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Economic Systems 37 (2013) 17–29
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Economic Systems
journal homepage: www.elsevier.com/locate/ecosys
Measuring the interconnectedness of financial institutions
Ramaprasad Bhar a,*, Biljana Nikolova b
a School of Risk and Actuarial Studies, The University of New South Wales, Sydney 2052, Australiab National Australia Bank Ltd, 23/255 George St, Sydney, NSW 2000, Australia
A R T I C L E I N F O
Article history:
Received 1 October 2011
Received in revised form 26 April 2012
Accepted 23 May 2012
Keywords:
Interconnectedness
Convergence
Kalman filter
Financial institutions
A B S T R A C T
This paper uses sophisticated empirical methodology to measure
the interconnectedness of financial institutions in five developed
economies – France, Germany, Japan, UK and USA – for the period
January 2000 to November 2009. The study goes beyond the
conventional use of first and second moments of returns and uses
the timevarying equity price of risk methodology to measure the
level of convergence of the financial sectors in the countries of
interest. More specifically, Kalman filter convergence tests are
applied to the weekly equity price of risk data to measure the
interconnectedness between these countries’ and the US finance
sectors. Results indicate the presence of short-term timevarying
interconnectedness of the finance sectors of France, Germany and
the UK with that of the US and steady-state longer term
interconnectedness only between Germany and the US. Short-
term and long-term steady-state interconnectedness between
Japan and the US is not evident. We conclude that going forward
in an environment of increased interconnectedness of international
financial markets, a coordinated global financial regulatory policy
with discretionary allocation of resources and execution strategy at
a national level is the preferred regulatory structure to ensure
sound operations of international financial systems.
� 2012 Elsevier B.V. All rights reserved.
1. Introduction
Over the past 24 months the world has experienced a financial crisis of unprecedented nature andscale. A long period of abundant liquidity, rising asset prices and low interest rates in the context ofinternational financial integration and innovation led to the build-up of global macroeconomic
* Corresponding author.
E-mail addresses: [email protected] (R. Bhar), [email protected] (B. Nikolova).
0939-3625/$ – see front matter � 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.ecosys.2012.05.003
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–2918
imbalances as well as a global ‘‘search-for-yield’’ and general under-pricing of risk by investors.Regulators in some cases facilitated, and in other cases failed to respond to, the build-up inimbalances. The abundant liquidity induced a rapid expansion of credit in many developed andemerging countries. Mortgage finance was one of the high growth areas, both in the US and elsewhere,and contributed to a bubble in global real estate prices. Financial innovation increased systemicvulnerability in a number of ways. The growth of the mortgage market, especially in the US, wassupported by financial innovation in structured finance and credit derivatives as well as by an activesecondary market for mortgage-related securities. Moreover, both regulated and unregulatedfinancial institutions became more ‘interconnected’ via over the counter markets with bilateralclearing and settlement arrangements. At the same time, the favourable macroeconomicenvironment, increased competition, technological advances, and growing asset prices causedfinancial institutions to move down-market, to lower credit underwriting standards, to engage inriskier trading activities with maturity mismatches and to rely excessively on quantitative riskmodels.
The slowdown and subsequent decline in US housing prices since 2005 was the trigger for theunravelling of the highly leveraged and unsound lending that had been building over time. Theseweaknesses first became apparent in the area of subprime lending, although other market segments(prime mortgage loans, commercial real estate, leveraged loans, etc.) were subsequently affected aswell (World Bank, 2008). The financial turmoil, coupled with significant ongoing financial de-leveraging, commodity price shocks and necessary adjustments in housing and other markets causeda sharp slowdown in economic growth in both developed and developing countries in the world. As aresult of the crisis some of the largest and most venerable banks, investment houses, and insurancecompanies have either declared bankruptcy or have had to be rescued financially. Nearly allindustrialized countries and many emerging and developing nations announced economic stimulusand/or financial sector rescue packages. Several countries resorted to borrowing from theInternational Monetary Fund as a last resort. The crisis has exposed fundamental weaknesses infinancial systems worldwide, demonstrated how interconnected and interdependent economies aretoday, and has posed vexing policy dilemmas.
