This paper investigates the dynamic interaction between ination and stock returns in four ination targeting countries. We find that following the introduction of formal targets, ination persistence and the magnitude of volatility spillovers between ination and stock returns have been reduced.

Alberto Montagnoli

Alexandros Kontonikas

Nicola Spagnolo

English

Research Papers in Economics

STOCK RETURNS AND INFLATION: THE IMPACT OF
INFLATION TARGETING
Alexandros Kontonikas￿ a, Alberto Montagnolib and Nicola Spagnoloc
a Department of Economics, University of Glasgow, Glasgow, UK
bDepartment of Economics, University of Stirling, UK
c Brunel Business School, Department of Economics and Finance , London, UK
August 24, 2006
Abstract
This paper investigates the dynamic interaction between in￿ ation and stock returns
in four in￿ ation targeting countries. We ￿nd that following the introduction of formal
targets, in￿ ation persistence and the magnitude of volatility spillovers between in￿ ation
and stock returns have been reduced.
Keywords: Multivariate GARCH, In￿ ation, Stock prices, Volatility.
JEL Classi￿cation: C22; E31; E44; E52
1 Introduction
Ever since the early 1990s, some countries adopted in￿ ation targeting as a new monetary
policy strategy. So far, these regimes are claimed to be a success since in￿ ation persistence
declined, in￿ ation rates became lower and less volatile, and in￿ ation expectations were an-
chored at low levels1.
Furthermore, it is expected that a regime consistent with low and stable in￿ ation tends,
as a by-product, to promote ￿nancial market stability (Bordo and Wheelock, 1998). Given
that stock returns measure nominal payo⁄s, ￿when in￿ ation of goods￿prices is uncertain, the
volatility of nominal asset returns should re￿ ect in￿ ation volatility￿(Schwert, 1989, p.1124),
hence lower in￿ ation variability should exert a calming e⁄ect on stock market volatility.
This view draws support from a theoretical literature that emphasizes the importance of
informational asymmetries in credit markets and shows how higher in￿ ation adversely a⁄ects
credit market frictions with negative consequences for ￿nancial sector performance (see e.g.
Huybens and Smith, 1999)2.
￿Corresponding author: A. Kontonikas. Tel: +44 141 330 6866. Fax: +44 141 330 4940. Email:
a.kontonikas@lbss.gla.ac.uk
1See Kontonikas (2004) for UK evidence.
2Moreover, a number of empirical studies ￿nds a signi￿cant relationship between high, sustained rates of
in￿ ation and ￿nancial crises; see among others, Boyd et al. (2001).
1Despite the success in controlling in￿ ation, during the late 1990s-early 2000 international
capital markets witnessed large swings in stock prices generating concern among academics
and policy-makers about the impact of stock price movements on the real economy and the
broader consequences of in￿ ation targeting. Kontonikas and Ioannidis (2005) show that an
in￿ ation targeting regime with strong interest rate reaction to in￿ ation should lead to lower
stock market volatility. On the other hand, the New Environment Hypothesis (NEH, see
e.g. Borio and Lowe, 2002) claims that in an economic environment characterised by low
in￿ ation, unsustainable ￿nancial imbalances may build up since investors, exhibiting money
illusion, consider that the real cost of capital has been signi￿cantly reduced. Exponents of
the NEH argue that price stability is not a su¢ cient condition for stock market stabilisation
and, in fact, the absence of obvious in￿ ationary pressures adds to the sustainability of the
stock market booms, by removing the threat of interest rate increases.
The novelty of this paper consists in jointly modelling the dynamic interaction between
in￿ ation and stock returns using a VAR-GARCH speci￿cation that allows for the impact of
