.5) to the weight variable. The last two columns show the allocation recommendation for each date.
7
In the sample, FyF sent out a total of 42 recommendations. We exclude recommendations from/to fund C to/from funds
A and E given that the composition of fund C exhibits a similar composition for domestic equity and fixed income compared
to other extreme funds. For details of all recommendations by FyF, see https://www.felicesyforrados.cl/resultados.
8
We follow similar definition used by Da et al. (2018).
13

Table 3: Financial Advisory Firm (FyF) switching recommendation dates.
This table shows the days between July 2011 to December 2017 in which the financial advisory firm (FyF) sent out switching
recommendation notifications to their subscriptors. Column ‘Weight‘ denotes the price pressure direction of each recommen-
dation. If the recommendation is to switch from fund A to E, there is a buy-side price pressure (+1). Otherwise, is a sell-side
pressure (-1). Balanced switching recommendations (e.g., allocation 50% to funds A and E) are assigned a weight of ±0.5.
Date Weight Pressure From To
7/27/2011 +1 Buy A E
10/12/2011 -1 Sell E A
11/22/2011 +1 Buy A E
1/11/2012 -1 Sell E A
3/29/2012 +1 Buy A E
6/19/2012 -1 Sell E A
6/28/2012 +1 Buy A E
7/19/2012 -1 Sell E A
8/29/2012 +1 Buy A E
1/2/2013 -1 Sell E A
4/3/2013 +1 Buy A E
7/17/2013 -1 Sell E A
8/16/2013 +1 Buy A E
9/6/2013 -1 Sell E A
1/24/2014 +1 Buy A E
8/19/2014 -0.5 Sell E 50% A / 50% E
10/30/2014 -0.5 Sell 50% A / 50% E A
12/15/2014 +1 Buy A E
2/12/2015 -0.5 Sell E 50% A / 50% E
3/18/2015 -0.5 Sell 50% A / 50% E A
5/13/2015 +0.5 Buy A 50% A / 50% E
12/16/2015 -0.5 Sell E 50% A / 50% E
12/22/2015 -0.5 Sell 50% A / 50% E A
1/6/2016 +0.5 Buy A 50% A / 50% E
1/15/2016 +0.5 Buy 50% A / 50% E E
11/9/2016 -0.5 Sell E 50% A / 50% E
12/22/2016 +0.5 Buy 50% A / 50% E E
9/12/2017 -0.5 Sell E 50% A / 50% E
9/28/2017 -0.5 Sell 50% A / 50% E A
10/12/2017 +0.5 Buy A 50% A / 50% E
11/28/2017 -0.5 Sell 50% A / 50% E A
12/19/2017 +0.5 Buy 100% A 50% A / 50% E
The domestic impact of portfolio reallocation has gained attention from the domestic press as well as
financial regulators. For instance, the Pension Supervisor implemented measures aimed to provide more
information to pension fund affiliates, to increase transparency in the process of changing funds, and to
mitigate the effects of these changes on the domestic market by: (i) creating an informational screen on
profitability for affiliates requesting a change in funds; (ii) authorizing funds E to invest up to 10% in
investment vehicles with a small share of restricted instruments; and (iii) partial allocation of massive
transfers, when changes exceed 5% of a pension fund’s value. In addition, the Central Bank of Chile
in the Financial Stability Report of 2013 (second half) mentioned the effects of Pension fund portfolio
reallocation in domestic markets: ”...The pension funds continued to record massive switching between
funds by their affiliates following announcements by financial advisors...”. Indeed, several recent studies
have documented the relevance of the portfolio reallocation of equity by Pension Funds in the domestic
14

markets (Da et al., 2018; Cuevas et al., 2018). We focus on the impact of the domestic government bond
market and its implication for other relevant benchmarks in financing costs.
3.2 Reallocation portfolio and market impact
To put in context the importance of the portfolio reallocation observed in the domestic markets, we
document the changes in dollar flows in equity and fixed income of the pension fund system, and then,
analyze the changes in government bonds yields around the FyF event-days. In Table 4, we report the
main descriptive statistics for changes in dollar flow in pension funds A and E for the aggregate pension
fund system, focusing on the domestic equity (Panel A) and the main assets in the domestic fixed income
market (Panel B and C). We split the whole sample in three periods: before the financial crisis (2003 to
2007), after the onset of the global financial crisis (GFC, 2008 to 2010), and after the advisory financial
firm FyF started to operate (2011 to 2017). For each sample, we report two columns to denote the changes
in total flows in millions of dollars and as a fraction of the total fund respectively.
Before the global financial crisis (GFC) in 2008, equity in fund A registered a monthly average inflow
of $72M (0.4% of the fund). In the same period, the average flow in fund E was -$15M for government
bonds and $1.5M for corporate bonds. In both cases the total outflow/inflow as a fraction of the total
fund is small (less than 0.1% of the fund). In the middle column, the opposite trend was observed in
domestic equity market, with an average outflow of $73M (or 0.4% of the fund). More importantly, the
average inflow in domestic equity increased, with an average amount of $21M and $24M for corporate
and government bonds (equivalent in both cases to 0.2% of the Fund E). This change in direction in
equity and fixed income reflects the safe-to-quality effect triggered by the global financial crisis, that is,
reallocating funds from riskier portfolios (fund A) to safer portfolios (fund E). Our sample exhibits a large
increase in volatility of flows in all these assets/funds, especially in government bonds. In the domestic
equity market, volatility increased 2.5 times ($220M) compared with that observed in the previous sample.
For the domestic fixed income market, volatility increased by 7-8 times higher than previous sample. In
particular, the domestic government bond market reached a level of volatility ($191M) similar to that
observed in the domestic equity. Finally, the 2011-2017 sample, equity flows exhibited a positive average
flows ($21M) with a lower volatility that from the GFC (but higher than the pre-GFC period). The average
inflow and volatility in the corporate bond asset remained somewhat in higher levels than previous sample
measured in dollars ($30M and $79M, respectively), but are similar in magnitude after adjusting by the
total fund E. However, we observe an important change in the domestic government bonds. Both average
flows and volatility increased substantially ($102M and $657M, respectively), representing 0.8% and 5.4%
of the total fund.
15

