Financial Structure and the Impact of
Monetary Policy on Asset Prices
Swiss National Bank
Institute for Monetary and Financial Stability
Goethe University, Frankfurt
9 January 2009
We study the responses of residential property and equity prices, inflation
and economic activity to monetary policy shocks in 17 countries in the period
1986-2007, using single-country VARs and panel VARs in which we
distinguish between groups of countries depending on their financial systems.
The effect of monetary policy on property prices is only about three times as
large as its impact on GDP. Using monetary policy to guard against financial
instability by mitigating asset-price movements thus has sizable effects on
economic activity. While the financial structure influences the impact of policy
on asset prices, its importance appears limited.
Keywords: Asset prices, Monetary policy, Panel VAR.
JEL Number: C23, E52
? This is a much revised version of an earlier paper entitled “Ensuring Financial Stability: Financial
Structure and the Impact of Monetary Policy on Asset Prices.” The views expressed are solely our
own and are not necessarily shared by the SNB. We are grateful to seminar participants at the
Banque de France, Deutsche Bundesbank, Swiss National Bank, Singapore Management
University, University of Basel, the ROME Workshop and the Hong Kong Institute for Monetary
Research, an anonymous referee of the SNB Working Paper Series, Helge Berger, Urs Birchler and
Petra Gerlach for helpful comments. Contact information: Katrin Assenmacher-Wesche
(corresponding author): SNB, Börsenstrasse 15, Postfach 2800, CH-8022 Zürich, Switzerland, Tel
+41 44 631 3824, email: Katrin.Assenmacher-Wesche@snb.ch; Stefan Gerlach: IMFS, Goethe
University, Grüneburgplatz 1 (Box H 12), 60629 Frankfurt/Main, Germany, email:
There is much agreement that asset prices, in particular residential property prices, provide a
crucial link through which adverse macroeconomic developments can cause financial
instability.1 Episodes of asset price booms are seen by many as raising the risk of a future
sharp “correction” of prices, which could have immediate repercussions on the stability of
financial institutions. Indeed, many observers have argued that property-price collapses
have historically played an important role in episodes of financial instability at the level of
individual financial institutions and the macro economy (e.g. Ahearne et al. 2005, Goodhart
and Hofmann 2007).
Not surprisingly, this view has led to calls for central banks to react to movements in asset
prices “over and beyond” what such changes imply for the path of aggregate demand and
inflation (Borio and Lowe 2002, Cecchetti et al. 2000). Proponents of such a policy emphasise
that episodes of financial instability risk depressing inflation and economic activity below
their desired levels. Consequently, they argue, central banks that seek to stabilise the
economy over a sufficiently long time horizon may need to react to current asset-price
movements (Bean 2004, Ahearne et al. 2005). Importantly, this idea does not mean that asset
prices should be targeted, only that central banks should be willing to tighten policy at the
margin in order to slow down increases in asset prices that are viewed as being excessively
rapid in order to reduce the likelihood of a future crash that could trigger financial instability
and adverse macroeconomic outcomes.
While seemingly attractive, this proposed policy presumes that central banks are able to
identify in real time whether asset prices are moving too fast or are out of line with
fundamentals. Of course, it is by no means clear that they are better able to judge the
appropriate level of asset prices and the risk of future sharp price declines than agents
transacting in these markets. Furthermore, the policy has implications for the speed by, and
the extent to which, monetary policy impacts on the economy (Bean 2004, Bernanke 2002,
Kohn 2006). First, changes in policy-controlled interest rates must have stable and
predictable effects on asset prices. Second, the effects of monetary policy on different asset
1 The chapters in Hunter et al. (2003) provide an overview of the interlinkages between monetary
policy, asset prices and financial stability.