The process for coping with the crisis by countries across the globe has involved governmentintervention and changes in the financial system to reduce risk and prevent future crises. The crisis hasreaffirmed some fundamental tenets of financial sector policymaking, such as the need for a solidfinancial infrastructure, including sound accounting and auditing standards, effective collateralregistration and enforcement systems, well-functioning payments and settlement systems, and welldesigned corporate governance structures. At the same time, the crisis is also prompting areconsideration of certain elements of financial sector policymaking, including regulation andsupervision (Nanto, 2009). Policy proposals to change specific regulations as well as the structure ofregulation and supervision at both domestic and international levels have been coming forth throughthe legislative process from recommendations by international organizations such as theInternational Monetary Fund, Bank for International Settlements, and Financial Stability Board(Forum). The proposed regulatory changes will need to be coordinated among nations to avoidmigration of business and transactions to less regulated markets. In an international marketplace ofmultinational corporations, instant transfers of wealth, fast communications, and globalized tradingsystems for equities and securities, if regulations in certain countries are anomalous or significantlymore ‘‘burdensome’’ than those in other industrialized nations, business and transactions couldmigrate towards other markets. However, despite the evident need for an internationally coordinatedregulatory approach, there was a divergence in view among country leaders in relation to theimplementation of government stimulus packages in the industrialized countries during the April2009 G-20 London Summit,1 showing preference towards traditional country-specific interventionaryapproaches rather than a more structured global approach (Nanto, 2009).
1 At the April 2009 G-20 London Summit, a schism arose between the United States and the U.K., who were arguing for large
and coordinated stimulus packages, and Germany and France, who considered their automatic stabilizers (increases in
government expenditures for items such as unemployment insurance that are triggered any time the economy slows) plus
existing stimulus programmes as sufficient.
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–29 19
The methodology we adopt in this paper allows us to interpret both the short-term variation andthe steady-state level interconnectedness between a pair of countries. Also, since we focus on theinterconnectedness via the price of risk, the results hold at a fundamental level. This is a more robustinference compared to the more conventional approach using first or second moment of returndistributions. Using this advanced methodology we show that there is a relatively stronginterconnectedness of financial institutions in industrialized markets in the period January 2000to November 2009. Furthermore, based on previous studies we observe the relationship between theprice of risk and consumer confidence in the selected markets and conclude that the sustainability andeffectiveness of the global economy is dependent upon the introduction of a uniform financearchitecture guided by global policy frameworks. However, we recognize the uniqueness ofeconomies and the significance of local market knowledge and suggest discretionary application of theproposed global policy measures by sovereign governments on a national level.
2. Literature review
Financial researchers have vastly explored the topic of financial markets integration over the pastthree decades. Diverse methodologies and databases have been used to study the this area of interest;however, most have taken the common approach of modelling return series from similar assetmarkets and analysing feedback mechanisms through the first or the second moments of thedistributions. Malliaris and Urrutia (1992, 1997) show that certain global events tend to move nationalstock markets in unison. McCarthy and Najand (1995) also provide a good account of the literature instock market linkages. It is, therefore, important to reflect on the interrelated concepts of marketintegration, convergence and interconnectedness. Dobado and Marrero (2005) point out that theintegration of two distant markets does not imply physical transportation of goods and services, butjust that the two markets are interconnected within the same economic discipline. In the goodsmarket, convergence of prices may be considered a genuine manifestation of market integration.Similarly, the price of risk may be considered a genuine sign of aggregate equity market integration.