To analyze the impact of FyF switching recommendation on the government bond yields, we focus
on the yields impact on dates reported in Table 3. In Figure 2, we report the cumulative changes of
government bond yields and interest rate swaps (IRS)9 in basis points from the day before (t − 1) to
eight-days after the FyF recommendation event (t + 8). We focus the analysis on different government
bonds (nominal and inflation-linked bonds) as well as potential price pressure impact (buying and selling
price pressure by FyF). In all cases we highlight (grey area) the sample in which the pension funds can
reallocate funds (between t and t+4), conditional on having received a switching portfolio allocation from
its contributors on day t (the day when FyF sent out the recommendations). Two important patterns
arise. First, government bond yields change in same direction as the FyF recommendation. That is,
when FyF recommend to reallocate to fund E, this would induce a buying pressure in the government
bond market, increasing the price and reducing yields of the government bonds. Results are reported in
Panel A, in which the effect is similar for both nominal and inflation-linked bonds as well as IRS yields.
The opposite effect holds for selling recommendations, as reported in Panel B. Second, in all cases, the
price pressure is more pronounced for government bonds than for IRS rates, and the total impact occurs
mostly during the windows [t, t + 4]. Overall, the evidence supports the idea that portfolio reallocation
generates significant price pressure in the government bond market.
9
We compare daily changes in government bonds with the IRS because the government bond is often used as a benchmark.
Thus, we expect that both instruments exhibit similar movements around the FyF event.
16

Table 4: Dollar flows in Government bonds in fund E
This table presents descriptive statistics on the changes in dollar flows in pension funds. Panel A shows the statistics for
equity in fund A. Panels B and C show the statistics for corporate bonds and government bonds in fund E, respectively. Each
panel is separated in the three sample periods (before the financial crisis, after the onset of the financial crisis and before
FyF, and after the financial advisor FyF started to operate). Each sample is reported as changes in total flows (million of
dollars, first column) and as a fraction of the size of the fund (ratio, second column).
Variables
Jan.2003-Dec.2007 Jan.2008-Dec.2010 Jan.2011-Dec.2017
(1) (2) (1) (2) (1) (2)
Panel A: Equity (Fund A)
Mean 72.0 0.4 -72.6 -0.4 21.1 0.1
STD 89.3 0.5 219.9 1.1 174.1 0.9
P25 -10.9 0.1 -262.5 -0.9 -192.4 -0.4
P50 (Median) 54.4 0.3 -91.5 -0.5 3.5 0.0
P75 190.1 0.4 169.2 0.2 234.7 0.8
Panel B: Corporate Bonds (Fund E)
Mean 1.5 0.0 20.8 0.2 30.1 0.2
STD 7.8 0.1 55.2 0.5 78.8 0.6
P25 -4.3 0.0 -9.5 -0.1 -15.4 -0.1
P50 (Median) 3.1 0.0 10.5 0.1 15.4 0.1
P75 6.2 0.1 37.5 0.3 76.1 0.6
Panel C: Government Bonds (Fund E)
Mean -15.0 -0.1 23.8 0.2 102.0 0.8
STD 23.1 0.2 191.2 1.6 657.1 5.4
P25 -27.2 -0.2 -48.1 -0.4 -199.0 -1.6
P50 (Median) -13.4 -0.1 -0.8 0.0 158.2 1.3
P75 0.2 0.0 54.3 0.4 469.7 3.8
3.3 Excess bond returns
Finally, we focus on the excess bond return induced by the financial advisory company. For each FyF
recommendation, we compute the abnormal return (AR) as the difference between the price return for
each nominal and inflation-linked bond adjusted by the one-year prime rate, which is the typical interest
rate used for institutional investors to finance in the domestic money market. We then compute the
average in the cross-section for all nominal (BCP) and inflation-linked bonds (BCU) separately. We focus
the analysis at the individual bond-level transaction for all bonds traded in the 2011-2017, the sample
that FyF started the switching recommendations10. To compute the cumulative abnormal return (CAR),
we aggregate the abnormal return for each day from 10 days before to 30 days after the event (t − 10,
t + 30). On days of “buy” recommendations (see Table 3), we consider the adjusted return for that day.
On days of “sell” recommendations, we consider the negative of this return. Figure 3 depicts the CAR
for all traded nominal and inflation-linked bonds around the FyF recommendations dates. In the figure,
the solid line corresponds to the CAR measure and the dashed lines denote the 90% confidence interval.
10
In unreported results we computed the cumulative abnormal return measure based on the aggregate government bond
index reported by RiskAmerica for both nominal and inflation-linked bonds. This delivers similar results by focusing on
bond-level excess returns.
17

The vertical line denotes the FyF recommendations days (centered at t = 0).
Figure 2: Changes in yields around FyF recommendation days.
The ﬁgure exhibits average cumulative changes in yields between [t − 1, t + 8], where t is the day with a recommendation.
Those events correspond to switching advices between fund A(E) and E(A) across all FyF days with recommendations (time
series) and diﬀerent bonds maturities (cross-section). The black solid line denotes changes in government bond yields and
the dashed lines denote changes in interest rates swap (IRS). Panel A shows average changes of yields in days that FyF sent
out recommendations to switch to fund E (buying pressure). Panel B reports similar results for days to switch to fund A
(selling pressure). All variables are reported in basis points.
18

Figure 3: Cumulative abnormal returns (CAR) in government bonds.
This figure shows the cumulative abnormal returns (CAR) for all traded nominal and inflation-linked bonds around the FyF’s
recommendations dates (10 days before and 30 days after the event). For each event we compute the abnormal return (AR)
as the difference between the price return for each nominal and inflation-linked bond adjusted by the one-year prime rate
(relevant interest rate for institutional to finance in the domestic market). Then, we compute the average in the cross-section
for all nominal (BCP) and inflation-linked bonds (BCU) separately. To compute the CAR, we aggregate the abnormal return
each day between [t−10, t+30], where t is the day of the event. In days when the recommendation is to buy (see Table 3 for
details), we consider the adjusted return in for day and when the recommendation is to sell we consider the negative adjusted
return for the day. In the figure, the solid line corresponds to the CAR and the dashed lines denote the 90% confidence
interval. The vertical line denotes the FyF’s recommendations days.
In the 10-days before the FyF event, we do not observe abnormal excess returns. This can be inter-
preted as the market did not anticipate any switching recommendations by FyF. However, after the FyF
event, both nominal (panel A) and inflation-linked bonds (panel B) exhibit important excess bond returns
after the event. The impact is around 60 bps after 30 days, and effect that is statistically significant and
does not exhibit any reversion. Our findings are consistent with Cuevas et al. (2018), who report that
the number of (paid) followers and second-hand followers, increased their savings into the recommended
fund type following a recommendation on days t + 3 to t + 8.
In sum, this section documents that the recommendations of the financial advisory generates substan-
tial reallocations between funds, inducing price pressures on government bonds. In the next section we
investigate the static and dynamic effects of such recommendations over yields, the relative importance
of different transmission channels, and the impact over inflation expectations.
19