in￿ ation targeting to be explicitly taken into account. In the debate between the standard
view that emphasizes the stabilising e⁄ects of in￿ ation targeting and the NEH, important
answers lie in the statistical identi￿cation of the volatility spillovers between in￿ ation and
stock returns. A decrease in the magnitude of volatility spillovers from in￿ ation to stock
returns, following the introduction of in￿ ation targeting, would imply further support for the
bene￿ts of this monetary policy framework.
2 Data
Our data comprises of four OECD countries (Australia, Canada, Sweden, United King-
dom) that have announced an in￿ ation target3. We measure nominal stock returns , rt,
and in￿ ation, ￿t, as the ￿rst di⁄erence of the natural logarithm of the stock price index
(SPI) and the consumer price index (CPI), respectively: rt = 100 ￿ (lnSPIt ￿ lnSPIt￿1),
￿t = 100 ￿ (lnCPIt ￿ lnCPIt￿1)4.
3 Econometric model and results
Engle and Kroner (1995) propose a class of multivariate GARCH models, the BEKK, with the
special property of ensuring a positive de￿nite conditional variance matrix. Following Engle
and Kroner (1995), we model the joint processes governing stock returns, rt, and in￿ ation,
￿t, using the following bivariate VAR-GARCH(1,1) speci￿cation:
xt = ￿ + ￿xt￿1 + ut (1)
where xt = (￿t;rt)
0
; and the residual vector ut = (e1;t;e2;t)
0
follows a bivariate Normal
distribution, with its corresponding conditional variance covariance matrix given by:
3In￿ ation targeting commenced on the following dates: Australia 1994-Q3; Canada 1991-M2; Sweden
1995-M1; United Kingdom 1992-M10.
4For Canada, Sweden, and the United Kingdom we use monthly data over the period 1980:12-2004:4. For
Australia, where only quarterly data are available, the sample is 1980:Q4-2004:Q4.
2Ht =
"
h1t h12t
h12t h2t
#
(2)
The parameter vector of the mean equation (1) is de￿ned by the constant ￿ = (￿1;￿2)
0
and the matrix of coe¢ cients ￿ =(￿11;￿12 j ￿21;￿22), while the parameter matrices for the
variance equation (2) are de￿ned as C, which is restricted to be upper triangular, and two
unrestricted matrices A and G: In order to account for the e⁄ects of in￿ ation targeting
on the time series structure of in￿ ation and stock returns and the volatility transmission
mechanism, we include a dummy variable (denoted by a star) for the autoregressive and
cross-e⁄ects parameters in the conditional mean as well as the cross-e⁄ects in the conditional
variance5. The dummy is equal to zero prior to the adoption of in￿ ation targeting and one
thereafter. Hence, the ￿rst and second moments will take the forms given by Eq. (3) and (4)
respectively:
"
￿t
rt
#
=
"
￿1
￿2
#
+
"
(￿11 + ￿￿
11) (￿12 + ￿￿
12)
(￿21 + ￿￿
21) (￿22 + ￿￿
22)
#"
￿t￿1
rt￿1
#
+
"
e1;t
e2;t
#
(3)
Ht = C
0
C +
"
g11 (g12 + g￿
12)
(g21 + g￿
21) g22
#0
Ht￿1
"
g11 (g12 + g￿
12)
(g21 + g￿
21) g22
#
+
"
a11 (a12 + a￿
12)
(a21 + a￿
21) a22
#0 "
e2
1;t￿1 e1;t￿1e
0
2;t￿1
e1;t￿1e
0
2;t￿1 e2
2;t￿1
#"
a11 (a12 + a￿
12)
(a21 + a￿
21) a22
#
(4)
Equation (4) models the dynamic process of Ht as a linear function of its own past values
Ht￿1 and past values of the squared innovations
￿
e2
1;t￿1;e2
2;t￿1
￿
, in both cases allowing for
own and cross in￿ uences in the conditional variance. This speci￿cation (with the unrestricted
matrices A and G) allows the conditional variances and covariances of in￿ ation and stock
returns to a⁄ect each other, thereby enabling one to test the null hypothesis of no volatility
spillover e⁄ects in one or even both directions.
[Table 1]
The estimated VAR-GARCH(1,1) model with associated robust standard errors (see
Bollerslev and Wooldridge, 1992) and likelihood function values are presented in Table 1.
Tests for causality-in-variance are carried out for each model, alternatively constraining the
matrices A and/or G to be upper triangular or lower triangular, thereby allowing for causal-