Merton (1980) provides the basis for an alternative approach to the analyses of the stock marketrisk premium. Merton’s model of inter-temporal capital asset pricing demonstrates that a risk-averseinvestor demands a risk premium to compensate for the market risk. Assuming that the variance of themarket return is a sufficient statistic for the market risk, this risk premium is proportional to themarket return. In the case of a representative investor, this proportionality constant is also equal to theinvestor’s relative risk aversion. This multiplier can also be used to measure risk by providing the unitprice of risk. There is no evidence to suggest that the unit price of risk should remain constant overtime. This, in fact, is the approach adopted by Jochum (1999). Jochum shows that the time variation inthe price of risk can be estimated using an unobserved component model that also allows the measureof risk to vary with time as an ARCH process. Chou et al. (1992) apply a similar methodology and relatethe inferred price of risk to the risk aversion parameter of the investor.
The notion of time variation of the unit price of risk is by no means limited to the frameworkoutlined above. The derivative contracts written on the market indices of many countries offer anotherpotential source of data for inferring the time variation of the market price of risk. The concept isembedded in the rich theory of derivatives pricing, a theory which requires a relation between risk-neutrality and historical probability distributions. Bhar et al. (2004) demonstrate how the model canbe set up and estimated using the state-space structures. This approach allows us to infer the timevariation of the market price of risk quite efficiently.
In addition to the studies of integration, a number of different methods have been proposed to testand measure the convergence of economic series. The main conclusions that arise from some previouswork are covered as follows. Some methods use cross-section information when testing forconvergence of a group of more than two series. Examples are the analysis of the time series of adispersion measure, ‘‘initial value’’ or ‘‘Barro’’ regressions and their derivations, random fieldsregressions and the estimation of Markov transition matrices (St. Aubyn, 1999). These methods sufferfrom a common drawback: it becomes difficult to accommodate the possibility of limitedconvergence. By limited convergence it is understood that only a subset of series (e.g. countries’GDPs) converges to one leading series. When there is more than one such grouping, there is more than
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–2920
one leading series and each of the remaining series is attracted by one and only one of them. Themethods proposed by Quah (1996) and Durlauf and Johnson (1992) are specially designed to addressthis issue. However, their effectiveness relies on strong correlation within the groupings. St. Aubyn(1995) demonstrates this using the Monte Carlo method.
Pure time series tests for convergence usually take the difference between two series and analyzeits properties. According to St. Aubyn (1999), they share a common characteristic: they make itpossible to allow for convergence clubs or limited convergence. For example, if you suppose thatcountry A belongs to the same club as country B, simply test for convergence of series A to B. The morepopular time series approach to convergence is to test this difference for stationarity using, forexample, a Dickey–Fuller statistic. This approach is not completely satisfactory in the sense thatconvergence, according to the definitions from the previous section, does not necessarily implystationarity of the differences. For example, Hall et al. (1992) give the following example: two seriesare completely independent from each other, and, from a certain point in time, become exactly equal.These series would converge, but would not pass the stationarity test. In order to allow for aconvergence process that starts occurring after the beginning of the period under consideration, Hallet al. (1992) propose the use of the Kalman filter. St. Aubyn (1999) reports evidence that this method ismore powerful than the Dickey–Fuller’s when there is a structural break in the convergence process.
This paper uses well-developed empirical methodology to measure the interconnectedness offinancial institutions in five developed economies – France, Germany, Japan, UK and USA – usingfinance sector index data from between January 2000 and November 2009, capturing the periodsbefore and during the global financial crisis. We go a step beyond the conventional use of first andsecond moments of return time series, and use the time-varying price of risk methodology followingJochum (1999) to measure the dynamics of the equity price of risk in the finance sector in each of theseeconomies, which in turn allows us to investigate the comparative behaviour of these markets duringthe selected period of observation. If equity markets are financially integrated, the price of risk shouldbe the same across markets. The price of risk may differ across markets if barriers to capital flowsacross markets or other influences impede their financial integration.
After inferring the price for risk, we extend the study of financial institutions’ interconnectednessto a deeper level by investigating the level and speed of convergence of the financial sector equity priceof risk in each country to the US financial sector equity price of risk. More specifically, Kalman filterconvergence tests are then applied to the weekly equity price of risk data to measure theinterconnectedness between these countries’ and the US finance sectors.