4 Empirical setting
4.1 Data
We use different sources to construct the dataset related to portfolio holdings, market yields and expecta-
tion surveys. In Appendix A, we report the main variables and sources of information. For institutional
portfolio holdings, we collect monthly portfolio transaction reported by the Chilean Pension Supervisor.11
The database contains portfolio holdings by all pension funds at a monthly frequency. For our purposes,
we collect the transactions and holdings for all domestic government bonds (nominal and inflation linked
bonds), corporate bonds and domestic equity. In addition, we collect the inflows/outflows of contributors
from pension funds type A and E from January 2003 to December 2017.
Data on government bond yields are collected from the Central Bank of Chile that represents zero-
coupon bond yields for maturities ranging from 3 and 6 months and 1, 2, 5 and 10 years for nominal
yields (BCP) and for maturities of 2, 5 and 10 years for inflation-linked bonds (BCU). To construct excess
bond returns we use bond price transactions/valuations from RiskAmerica12 on a daily frequency basis
from 2011 to 2017. To put in context our main results, we use several economic surprises commonly used
in the study of high frequency impact of news, such as monetary policy rate (MPR), inflation (CPI),
unemployment (U) and activity (Y) releases. In all cases, we compute the estandarized economic surprise
as the difference between the actual release and the market expectation reported by Bloomberg adjusted
by the standard deviation of the economic surprise, that is, Sj
t = Xt−E(Xt)
σ . The standardized economic
surprise allows us to make a proper comparison between different economic surprises that may be different
in magnitude of the surprise. In addition, we collect data for exchange rates (USD per CLP), oil prices
(WTI) and international risk (VIX) from Bloomberg.
Finally, we collect data from the Economic Expectation Survey (EES) conducted by the Central
Bank of Chile to domestic market participants. The EES collects questions not only on inflation up
to two-years ahead, but also on other variables, such as expected nominal and inflation-linked yields,
allowing us to compute implied inflation expectations at long-term horizons. The EES is answered at a
monthly frequency by the main domestic market participants (e.g. banks, financial analysts and pensions
funds). We focus our analysis at the individual-level response from 2003-2017, allowing us to trace the
consequences of FyF recommendations on changes in market expectations.
11
Detailed information is reported in https://www.spensiones.cl/portal/institucional/594/w3-propertyname-621.
html.
12
See www.riskamerica.com.
20

4.2 Decomposing term structure of interest rates
To gauge the impact of FyF’s recommendations on domestic government bond yields, we decompose
nominal, inflation-linked and breakeven yields into the expected risk-neutral and the term premia compo-
nents. Based on this decomposition, we can trace the main channels by which government bonds yields
are affected by FyF’s switching recommendations. Thus, we start by decomposing nominal and real bond
rates into their main components:
yh
t,N = Et(yh
t,N ) + ρh
t,N (1)
yh
t,R = Et(yh
t,R) + ρh
t,R (2)
BEIh
t = yh
t,N − yh
t,R = Et(πh
t ) + ρh
t,π, (3)
where yh
t,N and yh
t,R denote the yield on a nominal and real government zero-coupon bond of maturity
h, respectively. On the left-hand side of equations (1) and (2), Et(yh
t,•) corresponds to the risk-neutral
(or expected rate) component of the nominal/real yield, while ρh
t,• is the term-premium component.
Equation (3) corresponds to the difference between nominal and real yields of the same maturity. Thus,
the difference between these two terms correspond to the breakeven inflation (BEI) rate, which consists of
two components. The first is the expected inflation denoted by Et(πt+h) and the second term ρh
t captures
the inflation risk premia. Under this specification, we can decompose the nominal and real zero coupon
bonds yield into the components associated with inflation expectations and the component related to
inflation risk.
To decompose both the nominal and real yield curves, we follow the approach proposed by Abrahams
et al. (2016). In Appendix B we report the model in detail. Under this framework, we are able to decom-
pose observed yields as in (1) and (2), and construct the breakeven inflation curve and its components
(expected inflation and inflation risk premia).
4.3 Funds’ reallocation impact on government yields
We are interested in the impact of the financial advisor releases over yields and inflation expectations. To
study this, we follow two approaches, which are discussed in detail below.
4.3.1 Informational content of government bonds
First, we exploit the daily frequency of our yields dataset and the consequences of FyF recommendations.
To this, we measure the dynamic impact of such events, by relying on the local projection method proposed
21

by Jordà (2005). In particular, we estimate the following regression:
∆yj,t+h = αh
j + βh
j FyFt +
4
j=1
γh
j Sk,t +
n
i=1
δh
j ∆yj,t−i +
n
i=0
θh
j Xt−i + εh
j,t, (4)
where ∆yj,t+h corresponds to the difference between days t − 1 and t + h, with h = 0, . . . , 60, for
yield y of maturity j. We estimate this equation for nominal yield (BCP), inflation-indexed or real yield
(BCU), and break-even inflation measures. We focus in maturities of two and ten years.13
In equation (4), the main explanatory variable of interest is FyFt, which takes a numerical value if
FyF make a switching recommendation on day t, and zero otherwise. The numerical values and activation
dates are described in Table 3. We also control for surprises in macroeconomic releases, summarized in
vector St. As mentioned in section 4.1, these surprises are measured as the difference between the effective
release of the monetary policy rate (MPR), inflation (CPI), activity (Y) and unemployment (U) and the
expected value given by the expectation survey in Bloomberg. The rationale behind these variables is to
control for domestic shocks that may affect the evolution of yields. Those surprises are demeaned and
divided by their respective standard deviation, allowing us to directly compare the γh
j coefficients.
We also incorporate additional control variables, such as the (log of) VIX, nominal exchange rate
and oil price, given by the WTI index. All these variables are summarized in the vector X, and are
included contemporaneously and with lags. They capture international factors, such as changes in risk
appetite, capital flows and foreign demand shocks, that might affect the demand for domestic fixed income
instruments and their yields.
4.3.2 Informational content of market-based surveys
The previous approach relies on the specifics behind the model used to decompose yields. To avoid such
model-dependance, we rely on micro data that directly measures expectations, giving us a measure that
by construction is not contaminated by premia. We use the monthly data of the Economic Expectations
Survey (EES), collected by the Central Bank of Chile, to study the impact of FyF recommendations on
yields (nominal and real) and inflation expectations (at different horizons) of market participants.
We collect market responses at the individual-level from this survey, which is the main source followed
by the Central Bank to gauge inflation expectation at the monetary policy horizon from January 2003 to
December 2017. The EES covers the main market participants in the market, such as banks, institutional
13
In unreported results (available upon request), we also estimate this specification using 5y5 yields as an alternative to
10-year yields. This captures expected rates in five years, five years ahead, and is computed as (10 · y10 − 5 · y5)/2, in which
y5 and y10 are yields (nominal, real or break-even) of five- and ten-year maturity, respectively. All qualitative results with
respect to 10-year yields holds.
22