prices, such as residential-property and equity prices, must be about as rapid, since
stabilising one may otherwise lead to greater volatility of the other. Needless to say, if these
criteria are not satisfied simultaneously, any attempts by central banks to offset asset-price
movements may simply raise macroeconomic volatility, potentially increasing the risk of
financial instability developing. Third, the size of interest-rate movements required to
mitigate asset-price swings must not be so large as to cause economic activity and, in
particular, inflation to deviate substantially from their desired levels since, if this were to be
the case, the resulting macroeconomic cycles could lead the public to question the central
bank’s commitment to price stability. Fourth, the effects of monetary policy on asset prices
must be felt sufficiently rapidly so that a tightening of policy impacts on asset prices before
any bubble would burst on its own (since policy should otherwise presumably be relaxed to
offset the macro economic effects of the collapse of the bubble).2
Unfortunately, however, it is unclear whether monetary policy has predictable effects on
asset prices and, if so, whether these effects occur at about the same time horizons for
different asset prices, whether they are large relative to the effects of monetary policy on
inflation and economic activity, and whether they materialise faster than the effects on
inflation and economic activity. While the “over and beyond” approach to monetary policy
and asset prices is seemingly attractive, further work on the transmission mechanism of
monetary policy and the role of asset prices is thus warranted.
This paper is part of that work and seeks to shed light on the impact of monetary policy on
residential-property and equity prices, inflation and output growth. To do so, we establish
empirical regularities, as captured by the impulse-response functions of vector-
autoregressive models (VARs), that theoretical models of the relationship between monetary
policy and asset prices must account for. Instead of testing any specific hypothesis, we follow
the research strategy of Goodhart and Hofmann (2008), which estimates VARs that uses
minimal identifying assumption to study closely related issues. One attractive feature of both
papers is that they look at a broad cross section of countries, which experienced asset price
2 Bean (2004) and Kohn (2006) discuss the implications of transmission lags for the use of monetary
policy in the face of asset-price bubbles.
movements of varying severity and at varying points in time.3 This avoids the bias that
comes from looking mainly at countries that have undergone particularly pronounced asset-
We also investigate the role of financial structure for the link between monetary policy and
asset prices. A number of authors have argued that the strength of the transmission
mechanism depends on institutional characteristics of the financial system.4 In particular, it
has been argued that the reaction of output and inflation to monetary policy shocks is likely
to be stronger in financial systems that are more “flexible” and market based. The existing
literature has proposed a number of statistical measures – such as relative importance of
fixed versus floating rate lending, or average loan to valuation ratios – to capture these
characteristics. In studying the importance of the financial structure, we simply use various
statistics reported in the literature.
To perform the analysis we study a panel of 17 OECD countries using quarterly data for the
period 1986-2007.5 The analysis proceeds in three steps. Following Iacoviello (2002) and
Giuliodori (2005), we first study the impact of monetary policy on the economy by fitting
VARs for individual countries. 6 Not surprisingly, the resulting estimates are imprecise,
leaving considerable uncertainty about the quantitative effect of changes in interest rates on
asset prices relative to their impact on economic activity and inflation, as would seem to be
an important precondition for monetary policy to be used to mitigate asset-price movements.
Moreover, it is difficult to know whether these differences are significant and whether they
depend on the financial structure.
3 See also Goodhart and Hofmann (2007).
4 The importance of financial structure is emphasized by so many authors that it is impossible to
provide a full overview here. See, among others, Maclennan et al. (1998), Giuliodori (2005),
Tsatsaronis and Zhu (2004), CGFS (2006) and Calza et al. (2007).
5 Goodhart and Hofmann (2008) also study 17 countries but use a somewhat longer sample, which
starts in 1973, and also investigate the importance of money and credit for asset prices. Moreover,
they seek to distinguish between boom and non-boom periods. However, they estimate their panel
VAR using the standard fixed-effect panel estimator, which is likely to yield biased results for
reasons we discuss below.
6 Sutton (2002) and Tsatsaronis and Zhu (2004) also estimate VARs incorporating residential
property prices for a range of countries. The focus of their studies, however, is on which factors
explain movements in residential property prices and not on whether monetary policy can be used
to stabilize asset price movements.
To raise the precision of the estimates, we thus follow Goodhart and Hofmann (2008) and
estimate a panel VAR (PVAR) incorporating real residential-property and real equity prices.