The rest of the paper is organized as follows: Section 3 covers the empirical model, Section 4 coversthe data, Section 5 presents the empirical results and Section 6 concludes.
3. Empirical model
3.1. Time-varying price of risk
The model used in this paper is similar to that of Chou et al. (1992). It can be expressed by thefollowing Eq. (1). If xt represents the return on the equity index at time t, s2
t represents the conditionalvariance of the return at time t, and bt denotes the price of unit risk, then:
xt ¼ bts2t þ et (1)
where et � Nð0; s2t Þ is considered the measurement error. The time variation of the variance of the
return is assumed to follow a GARCH (1,1) process given by
s2t ¼ a0 þ a1e2
t�1 þ b1s2t�1 (2)
The dynamic of the price of risk (bt) is assumed to follow a simple random walk process describedby:
bt ¼ bt�1 þ vt (3)
where vt � Nð0; s2vÞ is uncorrelated with the measurement error.
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–29 21
The only observable quantity in this set up is the return from the market. The unknown parametersof the model can by summarized by Q � ½a0; a1; b1; s2
e ; s2v �. We can estimate these parameters by
recognizing that the model is already in the linear state-space framework. Eq. (1) is the measurementequation and Eq. (3) gives the state dynamic. The first step in the estimation is to construct theprediction error decomposition form of the likelihood function by applying the Kalman filter. Oncethis likelihood function is obtained, it can be directly maximized by the numerical method. This givesus the required parameters as well as the standard error of the estimates. The Kalman filter alsogenerates the most likely estimate of the states, i.e., bt. This is the quantity which interests us in ourfurther analysis. The model applied here has an added level of complexity which sets it apart fromother linear state-space model estimations. The complexity arises due to the time-varying conditionalvariance. Chapter 6 of Kim and Nelson (1999) clearly explains the difficulty entailed in this andproposes a workable solution (we see no need to repeat the methodology here). Interested readers willfind it fruitful to explore this excellent reference. We have implemented this algorithm in GaussTM.
3.2. Convergence
3.2.1. Defining convergence
The meaning of convergence may be clarified before we proceed into testing for it. We areconsidering two economic series of interest to us. For example, these could be GDPs per head of anytwo countries. These two are said to converge to each other if their difference becomes stationary inthe long term. In more formal terms, consider two economic series Xt and Yt. These two may representGDPs per head or any other two series we are interested in. These two series converge if:
ðXt � YtÞ !P et as t ! 1 (4)
where et is a random variable obeying the following conditions:
E½et� ¼ DXY ; VarðetÞ ¼ s2 < 1: (5)
Eqs. (4) and (5) mean that the difference between the two series converges in probability to a thirdseries that is stationary, having a constant mean DXY and a finite constant variance s2.
To turn this definition into one compatible with the definitions presented elsewhere (e.g. Barro andSala-i-Martin, 1995), we could define economic convergence as:
(a) P
oint wise, if s2 =0; (b) U nconditional, if DXY =0; (c) C onditional, if DXY 6¼0.Thus, the above definitions encompass the ‘‘beta-convergence’’ concept proposed by Barro andSala-i-Martin (1995). In their study, series are supposed to converge to their steady-state level at anannual constant rate. If steady states are the same, ‘‘beta-convergence’’ is unconditional. If they aredifferent but grow at the same rate, ‘‘beta-convergence’’ is conditional. Definitions presented in (4)and (5) are more general because they do not imply a constant rate of convergence and thereforedifferences between series are not necessarily stationary from the beginning.
Bernard and Durlauf (1995) propose a related definition: series Xt and Yt converge if the long-runforecasts for both series are equal at time t:
Limk ! 1ðXtþk � Ytþk Itj Þ ¼ 0 (6)
where It represents information at time t.In some sense, the definition in (6) is more stringent: Bernard and Durlauf actually allow for the
difference between series to be a random walk. The definition in (4) and (5) prevents this possibility byimposing a restriction on the variance.