investors (e.g. pension funds), and academics. Thus, we are able to identify different market participant’s
responses over time and analyze changes in expectation related to inflation and interest rates around
months when FyF sent out recommendations.
We focus on questions relevant for our analysis, namely, expectation of inflation (CPI) at the monetary
policy horizon (two-year ahead) as well as the implied-inflation expectations at the five-year horizon in one
and two-year ahead. We define implied-inflation expectation because the EES asks for expected nominal
and real yields at the five-year horizon, in one and two-years ahead. Based on EES’s resonses, we can
compute the inflation expectation for longer horizons.
The following specification captures the impact of FyF recommendations on expected inflation and
yields at different horizons, by estimating the following regression:
∆yt+h
i,t = αh
i + αh
t + βh
FyFt + γh
Xt−1 + εh
i,t. (5)
Here, ∆yt+h
i,t denotes the cumulative change in yield expectations between months t − 1 to t + h (with
h = 0, 1, 2) for agent i in month t. For the dependent variable we consider different alternative such as
inflation expectations and nominal and real yield expectations one-year and two-year ahead. FyFt is the
main explanatory variable of interest, which is an indicator variable that takes values of −1, −0.5, +0.5, +1
depending on the switching recommendation advice in a particular month. Xt denotes lagged variables
macroeconomic variables14 such as monetary policy rate, inflation, activity and unemployment events
that may affect agent’s expectations and other international variables that affect yields and inflation
expectation such as VIX, exchange rate and WTI. Individual fixed effects are captured by αi that accounts
for unobserved heterogeneity across agents and αt denotes a month and year fixed effects that capture
unobserved heterogeneity across time such as macroeconomic shocks. In all our specification we cluster
standard error at the agent-level.
5 Results
We start our analysis by presenting results using the model-implied decomposition, that is, the risk-neutral
(RN) and term premium (TP) components. In section 5.1, we investigate the impact effect of different
macroeconomic releases over yields and compare them with the FyF switching recommendations. Then,
in section 5.2, we extend the analysis to study the dynamic consequences of the FyF announcements.
Finally, in section 5.3, we present results using the market-responses data collected from the Economic
Expectations Survey (EES).
14
Because we regress monthly data, we use effective macroeconomic variables instead of macroeconomic surprises.
23

5.1 On-impact effect
In this section, we study the impact effect of different events on yields at different maturities. Results
are presented in Table 5, which is organized in three panels. In panel A, we present results for FyF
events, while panels B and C present results for inflation and activity releases, respectively. Recall that
coefficients of macroeconomic events capture the differential effect of government bond yields in days
of macroeconomic releases, while the FyF events capture the recommendations made by the financial
advisory firm. For each panel, we present results for the nominal, real and breakeven yields separately, and
within each yield, we study the effect with different maturities (two- and ten-year yields) and components
(fitted yield, risk-neutral component and term-premium component). We start with the effect on two-year
yields before proceeding to the ten-year yields.
Two-year yields On panel A of Table 5, we observe the impact of FyF announcements over yields.
Recall that we quantify movements between funds A to E (i.e., riskier to safer) with a positive number.
Therefore, these movements impose a buying pressure in the market for government bonds. The increase
in demand for these instruments must generate a decrease in their yields, which is what we observe in
our analysis. In the case of the nominal two-year yield, we observe a statistically significant negative
impact of 1.3 basis points associated with FyF recommendations, consistent with the pressure imposed
in the market. However, as we note in the following columns, while still negative, the effects on real and
breakeven yields have a lower magnitudes and no statistical significance. In terms of the elements that
compose the yields, we see a positive effect on the risk-neutral component (except for the breakeven yield)
and a negative effect on the term premium component, with no significance in any yield of the two-year
maturity.
24

Table 5: Impact response of yields on different announcements
This table presents the impact response of different yields and its components on macroeconomic and FyF announcements
by estimating Eq. (4), with h = 0. Panel A shows the coefficient associated with FyF surprises. Panel B shows the
coefficient associated with CPI surprises. Panel C shows the coefficient associated with activity surprises. Each panel
presents results for the two- and ten-year yields. Fit corresponds to the fitted yield after using the model decomposition. RN
and TP correspond to the risk neutral and term premium components, respectively, which are obtained from the empirical
decomposition of the nominal/real yield curves (see main text and the Appendix for details). All macroeconomic surprises
are expressed in deviation with respect to mean and divided by their standard deviation. All regressions control for surprises
in monetary policy releases, unemployment releases, five lags of the dependent variable, and contemporaneous value and five
lags of (the log of) nominal exchange rate, VIX and WTI price index (coefficients not reported). Heteroskedasticity and
autocorrelation consistent standard errors (Newey-West) are in parentheses considering 10-day lag. *, ** and *** denote
statistical significance at the 1, 5 and 10% levels, respectively.
Nominal Real Breakeven
2y 10y 2y 10y 2y 10y
Panel A: FyF
Fit -1.28∗
-1.32∗∗
-0.41 -1.78∗∗
-0.93 0.48
RN 0.23 0.60 1.19 1.39∗
-0.88 -0.78∗∗
TP -1.72 -2.01∗∗
-1.48 -3.21∗∗
-0.10 1.24
Panel B: CPI
Fit 3.66∗∗∗
3.12∗∗∗
-8.06∗∗∗
-0.87∗∗∗
11.72∗∗∗
4.05∗∗∗
RN 7.48∗∗∗
1.32∗∗∗
3.55∗∗∗
0.72∗∗
3.95∗∗∗
0.61∗∗∗
TP -3.86∗∗∗
1.81∗∗∗
-11.68∗∗∗
-1.61∗∗∗
7.76∗∗∗
3.45∗∗∗
Panel C: Activity
Fit 0.44 1.44∗∗∗
0.34 0.95∗∗∗
0.19 0.42
RN -0.51 -0.37 -0.81 -0.75∗∗
0.28 0.37∗∗∗
TP 1.23 1.82∗∗∗
1.28 1.78∗∗∗
-0.10 0.05
What is the effect of macroeconomic announcements on the two-year yields? Starting with panel B,
we observe a strong positive effect on the nominal two-year yields for CPI announcements, namely of 3.7
basis points. This effect is due to an increase in the RN component (7.5 bp) ameliorated by a decrease
in TP (-3.9 bp). The positive inflationary surprise reduces the demand on nominal instruments, which
is consistent with an increase in its yield. In the case of real instruments, we observe the opposite effect
because they provide insurance against inflation. On impact, there is a decrease in the observed yield,
namely of 8.1 bp, which comes from an increase in the RN component (3.6 bp) and strong decrease in its
TP (-11.7 bp). Consistent with the previous observations, we report a positive effect on the breakeven
inflation for the two-year instruments, namely of 11.7 bp, with increments in both the RN and the TP
components.15 Note that all these impact responses on the two-year yields after positive inflationary
surprises are statistically significant.
In terms of activity surprises (panel C), we observe a common pattern between the nominal and real
yields. In both cases, there is a small increase in the observed yield, attributed from a decrease in the RN
component which is compensated by an increase in their TP, with the exception of breakeven yields, which
show the opposite sign for each component. All these effects are quantitatively smaller in comparison
15
Recall that this is computed as the difference between the nominal and the real yield of instruments of the same maturity.
25