ity only in one direction at a one time. Hypothesis testing is performed using a likelihood
ratio test (LR). Appropriate empirical critical values are computed by means of bootstrap-
ping6. The null hypothesis of unidirectional cross-market spillovers is rejected for all sample
countries. Therefore, an unrestricted speci￿cation that allows for bi-directional spillovers is
5Caporale and Spagnolo (2003) employ a similar VAR-GARCH speci￿cation to investigate the real e⁄ects
of ￿nancial crises.
6We ￿nd that the LR test has ￿nite-sample Type-I error probabilities that do not di⁄er signi￿cantly from
the nominal value of 0.05, with empirical rejection frequencies reasonably close to the corresponding asymptotic
ones.
3preferred. Furthermore, Ljung-Box statistics show no sign of remaining serial correlation and
heteroskedasticity in the standardized and the squared standardized residuals of in￿ ation and
stock returns.
Estimates of the conditional mean indicate that over the full sample, the in￿ ation per-
sistence coe¢ cient (￿11) is positive and statistically signi￿cant in all cases. Canada exhibits
the greatest degree of in￿ ation persistence, followed by Australia, UK, and Sweden. The co-
e¢ cient of the dummy variable that is associated with in￿ ation persistence (￿￿
11) is negative
and statistically signi￿cant in all cases indicating that during the targeting period in￿ ation
is less persistent (￿11 + ￿￿
11 < ￿11).
Contrary to the E¢ cient Markets Hypothesis prediction of no time-series dependence in
stock returns, the full sample estimate for the autoregressive component of stock returns (￿22)
is statistically signi￿cant for Sweden. The estimated ￿22 coe¢ cient is positive, supporting
￿ momentum￿type of strategies since positive returns are likely to be followed by further price
increases. It appears that the underlying stock market dynamics do not change over the two
sub-periods (before and after targeting) since ￿￿
22 is statistically insigni￿cant in all cases.
Considering the conditional mean cross-e⁄ects running from past in￿ ation on current stock
returns, the estimated coe¢ cient (￿21) is statistically signi￿cant, only for Australia with its
value (2.6) indicating that equity market investment in Australia more than compensates
for increases in consumer prices. However, the estimated value of ￿￿
21 suggests that during
the targeting period the in￿ ation premium is almost eliminated. Finally, only in the case
of Sweden, post-targeting, there appears to be a statistically signi￿cant conditional mean
cross-e⁄ect running from lagged stock returns to current in￿ ation (￿￿
12 = ￿0:02)7.
Moving on to the conditional variance estimates, the parameters in A reveal whether the
conditional variances of in￿ ation and stock returns are correlated with past squared deviations
from their respective means. Focusing upon the parameters a21 and a12, that depict how the
past squared errors of one variable a⁄ects the current conditional volatility of the other
variable, they are signi￿cantly di⁄erent from zero only in the case of Sweden. The magnitude
of cross-series spillovers of shocks onto volatility is substantially greater from in￿ ation to
stock returns, since: ja12j > ja21j. The parameters in G describe how the current levels of
conditional volatilities are correlated with past conditional volatilities. The estimates of the
o⁄-diagonal elements (g12 and g21) show that in all sample countries there are statistically
signi￿cant bi-directional spillovers between stock market and in￿ ation volatility. Again, the
magnitude of spillovers is substantially greater from in￿ ation to stock returns, than vice
versa, since: jg12j > jg21j. Our results di⁄er from earlier ￿ndings of Schwert (1989) for the US
market in showing that the causality link is stronger from macroeconomic to stock market
volatility.