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3.2.2. Testing for convergence
3.2.2.1. Testing convergence – Dickey–Fuller. Dickey–Fuller convergence tests are stationarity tests forthe differences between the two economic series. Eq. (7) can be estimated using OLS and dt is thedifference between the two series being discussed.
dt � dt�1 ¼ b1 þ b2dt�1 þX1
j¼1
b2þ jðdt� j � dt� j�1Þ þ et: (7)
The number of lags denoted by 1 is chosen so that the residuals would not display significantautocorrelation or non-normality. The standard tests for autocorrelation and normality could beimplemented for this purpose.
The statistic of concern is the t-statistic for the b2 coefficient. Under the null hypothesis of non-stationary, this statistic is not normally distributed. The relevant critical values are available fromMacKinnon (1991).
3.2.2.2. Testing convergence – Kalman filter. This is a brief description of the method; the detaileddescription can be found in St. Aubyn (1995). In this approach the difference between the two series(time-varying price of risk of two countries), dt, is modelled as,
dt ¼ at þ et; et � Nð0; s2e Þ (8)
2 2 2
at ¼ at�1 þ ht; ht � Nð0; Vt Þ; Vt ¼ fVt�1 (9)and V20 is the assumed initial value of the variance of at.
The difference between the two series is modelled as a random walk plus noise. Although the noisevariance s2
e is constant through time, the variance of ht is potentially time-varying and given by V2t . If
jfj is less than 1, this variance tends to 0 in the long run, meaning that the two series are converging.Their difference becoming a stationary variable, the random walk component vanishes eventually andthe noise becomes the only stochastic disturbance.
The model in (8) and (9) is written in a state-space form equation where the first equation is themeasurement equation and the second is the state equation. The likelihood function can beconstructed using the Kalman filter as discussed in chapter 8 of Bhar and Hamori (2005). Themaximum likelihood estimates for the parameters are obtained by maximizing it.
The null hypothesis of no convergence implies that f=1. Therefore, the proper test for convergenceis: H0: f=1 against H1: f<1. The implementation of this test requires the distribution of f under thenull. St. Aubyn (1995) shows the critical values that are applicable for this hypothesis test viasimulation. The test is based on the quantity
tðf_Þ ¼ f_� 1ffiffiffiffiffiffiffiffi
h�1f
q (10)
where f_
is the estimated maximum likelihood value of f and h�1f is the inverse of the diagonal element
of the Hessian matrix corresponding to f.The test described above using the Kalman filter can be easily extended to accommodate
differences in steady-state values reached by the economic time series under investigation. Finally,the hypothesis that there are differences in the convergence process across the candidate series isaddressed. That is to say that even if all series are converging towards the same attracting economy,it may well be the case that they are doing so at different speeds and towards different steadystates.
Consider the following set up,
dt ¼ dSS þ btðdt�1 � dSSÞ þ et (11)
bt � b̄ ¼ a1ðbt�1 � b̄Þ þ ht; � 1 < a1 < 1: (12)
Table 1Summary statistics.
France Germany Japan UK USA
Mean 0.0005 0.0003 �0.0015 �0.0007 �0.0008
S.D. 0.0435 0.0402 0.0477 0.0437 0.0433
Maximum 0.2048 0.1549 0.2099 0.2091 0.2398
Minimum �0.1764 �0.1736 �0.2350 �0.2657 �0.2803
JB-test 406.7044 83.3328 99.7378 723.6645 1277.3830
p-Value 0.0000 0.0000 0.0000 0.0000 0.0000
JB is the Jarque–Bera test statistic for normality.