Our results show that while monetary policy does have important effects on asset prices,
those effects are not particularly large relative to those it has on inflation and output. This
suggests that attempts to stabilise asset prices by using interest rate policy are likely to
induce pronounced macroeconomic fluctuations.
However, while the panel estimates confirm that monetary policy has predictable effects on
residential-property prices, by construction these estimates disregard all country-specific
information. In order to study the importance of institutional factors, we go on to split the
sample of countries into two groups depending on their financial structure. We then estimate
a panel VAR for each group and explore whether the impact of monetary policy on asset
prices, inflation and output differs between the two groups. We use several measures
proposed in the literature to capture differences in financial structure, including the
importance of floating rate lending; whether mortgage equity withdrawal is possible; the
loan-to-value ratio for new mortgages; the mortgage-debt-to-GDP ratio in the economy; the
method used to value property; whether mortgages are securitised; and the share of owner
occupied dwellings. Since the notion of a financial system is a multi-faceted concept and
these measures each only capture one aspect, it is possible that they lead to an underestimate
of the importance of institutional factors. We therefore end the study by using the mortgage
market index recently proposed by the IMF (2008) to capture the joint impact of financial
market characteristics on the monetary transmission mechanism. To preview briefly the
results, we find that the financial structure does condition the responses of asset prices to
monetary policy but also that the differences between country groups are less important than
perhaps commonly thought.7
The paper is organised as follows. The next section contains a discussion of the data and
Section 3 presents the results for the VARs estimated for individual countries. In Section 4
we first briefly discuss panel VARs before discussing the estimates. Section 5 focuses on the
importance of financial structure and provides panel-VAR estimates when the countries are
divided into two groups on the basis of financial structure. Finally, Section 6 concludes.
7 See Maclennan et al. (1998) for a dissenting opinion.
The econometric analysis below is conducted on quarterly data on equity and residential
property prices, consumer price indices (CPIs), real gross domestic product (GDP) and
interest rates.8 Much of the interest in the behaviour and determination of asset prices stems
from their role in episodes of financial instability. Since there is a natural tendency to focus
on data from countries that have experienced pronounced asset-price swings, there is a risk
of sample selection bias, which can be mitigated by using data from a broad cross-section of
countries. We therefore study 17 countries for which we could obtain both equity and
residential property price data: Australia, Belgium, Canada, Denmark, Finland, France,
Germany, Ireland, Italy, Japan, the Netherlands, Norway, Spain, Sweden, Switzerland, the
UK and the US.
The sample starts in 1986 in order to avoid the more turbulent, higher inflation period that
ended in the first half of the 1980. Moreover, and as noted by Ahearne et al. (2005) and
Girouard and Blöndal (2001), many countries deregulated their mortgage markets during the
early to mid-1980s, suggesting that estimates relying on older data are unlikely to be
representative for modern economies. The data set ends in 2007, which covers the first two
quarters of the global financial crisis triggered by the developments in the US subprime
Residential property prices are from the data base of the Bank for International Settlements
(BIS). Quarterly data over the whole sample period are available for Australia, Canada,
Switzerland, Denmark, Finland, France, the Netherlands, Sweden, the UK and the US. 10 For
Belgium we link an older series for small and medium-sized houses to the residential
property price series for all dwellings from 1988 on. For Spain we link the residential
property prices of existing dwellings with those of owner-occupied homes in 2005. For
Ireland and Norway we interpolate annual data with the Chow-Lin (1971) procedure, using
8 All results are obtained with the software RATS 7.0.
9 Goodhart and Hofmann (2008) in their panel VAR analysis also study, as a part of their robustness
analysis, a sub-sample spanning 1986 to 2006 and find that this later period indeed differs from the
earlier part of their sample. However, their data series are somewhat different from those we use
10 For Australia, missing values for the first two quarters of 1986 were generated using the growth of
residential construction cost.