with FyF and CPI events, and none of them are statistically significant.
Ten-year yields Now we study the impact effect over long-term yields after different releases. Starting
with the case of the FyF recommendations events in panel A, we observe a negative impact effect on the
nominal yields of -1.3 basis points. As before, in the case of short-term maturity yields, these negative
effects come from a positive response of the RN component, which is dominated by a stronger negative
impact of the TP component. Note that the magnitudes are comparable to the ones observed for the
two-year yields. A similar pattern is observed for real bonds, with a negative response of -1.8 basis points.
As in the case of nominal yields, these responses are decomposed into a positive response of the RN
component and negative response in the TP. Unlike the two-year yields, the magnitude of the ten-year
yield is at least twice as large. Finally, the breakeven yields show no significant impact for long-term
yields.
Compared with CPI announcements (panel B), we observe the following: Nominal ten-year yields
show a strong reaction after inflationary surprises. On impact, we observe a 3.1 bp increase, which is
accompanied by an increase in the RN and TP components of about similar magnitude. However, this
effect does not hold for real yields. The ten-year bonds show a decrease in their yield (-0.9 bp), which is
due to a decrease in TP (-1.6 bp) dampened by an increase in RN (+0.7 bp). Interestingly, the response
of ten-year yields is 0.5 bp higher than that of FyF recommendation events. Finally, breakeven rates show
a strong impact-response with CPI announcements of 4.0 bp, with TP as the dominating component.
Relative to activity announcements (panel C), we observe responses in long-maturity instruments that
were not observed in the case of short-term maturity bonds. In the case of nominal yields, we observe an
increase of 1.4 bp. In both cases, the TP increment dominates the reduction of the RN component. A
similar pattern is observed for inflation linked bonds, with an observed increment of 1.0 bp. Again, the
RN component dampens the increase of the TP. Note that as in the case of CPI shocks, the response of
yields after activity surprises is lower than in the case of the FyF releases, both in terms of the observed
impact and for changes in each component as well. Finally, long-term yields do not respond significantly
to activity surprises.
As mentioned in section 2, AFPs have around four days to apply the changes required by contributors.
Therefore, we interpret the impact effect of the FyF recommendations previously described (computed as
the effect in h = 0 of Eq. 4) as a lower bound of the effect of those recommendations. This lower bound
is especially pronounced for the long-term yields, as shown in Table C.1 in the Appendix. In particular,
we observe that the FyF effect is stronger than both CPI and activity effects for the nominal and real
yields at the ten-year maturity, which is particularly significant for real yields.16
16
This also holds for the two-year real yields.
26

From this analysis we conclude the following. In terms of impact effect, FyF announcements have
a second order impact on short-term rates for both nominal and real yields. The impact observed in
those instruments can be attributed mostly to CPI surprises. On the other hand, FyF also have an effect
on long-term bonds (ten-year yields). While both nominal and real yields decrease on impact, driven
by a decrease in their TP components, we do not observe significant changes on breakeven inflation
rates. Both CPI and activity announcements move yields in opposite directions: in the case of nominal
yields, CPI (activity) is associated with decreases (increases) real yields. In terms of magnitudes, nominal
long-term yields react by a smaller magnitude after FyF announcements compared to macroeconomic
announcements. However, the opposite is observed in real yields, which are the focus of this paper.
5.2 Persistence
While the previous section shows a significant impact effect of the FyF announcements on different
instruments, which at times is stronger than macroeconomic surprises, we now ask whether these effects
are persistent over time. To capture persistence, we focus solely on FyF switching recommendations events
and present impulse-responses after the FyF recommendation for the same instruments as analyzed before,
separating between the two-year yields (which is the relevant horizon for monetary policy) in Figure 4,
and the ten-year yields (which correspond to the relevant horizon for benchmark rates in the economy)
in Figure 5. Each figure shows the response of the corresponding yield after the FyF announcement in
a 60-day horizon. We also present 90% confidence intervals to denote the statistical significance of each
response. The y-axis on each figure are in basis points.
Two-year yields We start the analysis with yields relevant for the monetary policy in Chile (two years)
in Figure 4. Each row presents results for nominal, real and breakeven yields, while columns present results
for each component (observed, RN and TP). In the case of nominal yields, we see a persistent decrease over
time that reaches -20 bp in the 60-day horizon. Even though there is high variability in these responses,
results are statistically significant. In terms of composition, we observe that RN and TP components
decrease after 20 days of the release, so both contribute to the observed effect. However, it seems that
neither is individually (statistically) significant.
In the second row of the figure, we observe the responses of inflation-linked bonds. As in the case
of nominal bonds, we observe a strong decreasing pattern in the response of short-term yields, dropping
to -20 bp after 60 days. Unlike nominal yields, these effects are significant for only 30 days after the
recommendation. The total effect comes from decreases in both components, but neither component is
significant on their own.
Finally, at the bottom row of the figure, we observe the response of the breakeven inflation rates.
27

Observed responses fluctuate around zero over time. While the RN component increases after 30 days,
TP decreases. None of the responses (neither observed nor their components) are statistically significant.
From this figure, we conclude that, even though the FyF recommendations have persistent effects
on yields of short-term benchmark instruments, these are not significant. Both nominal and real yields
decrease by 20 bp when there is buying pressures, with both RN and TP components moving in the same
direction. However, the high variability in the estimates imply no statistical significance in the response
of either component.
Ten-year yields In Figure 5, we present the same analysis for the long-term yields, which serve as the
benchmark for setting cost of funding for firms and households. Nominal yields show a significant and
persistent decrease over time. For example, we observe a decrease in nominal rates close to 15 bp 60 days
after the FyF recommendation. This effect is driven completely by decrease in TP component of similar
magnitude and statistical significance.
A similar trend is observed for real yields in the second row. The FyF recommendations generate a
significant decrease in yields of 15 bp in the 60-day horizon. Unlike nominal bonds, the RN component
exhibits a positive effect, with increases of 0 to 5 bp. Therefore, the TP shows a stronger reaction after
these announcements, which are below the 15 bp decrease of the observed yields.17
From these exercises, we conclude the following. First, FyF recommendations show a significant effect
both on impact and over time. Second, these effects are stronger on long-term maturity yields than in
short-term yields, therefore, they may generate effects on other market rates (e.g. corporate bonds yields
and mortgage rates) that are more affected by changes in long-term yields. Third, in terms of channels,
even though the RN components of long-term horizon yields tend to increase, the whole observed effect
in observed yield is driven by decreases in TP. This implies that the FyF recommendations generate
pressures in yields through risk-premia and not by changes in expectations.
17
Breakeven inflation rates show a (not statistically significant) decrease in the horizon of the analysis, which is driven by
a decrease in both the RN and TP components.
28