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–29 23
Here et and ht are i.i.d. normally distributed. bt is time-varying speed of convergence. Since a1 is
bounded within the unit circle, bt tends to return to its ‘‘long-run’’ value, given by b. This is a case of a‘‘return to normality’’ model. If bt is smaller than one, the difference between the two series convergesto the steady-state value given by dss.Eqs. (11) and (12) express the measurement and transition equations in a state-spacerepresentation of the return to normality model. The concentrated log-likelihood can be written,as discussed in chapter 8 of Bhar and Hamori (2005), as an implicit function of the relevant parametersand maximized using numerical methods.
4. Data
The model is applied to the finance sector stock price index weekly data from France, Germany,Japan, UK and USA for the period January 2000 to November 2009. Weekly (Wednesday) price seriesdata are used to avoid non-synchronous trading and day-of-the-week effects, as discussed inRamchand and Susmel (1998), Aggarwal et al. (1999) and Ng (2000). The data are sourced fromBloomberg. Weekly equity index returns are calculated by taking the first difference of the naturallogarithm of the weekly price index relative, measured in local currency, times 100. Summarystatistics for the weekly local, regional and world returns are presented in Table 1.
Table 1 presents the summary statistics describing the return series for each country’s financialsector equity index. The mean returns for France and Germany are positive and negative for Japan, UKand USA, which is reflective of the performance of the major finance sector equities during the periodJanuary 2000 to November 2009. The considerable size of the minima and maxima relative to themean and standard deviation of the series may indicate that the series are heavy tailed. This possibilityis confirmed by a rejection of the normality assumption for the series. The JB-test statistic is computedusing skewness and kurtosis parameters.
5. Empirical results
5.1. Time-varying equity price of risk results
Tables 2 and 3 show the empirical results of the time-varying price of risk model and the modeldiagnostics. Table 2 reports the maximum likelihood estimates of the parameters of Eqs. (1)–(3). Table3 reports the p-value of the Q and Q2 statistics. The Q and Q2 tests check for no serial correlation in thestandardized and squared standardized residuals up to lag 24 respectively. The results indicate thatthe null hypothesis of no serial correlation in the standardized and squared standardized residuals isaccepted for all countries at a significance level of 0.05 or 0.10. The empirical specification of the modelin Table 2 is thus clearly supported.
Figs. 1–5 depict the movement of price of risk for each of the selected countries. Falling markets areassociated with drops in the price of risk, while rising markets are associated with rises in the price ofrisk. Market participants will invest in what might be perceived as expensive or overvalued assets inbull markets on the basis of high profit expectations, but will demand increased compensation for theassumed risk of potential market downturn. There is an evident similarity in the perception of marketrisk in the French, German and Japanese financial services sectors in terms of scale and movement,
Table 2Maximum likelihood estimates of the parameters (equity price of risk).
France Germany Japan UK USA
a0 0.34037 0.31124 0.34105 0.14633 0.16648
(0.10560) (0.12879) (0.14791) (0.05318) (0.06111)
a1 0.22935 0.07490 0.13485 0.15560 0.16820
(0.03783) (0.01627) (0.02327) (0.02429) (0.03471)
b1 0.77030 0.90572 0.86502 0.84439 0.83180
(0.03754) (0.01434) (0.02308) (0.02428) (0.03471)
s2e 0.00010 0.00010 0.00010 0.00010 0.00010
(2.67E�06) (2.69E�06) (3.76E�06) (2.86E�05) (1.29E�05)
s2n 2.78E�10 1.37E�10 7.11E�08 1.57E�11 1.48E�12
(1.16E�08) (3.62E�12) (6.82E�07) (1.64E�09) (5.71E�10)
Numbers in parentheses below the parameters are standard errors computed using the Hessian matrix at the point of
convergence.
Table 3Model diagnostics (equity price of risk).
France Germany Japan UK USA
Q-Statistic 0.338 0.015 0.045 0.298 0.022
Q2-Statistic 0.990 0.744 0.685 0.765 0.023
The table entries represent p-values for the corresponding tests. The Q-statistic and Q2-statistic tests check for no serial
correlations in the standardized and squared standardized residuals up to lag 24, respectively.