a rent index and an index of residential construction cost as reference series, and link the
resulting series to the BIS quarterly data that start in 1988 and 1991, respectively.11 The same
interpolation procedure is applied to annual property price data for Germany and Italy.12 For
Japan the semi-annual series on residential land prices is interpolated.13
Figure 1 shows the resulting residential property price series. 14 Interestingly, many
economies experienced a sharp rise in residential property prices in the second half of the
1980s, in many cases associated with liberalisation and deregulation of the housing finance
sector. Residential property prices were subsequently weak or fell in the 1990s, following the
US recession in 1990-1991 and the episode of high interest rates in many European countries
after the turmoil in the European exchange rate mechanism (ERM) in 1992-93, which was
triggered by the adoption of tight monetary policy in Germany to offset the aggregate
demand effects of German Reunification. In the early 2000s, several countries – in particular
Belgium, Denmark, Spain, the UK and the US – again experienced large increases in
residential property prices. The figure also indicates that Japan and Germany do not follow
this general pattern. After the collapse of the “bubble economy” in Japan around 1990,
residential property prices fell continuously until the end of the sample. In Germany,
residential property prices started falling in 1994 and declined until 2006, vividly indicating
the depth of the “German crisis.”
Before proceeding, it should be emphasised that data on residential property prices are not
necessarily comparable across countries. The main differences concern the type of housing
that is included (single family houses, flats or all types), whether existing dwellings or new
dwellings are considered, whether prices are per dwelling or per square meter, and the
region (urban, non-urban or both) where the data is collected. While price developments
vary between types of housing reflecting supply and demand conditions in different market
segments, the most noticeable differences arise with respect to the area where the data come
11 Annual data for Norway are from Eitrheim and Erlandsen (2004).
12 Annual property price data for Italy are taken from Cannari et al. (2006).
13 In Japan, a market for old homes practically does not exist as houses are normally torn down after
a few decades. As a consequence, land prices determine the value of housing, see the Economist
14 We note that despite the difference in data sources, the patterns are comparable to those reported
in Tsatsaronis and Zhu (2004) and Ahearne et al. (2005).
from. Property-price booms generally occur in metropolitan areas, and are often less
pronounced if data for the whole country are considered. The impact of this, however, is
difficult to assess since only few countries have series covering these different categories. As
an example, Figure 2 shows the annual increase in nominal UK residential property prices
for the whole country and the greater London area. While the prices in the latter area seem
more volatile, the two series evolve in much the same way over time (their correlation is
0.82). The right-hand panel shows the annual increase in prices for single-family houses and
flats in Switzerland. Again, the year-to-year changes differ somewhat but generally convey
the same information (the correlation is 0.86). For our study we use whenever possible the
broadest residential property price index available in order not to capture regional booms.
Nevertheless, great care needs to be exercised when comparing property-price developments
Turning to the sources of the other data, the CPI (all items) and share price indices (all
shares) are from the OECD Main Economic Indicators (MEI) data base. Real GDP data were
taken from the BIS data base and supplemented with data from the International Financial
Statistics (IFS) data base of the IMF. For Ireland annual GDP data before 1997 were
interpolated with the Chow-Lin (1971) procedure using industrial production as the
reference series. We use a three-month interbank rate for Denmark, Switzerland, Spain,
Finland, France, Germany, Ireland, Italy, the Netherlands, Norway and the UK, a three-
month Treasury bill rate for Belgium, Sweden and the US, and a three-month commercial
paper rate for Australia, Canada and Japan.15 All interest rates are from the OECD's MEI. For
Finland and Denmark missing data for 1986 were replaced with data from the IFS (call
money rate). For the euro-area countries we use the three-month EURIBOR rate after 1998.
Except for interest rates and equity prices all data are seasonally adjusted.
3. VARs for individual economies
We start by estimating VAR models for individual countries, following the approach taken
by Giuliodori (2005), Iacoviello (2002) and Neri (2004). We include five variables: the CPI (p),
15 To eliminate a large spike during the ERM crisis we regressed the three-month interest rate for
Ireland on a dummy, which is unity in 1992Q4 and zero elsewhere, and used the fitted value in the
real GDP (y), the three-month interest rate (i), residential property prices (pp) and equity
prices (eq). Except for the interest rate, all variables are in logarithms. Before we turn to the
econometric analysis it is useful to investigate the time-series characteristics of the data.