Figure 4: Dynamic effect of FyF announcements: two-year yields
This figure plots the βh
coefficient in Eq. (4), associated with the FyF announcements (impulse-response function) for two-
year yields. Columns present results for fitted yield after using the model decomposition, risk neutral component (rn), and
term premium component (tp), respectively. Rows present results for nominal bonds (BCP), inflation-linked bonds (BCU)
and breakeven inflation (BI), respectively. The y-axis in basis points. The x-axis corresponds to days. The grey region
corresponds to 90% confidence intervals.
−40−30−20−100
0 20 40 60
BCP−fit
−40−20020
0 20 40 60
BCP−rn
−30−20−10010
0 20 40 60
BCP−tp
−50−40−30−20−100
0 20 40 60
BCU−fit
−40−20020
0 20 40 60
BCU−rn
−40−2002040
0 20 40 60
BCU−tp
−40−2002040
0 20 40 60
BI−fit
−1001020
0 20 40 60
BI−rn
−30−20−1001020
0 20 40 60
BI−tp
29

Figure 5: Dynamic effect of FyF announcements: ten-year yields
This figure plots the βh
coefficient in Eq. (4), associated with the FyF announcements (impulse-response function) for ten-
year yields. Columns present results for fitted yield after using the model decomposition, risk neutral component (rn), and
term premium component (tp), respectively. Rows present results for nominal bonds (BCP), inflation-linked bonds (BCU)
and breakeven inflation (BI), respectively. The y-axis in basis points. The x-axis corresponds to days. The grey region
corresponds to 90% confidence intervals.
−30−20−100
0 20 40 60
BCP−fit
−505
0 20 40 60
BCP−rn
−30−20−100
0 20 40 60
BCP−tp
−20−15−10−50
0 20 40 60
BCU−fit
−50510
0 20 40 60
BCU−rn
−25−20−15−10−50
0 20 40 60
BCU−tp
−15−10−50510
0 20 40 60
BI−fit
−6−4−202
0 20 40 60
BI−rn
−15−10−50510
0 20 40 60
BI−tp
5.3 Impact on expectations
In the previous section, we document relevant impact on yields mainly through changes in term premia.
We also show that the risk-neutral (expectation) response is small, which also holds for measures of
inflation expectation at different horizons. In this section, we complement our analysis by focusing on the
impact of yields and inflation expectation at different horizons based on the EES.
In Table 6, we report the results for inflation expectation (CPI) at the two-years ahead and the five-
year inflation expectation one-year and two-year ahead. For each measure of inflation expectation, we
estimate equation (5) by using the contemporaneous effect of FyF’s recommendation at month t, which
captures the impact on the same month of the FyF recommendations, allowing to capture effect of the
switching advice on different measures of inflation expectation. We also include leads of FyF (t + 1 and
t + 2) to capture posterior effects or reversions. In the case of inflation expectation at the two-year ahead
30

(relevant for monetary policy) we find that FyF’s recommendation have no statistically significant effect
on neither contemporaneous (month t) nor subsequent months.
Similarly, for the five-year inflation expectations one-year ahead and two-year ahead, we document
that inflation expectations at those horizons have no statistical significant effect. Overall, our evidence
suggests that switching recommendation advices by the FyF have a null impact on inflation expectations.
In Table 7, we present the results for expected five-year nominal and real yields one-year (Panel A)
and two-year ahead (Panel B). The first column exhibits the impact of FyF’s recommendation on the
expected five-year nominal yields while the second column exhibits the results for the expected five-year
real rate. Our results suggest a similar pattern observed in government yields. Here, we document a
relevant impact on both nominal and inflation-linked yields. In particular, for nominal bonds, we report
a 6 and 10 basis points increase from t to t + 2 depending on the horizon (one or two years ahead).
Similarly, for inflation-linked bonds, we document an increase of 8 to 9 basis points by t + 2 depending
on the horizon. Thus, the evidence presented is consistent with our findings in the previous section using
decomposition of yields.
6 Implications for funding costs
In the previous section, we reported the relevance of the FyF switching recommendation on government
bond yields, in particular, in inflation-linked bond yields through term premia component. As discussed
previously, movements generated by recommendations have a significant impact on benchmark yields.
Now we explore the implications of those movements on the cost of financing for private agents, starting
with the analysis of firms’ financing costs.
6.1 Impact on financing for firms
In this section, we analyze the impact of the FyF recommendations on corporate credit spreads. In this
context, we measure credit spread as the difference between corporate bond yields and a benchmark
government bond with a similar maturity. We focus our analysis on the asymmetric effect on credit
spreads. That is, whether recommendations that induce a buying price pressure exhibits different impacts
compared to recommendations that induce selling pressure. We also focus on the asymmetric effect of
the FyF switching recommendation. As we documented in section 3.2, we observe a symmetric response
in both the nominal and inflation-linked bonds associated with the FyF recommendations. Moreover, we
also analyze the impact of credit spreads differentiated by their credit risk. We focus on bonds ranging
from high-quality bonds (AAA) to lower-quality bonds (BBB).18
18
As common in the literature, we exclude financial bonds from our sample
31

Table6:ImpactofFyFrecommendationoninflationexpectations.
ThistablepresentstheresultswhenweregressinflationexpectationsonFyFreleases,usingindividualleveldatafromtheEconomicExpectationsSurvey(EES).PanelA
presentsresultsforexpectationsatthetwo-yearsahead.PanelBandPanelCshowresultsforthebreakeveninflationratesatthe12-and24-monthsahead,respectively.
Columnt+h,forh=0,...,2denotesleadsoftheFyFvariable.Allregressionsincludeonelagofthedependentvariableandcontrolformonetarypolicy,inflation,
activityandunemploymentsurprises,aswellasmacroeconomicvariables(nominalexchangerate,WTI,andVIX).Robuststandarderrorsclusteredattheforecaster
levelinparentheses.*,**and***denotestatisticalsignificanceatthe1,5and10%levels,respectively.
AnnualCPI24-monthaheadFive-yearBI12-monthaheadFive-yearBI24-monthahead
tt+1t+2tt+1t+2tt+1t+2
FyF-0.56-1.26-0.45-2.23-2.09-2.241.420.730.96
(1.42)(1.25)(1.10)(2.66)(2.14)(1.77)(2.69)(2.10)(1.86)
MPrate(MPR)5.80**-1.99-1.734.48-6.45***-9.75***5.65*-5.16***-3.64*
(2.42)(1.73)(1.69)(3.18)(1.84)(2.11)(2.86)(1.65)(1.97)
Inflation(CPI)12.96***7.70***1.768.57**5.37-7.62*1.720.01-4.02
(4.37)(2.36)(2.71)(3.85)(3.24)(4.23)(4.23)(3.25)(3.70)
Activity(Y)0.45-1.72*-1.451.22-2.52*-2.55**2.56*1.26-0.62
(1.19)(0.87)(0.92)(1.31)(1.40)(1.26)(1.52)(1.41)(1.30)
Unemployment(U)9.30***4.425.67*4.929.37**3.452.004.79-0.83
(3.16)(2.66)(3.10)(3.18)(3.67)(3.43)(3.72)(3.96)(3.21)
Exchangerate(ner)0.330.01-0.031.24***0.440.421.00**-0.100.28
(0.40)(0.29)(0.22)(0.47)(0.50)(0.39)(0.41)(0.50)(0.39)
WTI-0.27***-0.17*-0.21**-0.20-0.15-0.33***-0.08-0.10-0.17*
(0.10)(0.09)(0.10)(0.13)(0.09)(0.10)(0.15)(0.11)(0.10)
VIX-0.11**-0.05-0.02-0.26***-0.18***-0.15***-0.13**-0.08-0.12***
(0.05)(0.04)(0.04)(0.06)(0.05)(0.04)(0.06)(0.06)(0.04)
Obs545753625335543153625331520951275092
Adj.R20.0170.0130.0200.0180.0380.0500.0140.0190.027
32