[Table 2]
In order to evaluate the impact of in￿ ation targeting on the volatility transmission between
in￿ ation and stock returns we employ the following rule: if the absolute value of the relevant
coe¢ cient (a12;a21;g12;g21) is greater (smaller) for the full sample than for the targeting
period, it implies that there has been a decrease (increase) in the magnitude of volatility
7Filardo (2000) uses US data and reports a negative correlation between lagged stock returns and current
in￿ ation.
4spillovers during targeting. If instead, the absolute value is the same across the two periods
then ￿ no change￿is suggested. The summary of the results in Table 2 suggests that while there
have been no changes in the magnitude of cross-series spillovers from past shocks onto current
volatilities, major shifts are observed in the magnitude of spillovers from past onto current
volatilities. In particular, following the introduction of targeting there has been a decrease in
the magnitude of volatility spillovers from in￿ ation to stock returns in three out of the four
countries (Australia, Canada, and United Kingdom): jg12j > jg12 + g￿
12j. This is in line with
the traditional view that a monetary policy framework that focuses on price stability exerts
a calming e⁄ect on stock market volatility. Only in Sweden the introduction of targeting
appears to have increased the magnitude of volatility spillovers from in￿ ation to stock returns
thereby providing some support for the NEH. Considering whether the magnitude of volatility
spillovers from stock returns to in￿ ation has been a⁄ected by targeting, we can see that in
all cases there has been a decrease: jg21j > jg21 + g￿
21j. Thus, in￿ ation targeting seems
to have generated a self-reinforcing volatility calming mechanism. That is, lower in￿ ation
volatility translates to smaller spillover to stock market volatility, which in turn produces
smaller spillover to in￿ ation variance.
4 Conclusions
This paper investigates how the introduction of in￿ ation targeting a⁄ected the dynamic
interaction between in￿ ation and stock returns within a sample of four countries which have
adopted an in￿ ation targeting policy. The e⁄ect of targeting has been modelled by including
a dummy variable in the conditional mean and variance speci￿cation of in￿ ation and stock
returns within a bivariate VAR-GARCH framework. This extension and the focus on the
second moments di⁄erentiate this study from other contributions to the literature on linkages
between stock returns and in￿ ation. Our empirical results shed some further light in the
debate about the relative bene￿ts of in￿ ation targeting. Focusing on the second moments,
we identify major changes in the spillovers from past onto current volatilities following the
introduction of in￿ ation targeting. Speci￿cally, the magnitude of volatility spillovers between
in￿ ation and stock returns has been lower thereby supporting the idea that a monetary policy
regime that aims for price stabilization exerts a self-reinforcing calming e⁄ect on stock market
volatility. Hence, higher ￿nancial stability may be classi￿ed among the bene￿ts of explicit
in￿ ation targeting. However, whether monetary authorities might be able to achieve ￿nancial
stability via in￿ ation targeting is an issue which can only be addressed in the context of a
structural model. This is beyond the scope of the present article, but constitutes an interesting
topic for future research.
References
[1] Bordo, M. and and D. Wheelock, 1998. Price stability and ￿nancial stability: the his-
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the nexus, BIS Working Paper, 114.
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[4] Boyd, J.H., R. Levine, and B.D. Smith, 2001. The impact of in￿ ation on ￿nancial market