Table 1 reports results for the individual countries. Interest rates and equity prices are all
nonstationary in levels but stationary in first differences. For GDP stationarity in levels is
rejected only for Japan while nonstationarity of the first differences is rejected only for
Finland and the UK. The results for the CPI and residential property prices are less clear cut.
While the levels generally appear nonstationary, in more than half of the cases we do not
reject nonstationarity of the first differences. Since we take a panel approach below, we
therefore also perform panel unit root tests that provide a summary assessment of whether a
variable can be regarded as stationary or not. We use the panel unit root test proposed by Im,
Pesaran and Shin (2003) that allows for heterogeneity in the dynamics of the time series.
Based on the results from the panel unit root tests, we consider all variables as integrated of
order one, I(1).16
Next we test for cointegration between the variables.17 When using a common lag length of
four (which is sufficient to eliminate any seasonal pattern in the residuals for quarterly data)
for all countries, the existence of at least one cointegrating vector was not be rejected. We
therefore specify the VAR models in the levels of the variables. Nevertheless, we neither
impose the number of cointegrating relations on the systems nor do we attempt to impose
overidentifying restrictions on the cointegrating vector.
For an individual country n, n = 1, … , N, the reduced form of the VAR can be written as
= ? + A (L Y
+ ? , where Y = ( p , y ,i , pp ,eq ) ,
n is a constant, An(L) is a
matrix polynomial in the lag operator and ? is a vector of normally, identically distributed
disturbances. For each country the number of lags included in the VAR is chosen by the
Akaike information criterion, considering a maximum lag length of four.
16 Interestingly, the panel unit root test indicates that the CPI is stationary around a trend. See
Gerlach and Knüppel (2009) for a discussion.
17 Iacoviello (2002) argues that a long-run relation between GDP and real residential property prices
To study the monetary transmission mechanism, we investigate the responses of the
different variables to monetary policy shocks.18 We use a Choleski decomposition to identify
the shocks, with the variables ordered as above, which is standard in the monetary
transmission literature (see Christiano et al. 1999). This triangular identification structure
allows output and the price level to react only with a lag to monetary policy shocks, whereas
property and equity prices may respond immediately. We thus assume that central banks
react to current output growth and inflation when setting interest rates, but not to current
property and equity prices.19
While this last assumption may seem controversial in that few observers would doubt that
central banks react to changes in asset prices since these influence aggregate demand and
inflation pressures, barring exceptional circumstances one would not expect any reactions to
be instantaneous but rather to occur if asset prices rise or fall for some time. By contrast, asset
prices react immediately to changes in monetary policy. Thus, it seems sensible to attribute
the contemporaneous correlation between interest rates and asset prices to reactions by the
latter to the former rather than conversely. We have explored whether the results are
sensitive to this assumption. Not surprisingly, for equity prices the ordering does matter but
for residential property prices it does not. However, the alternative assumption that the
contemporaneous correlation between innovations in interest rates and equity prices is due
solely to reactions by monetary policy is not only implausible for the reasons mentioned, but
also leads to counterintuitive results. For instance, equity prices start to increase after a
contractionary monetary policy shock.20 It therefore seems appropriate to order the interest
rate before the asset prices in the system.
18 Of course, monetary policy is best characterised by the central bank’s systematic reactions to
developments in the variables in the VAR, which are captured by the estimated coefficients.
However, we are interested in the question of how asset prices react to a change in the interest rate,
keeping the other variables constant. To address this question, we need to identify monetary policy
shocks (see Christiano et al. 1999 or Walsh 2003, Chapter 1, for a discussion of these issues).
19 To identify the monetary policy shock it is sufficient to determine the position in the triangular
ordering of the monetary policy instrument; the ordering of the variables in the groups before and
after the interest rate does not matter.
20 A rise in equity prices after a contractionary monetary policy shock is inconsistent with results
obtained with structural identification assumptions relying on the long-run effects of monetary
policy, see Lastrapes (1998).