Table 7: Impact of FyF recommendation on yields expectations.
This table presents the results when we regress inflation expectations on FyF releases, using individual level data from the
Economic Expectations Survey (EES). Panel A presents results for expectations at the one-year ahead while Panel B shows
results for expectations at the 24-months ahead. First set of columns show results for nominal rates, while second set of
columns present results for real yields. All yields are of five-year maturity. Column t+h, for h = 0, . . . , 2 denotes leads of the
FyF variable. All regressions include one lag of the dependent variable and control for monetary policy, inflation, activity
and unemployment surprises, as well as macroeconomic variables (nominal exchange rate, WTI, and VIX). Robust standard
errors clustered at the forecaster level in parentheses. *, ** and *** denote statistical significance at the 1, 5 and 10% levels,
respectively.
Nominal Five-year rate Real Five-year rate
t t+1 t+2 t t+1 t+2
Panel A: One-year ahead
FyF -9.89*** -8.83*** -10.64*** -7.85*** -6.90*** -8.68***
(2.48) (2.45) (2.08) (1.50) (1.48) (1.22)
Monetary Policy Rate (MPR) 15.22*** -11.11*** -12.00*** 6.99*** -3.82** -5.25***
(4.26) (1.80) (3.76) (2.63) (1.73) (1.56)
Inflation (CPI) 15.28*** 12.46*** -7.18 6.95** 8.23** 0.90
(4.55) (4.19) (4.49) (3.11) (3.27) (2.66)
Activity (Y) 1.72 0.57 -3.57*** 0.92 2.56** -0.48
(1.22) (1.34) (1.32) (1.21) (1.01) (0.94)
Unemployment (U) 4.49 8.22* -0.58 -0.78 -1.77 -2.89
(3.49) (4.17) (4.10) (2.84) (2.86) (2.88)
Exchange rate (ner) 1.69*** 0.90 1.29** 0.58* 0.40 0.74***
(0.60) (0.56) (0.51) (0.30) (0.34) (0.27)
WTI -0.30** -0.20 -0.30** -0.01 -0.00 0.03
(0.14) (0.14) (0.11) (0.10) (0.09) (0.08)
VIX -0.45*** -0.25*** -0.25*** -0.14** -0.05 -0.06
(0.07) (0.06) (0.05) (0.05) (0.04) (0.04)
Obs 5447 5380 5350 5468 5406 5379
Adj. R2 0.039 0.040 0.060 0.029 0.049 0.053
Panel B: Two-year ahead
FyF -5.91** -3.50 -8.03*** -7.40*** -4.38*** -9.08***
(2.63) (2.35) (1.91) (1.89) (1.59) (1.82)
Monetary Policy Rate (MPR) 6.58*** -10.86*** -7.49** 0.24 -5.27*** -7.23***
(2.35) (2.21) (3.43) (3.26) (1.97) (1.71)
Inflation (CPI) 5.22 1.23 -7.19* 3.94 2.42 -3.13
(5.07) (3.97) (3.97) (3.61) (4.05) (2.96)
Activity (Y) 2.12 3.11** -0.54 -0.07 1.48 0.31
(1.37) (1.37) (1.31) (1.37) (1.28) (1.10)
Unemployment (U) 1.87 5.16 -3.12 -0.62 0.67 -2.25
(4.51) (3.96) (3.81) (3.46) (3.49) (3.70)
Exchange rate (ner) 1.28** 0.73 0.41 0.22 0.61* 0.12
(0.51) (0.55) (0.49) (0.36) (0.34) (0.25)
WTI -0.14 -0.02 -0.27** -0.04 0.07 -0.04
(0.12) (0.14) (0.11) (0.13) (0.09) (0.08)
VIX -0.20*** -0.14** -0.18*** -0.05 -0.05 -0.00
(0.07) (0.07) (0.06) (0.06) (0.04) (0.04)
Obs 5224 5146 5114 5249 5173 5141
Adj. R2 0.014 0.023 0.032 0.013 0.028 0.032
33

Our main specification is as follows:
∆CSj,t+h = αh
j + βh
j FyFt +
4
j=1
γh
j Sj,t +
n
i=1
δh
j ∆CSj,t−i +
n
i=0
θh
j Xt−i + εh
j,t, (6)
where ∆CSj,t+h corresponds to the cumulative difference between days t − 1 and t + h, with h =
0, . . . , 30, for corporate bonds with different credit risk j (for AAA, AA, A and BBB). Note that equation
(6) is similar to equation (4), but now using credit spreads as dependent variable. We use the same
number of lags and the same controls as in that specification.
Table 8 reports the main results. We exhibit the βh
j coefficient from equation (6) by different corporate
credit risk categories from AAA to BBB. In each credit risk category, we split the analysis by running
the regression over the total FyF switching recommendations (column Total) and by estimating the same
regression but differentiating between buying and selling switching recommendation advises (columns Buy
and Sell, respectively). In addition, we report the βh
j for different horizons (h) from 1, 2, 3, 4, 5, 10, 15,
20, 25 and 30 days after the FyF event. The last two rows of the table report the average numbers of
bonds used to construct the aggregate index for each bond risk category as well as the average duration
of the total outstanding corporate bond market.
We highlight two results. First, we document an asymmetric impact of FyF’s price pressure on the
credit risk market. By analyzing the overall effect (column Total), we document a small impact and slightly
statistically significant effect depending on the credit risk spread category. For instance we find an impact
of 4 and 12 basis (significant at 10%) points on credit spreads for AA and A bonds, respectively, and no
significant effect for AAA and BBB bonds up to 30 days after the event. However, in events of buying
price pressure (Buy column), we document that corporate credit spreads increase up to 12 basis points
(depending on the rating) and do not revert in a horizon of up to 30 days after the FyF recommendation.
For instance, for credit rating AA and A, we exhibit an increase of 9 and 12 basis points, significant at
the 1% level. In contrast, in days of selling pressure, we do not find significant changes in credit risk for
all horizons and credit risk category.
Second, we find a stronger effect in more risky debt. For high-quality corporate bonds (AAA), we
find neither asymmetric effect nor statistical significance. However, the effect on credit spread is stronger
when credit-quality deteriorates (from AA to BBB). The impact up to 5 days after the FyF event is 0,
2, 4 and 4 for bonds from AAA, AA, A and BBB, respectively, whereas at horizon of 30 days the impact
increased to 4, 9, 12 and 13 for AAA, AA, A and BBB bonds, respectively. This upward effect is observed
only in events related to the buy price pressure. We do not find statistical significance for the riskier debt
(BBB), which can be explained by the small numbers of corporate bonds under this classification and the
34