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[5] Caporale, G.M., and N. Spagnolo, 2003. Asset prices and output growth volatility: the
e⁄ects of ￿nancial crises. Economic Letters, 79, 69-74.
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[8] Huybens, E., and B.D. Smith, 1999. In￿ ation, ￿nancial markets, and long-run real ac-
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[9] Kontonikas, A, 2004. In￿ ation and in￿ ation uncertainty in the United Kingdom, evidence
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Biometrika, 65, 297-303.
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Finance, 44, 1115-1153.
6TABLE 1
Stock Market Returns and In￿ ation
Causality-in-Mean and Volatility Spillovers
Param. Australia Canada Sweden United Kingdom
￿1 0.522 (0:101) 0.141 (0:017) 0.186 (0:029) 0.236 (0:024)
￿2 1.387 (0:629) 0.518 (0:249) 0.991 (0:319) 1.117 (0:375)
￿11 0.612 (0:079) 0.658 (0:061) 0.341 (0:061) 0.423 (0:073)
￿￿
11 -0.339 (0:121) -0.646 (0:076) -0.251 (0:081) -0.386 (0:078)
￿12 0.003 (0:011) 0.003 (0:003) 0.008 (0:006) -0.005 (0:006)
￿￿
12 -0.018 (0:021) 0.001 (0:005) -0.021 (0:009) 0.006 (0:009)
￿21 2.597 (1:176) -0.216 (0:596) 0.027 (0:526) -0.261 (0:607)
￿￿
21 -2.410 (1:198) 0.911 (0:902) -1.491 (1:169) -1.032 (0:884)
￿22 -0.188 (0:191) 0.138 (0:102) 0.272 (0:079) -0.084 (0:057)
￿￿
22 0.163 (0:212) -0.029 (0:105) 0.067 (0:103) 0.032 (0:113)
c11 0.001 (0:909) 0.001 (0:866) 0.007 (0:076) 0.003 (0:817)
c12 1.108 (0:563) 1.716 (0:891) 3.789 (7:001) 2.889 (0:441)
c22 0.552 (0:176) 0.171 (0:045) 1.255 (9:954) 0.115 (0:033)
a11 -0.229 (0:112) -0.761 (0:124) 0.056 (0:150) -0.804 (0:094)
a12 -1.381 (1:392) -1.757 (1:455) 2.203 (1:003) 0.246 (2:334)
a￿
12 0.114 (1:588) 3.121 (2:361) 0.739 (1:842) 0.614 (3:322)
a21 -0.014 (0:018) -0.005 (0:009) -0.031 (0:014) -0.006 (0:005)
a￿
21 -0.017 (0:023) 0.015 (0:009) 0.031 (0:019) 0.005 (0:011)
a22 0.772 (0:274) -0.346 (0:106) 0.443 (0:096) 0.593 (0:104)
g11 0.426 (0:215) 0.529 (0:172) -0.508 (0:189) 0.021 (0:059)
g12 5.112 (1:748) -5.223 (0:375) -3.288 (1:276) 5.147 (1:543)
g￿
12 -7.975 (3:190) 1.508 (0:252) 9.229 (2:006) -5.943 (3:036)
g21 -0.036 (0:032) 0.018 (0:001) 0.101 (0:017) 0.055 (0:017)
g￿
21 0.036 (0:041) -0.014 (0:001) -0.166 (0:024) -0.101 (0:037)
g22 0.544 (0:181) -0.821 (0:156) -0.121 (0:109) 0.324 (0:114)
Log-Lik -251.82 -398.47 -575.07 -503.81
LR test
C: V:
[0:039]
(0:047)
[0:045]
(0:052)
[0:042]
(0:048)
[0:043]
(0:049)
LB￿ 2.45 1.17 2.81 3.87
LB2
￿ 4.15 3.98 4.16 3.05
LBr 1.09 3.16 2.22 4.11
LB2
r 4.23 2.34 3.12 3.21
Note: Quasi-maximum likelihood standard errors based on Bollerslev and Wooldridge (1992) are reported
in brackets. LB and LB2 are respectively the Ljung-Box (1978) test on the signi￿cance of autocorrelations of
5 lags in the standardized and squared standardized residuals. The covariance stationary condition is satis￿ed
by all the estimated models, all the eigenvalues of A ￿ A + G ￿ G being less than one in modulus. LR tests
[p-value] and corresponding bootstrapped critical values (C.V.) are respectively reported in square and round
brackets.
7Table 2
Impact of In￿ ation Targeting on Volatility Spillovers
Parameter Australia Canada Sweden United Kingdom
a12 Insigni￿cant Insigni￿cant no-change Insigni￿cant
a21 Insigni￿cant Insigni￿cant no-change Insigni￿cant
g12 Decrease Decrease Increase Decrease
g21 Insigni￿cant Decrease Decrease Decrease
Note: If j￿ijj <
￿ ￿￿ij + ￿￿
ij
￿ ￿ ) increase; if j￿ijj >
￿ ￿￿ij + ￿￿
ij
￿ ￿ ) decrease; if j￿ijj =
￿ ￿￿ij + ￿￿
ij
￿ ￿ )
no change, where ￿ = (￿; g). If the estimated coe¢ cient is statistically insigni￿cant at the 5% level, its
value is taken as zero.
8

Stock Returns and Inflation: The Impact of Inflation Targeting

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Stock Returns and Inflation: The Impact of Inflation Targeting

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