Table8:ImpactofFyFonCreditSpreads.
ThistablepresentstheresultswhenweregresscreditspreadsonFyFreleases.PanelAtoDpresentsresultsconditionaloncreditrisk,beingAAA(BBB)corporatebonds
withthelowest(highest)risk.EachpanelseparatesregressionsfortotalFyFreleases(firstcolumn),Buyreleases(secondcolumn)andSellreleases(thirdcolumn).Each
rowshowstheimpactatdifferenthorizons(h).Atthebottomofeachpanelwepresentdescriptivestatisticsoftheratescomposingeachkindofbond(numberofbonds
anddurationofthetotaloutstandingcorporatebonds).Allregressionscontrolforsurprisesinmonetarypolicyreleases,unemploymentreleases,fivelagsofthedependent
variable,andcontemporaneousvalueandfivelagsof(thelogof)nominalexchangerate,VIXandWTIpriceindex(coefficientsnotreported).Heteroskedasticityand
autocorrelationconsistentstandarderrors(Newey-West)inparenthesesconsidering10dayslag.*,**and***denotestatisticalsignificanceatthe1,5and10%levels,
respectively.
PanelA:AAAPanelB:AAPanelC:APanelD:BBB
Horizon(h)TotalBuySellTotalBuySellTotalBuySellTotalBuySell
10.79*0.57-1.030.90*0.34-1.52**0.650.82*-0.481.900.19-3.80
(0.43)(0.55)(0.66)(0.53)(0.68)(0.77)(0.45)(0.48)(0.77)(1.42)(0.96)(2.73)
20.761.30**-0.161.231.60*-0.810.341.71**1.192.411.58-3.33
(0.61)(0.59)(1.05)(0.77)(0.87)(1.26)(0.90)(0.72)(1.58)(1.56)(1.57)(2.76)
30.891.48-0.242.38**3.26**-1.411.423.47***0.862.622.37-2.91
(0.95)(1.02)(1.62)(1.16)(1.65)(1.56)(1.08)(1.27)(1.41)(2.00)(1.78)(3.69)
41.44*1.88-0.952.67**3.79**-1.442.26**4.49***0.222.352.21-2.51
(0.83)(1.23)(1.08)(1.04)(1.53)(1.29)(1.03)(1.31)(1.17)(1.90)(2.00)(3.29)
50.610.28-0.981.852.13-1.531.533.70**0.882.484.13*-0.66
(1.11)(1.77)(1.34)(1.28)(2.14)(1.28)(1.24)(1.86)(1.14)(2.08)(2.27)(3.39)
100.962.100.271.863.92*0.373.08*6.89***1.034.109.45*1.69
(1.73)(1.70)(2.97)(1.46)(2.04)(1.87)(1.73)(1.92)(2.33)(3.60)(5.10)(4.14)
150.020.490.483.54*4.29**-2.723.58*6.39***-0.554.357.47-0.98
(2.10)(2.15)(3.66)(1.92)(2.18)(3.15)(1.89)(1.94)(3.03)(3.51)(4.65)(5.24)
201.422.920.203.94*6.85**-0.794.29*8.83***0.616.8012.92*-0.18
(2.23)(2.92)(3.27)(2.29)(3.45)(2.73)(2.58)(2.99)(3.81)(4.72)(6.80)(6.49)
252.794.61-0.764.70*8.29**-0.715.68*10.82**0.034.2811.653.89
(2.43)(3.90)(2.61)(2.50)(3.59)(3.25)(3.01)(4.23)(3.83)(6.16)(8.89)(8.07)
302.404.740.204.30*8.68***0.576.09*12.46***0.993.9113.396.62
(2.35)(3.73)(2.69)(2.45)(3.29)(3.31)(3.30)(4.19)(4.56)(6.90)(10.99)(7.38)
#Bonds411238515
Duration9.16.76.24.9
35

shorter average maturity.19
Overall, this evidence suggests that the pass-through of changes in funding costs (government bond
yields) increases the cost of funding for firms. Thus, our results supports the idea that changes in credit
risks are driven by local supply/demand shocks independent of common credit-risk factors (Collin-Dufresn
et al., 2002).
6.2 Impact on financing for households
In this section, we estimate the impact of FyF recommendations on mortgage loan rates, the most
important liabilities for households.
The mortgage credit in Chile exhibits three main mortgages types20; mortgage bonds, endorsable
and non-endorsable mortgage loans. Mortgage bonds are regulated by the Central Bank financial rules
and specific regulations by the Financial Market Commission (CMF). These rules allow banks to finance
mortgages with third-party resources but force them to originate loans on their balance sheets and transfer
the risk of nonpayment to investors. Endorsable mortgage loans are regulated by general regulations
(according to the General Bank’s laws) and specific CMF regulations. Endorsable mortgage loans are
subject to a loan-to-value (LTV) ratio of 80 percent, fire and life insurance, and prepayment rules. Finally,
non-endorsable mortgage loans do not present any special requirement. They do not have a loan-to-value
limit, insurance requirement and they have the same repayment restrictions as endorsable loans. They
are the most flexible instrument for mortgage loans, making them for customers with special need more
attractive. In fact, 95% of total new mortgages loans were originated as non-endorsable types.
To assess the impact on the mortgage spread (the mortgage rate over the corresponding inflation-linked
government bond yield), we follow the common approach used in empirical literature (Page, 1964; Sirmans
et al., 2013). In particular, we consider variables that captures relevant macroeconomic conditions (e.g.
activity and unemployment) by estimating the following specification:
∆MSj,t+h = αh
j + βh
j FyFt +
n
i=1
δh
j ∆MSj,t−i +
n
i=0
θh
j Xt−i + εh
j,t, (7)
where ∆MSj,t+h corresponds to the cumulative difference between month t − 1 and t + h, with
h = 0, 1, 2, for mortgage rate over the inflation-linked bond with similar maturity. We control for variables
that may affect the risk of default or prepayment captured by macroeconomic conditions (unemployment
and monthly GDP) as well as for variables that capture the riskiness of the overall mortgage loan credits
19
We exclude from our analysis non-investment grade bonds (BB or lower) because they are infrequently traded and much
smaller than the BBB bond category.
20
A detailed review of the mortgage market in Chile is discussed by Micco et al. (2012).
36

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