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–2924
which is different to the perception of risk in the US and UK financial services sectors. It is interestingto note that the price of risk in the financial services sectors of UK and US appears to be at a much lowerscale than that in the other three markets. The generic trend, especially during the global financialcrisis, appears to be quite similar in all five markets. However, while the dip in the price of risk curvefor France, Germany and Japan is quite pronounced around January 2009, followed by a gradualrecovery in France and Germany and very slow and uncertain recovery in Japan, the UK and the US sawa relatively lower price of risk decline, followed by a relatively sound recovery, especially in the US. Asfurther elaborated in the following section, this might be illustrative of the effectiveness ofgovernment intervention in response to the global financial crisis, and, more importantly, consumerand business confidence in the effectiveness of government intervention policies.
Sheard (2009) argues that an important transmission channel of policy is its effect on private sectorbeliefs and expectations about the future. In banking crises, depressions or deflations, economies canbe viewed as suffering from a coordination failure, which might have strong negative effects onconsumer and business confidence and market sentiment. Governments and central banks need to
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Fig. 1. Equity price of risk – France.
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Fig. 2. Equity price of risk – Germany.
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Fig. 3. Equity price of risk – Japan.
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show a strong and timely commitment to achieving their policy goals and then take forceful actionconsistent with those goals. Along these lines, the International Monetary Fund attributes theimproved financial conditions across the globe of late to ‘cuts in policy interest rates, continuedprovision of ample liquidity, credit easing, public guarantees, and bank recapitalization which haveappreciably lowered concerns about systemic failure and have supported intermediation’ (IMF, 2009).
Fig. 6 depicts the consumer confidence index in all six countries in the period before and during thefinancial crisis. While the US shows the highest drop in consumer confidence during the outbreak ofthe crisis, it also shows the greatest trend recovery in recent months, although characterized with
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an-0
5
5-J
ul-
05
5-J
an-0
6
5-J
ul-
06
5-J
an-0
7
5-J
ul-
07
5-J
an- 0
8
5-J
ul -
08
5-J
an-0
9
5-J
ul-
09
Fig. 4. Equity price of risk – UK.
-0.00005
0.00000
0.00005
0.00010
0.00015
0.00020
0.00025
0.00030
5-J
an-0
0
5-J
ul-
00
5-J
an-0
1
5-J
ul-
01
5-J
an-0
2
5-J
ul-
02
5-J
an-0
3
5-J
ul-
03
5-J
an-0
4
5-J
ul-
04
5-J
an-0
5
5-J
ul-
05
5-J
an-0
6
5-J
ul -
06
5-J
an- 0
7
5-J
ul-
07
5-J
an-0
8
5- J
ul-
08
5-J
an-0
9
5- J
ul-
09
Fig. 5. Equity price of risk – USA.
Source: OE CD, Main Economic Indicator Database
-60
-50
-40
-30
-20
-10
0
10
20
30
40
Dec
-06
Feb
-07
Ap
r -0
7
Jun
-07
Au
g-0
7
Oct
-07
Dec
-07
Feb
- 08
Ap
r-0
8
Jun
-08
Au
g-0
8
Oct
- 08
Dec
-08
Feb
- 09
Ap
r -0
9
Jun
-09
Au
g-0
9
Oct
-09
France Germany UK Japan US
Fig. 6. Consumer confidence (December 2006–November 2009).
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–2926
relatively high volatility. In line with the commentary above, it appears that US policymakers aresuccessful, most of the time, in communicating a timely and committed approach to responding to thefinancial crisis. Their approach has been focused on simultaneous mobilization of the fiscal, monetaryand banking systems and housing policy to end the financial crisis, restore liquidity to financialmarkets, end the recession and prevent deflation. Most importantly, US policymakers, notably theTreasury, the Federal Reserve and the administration, have said that they will do ‘‘whatever it takes’’ toovercome the current problems, which has sent a strong message to the national and internationalpublic, backed up with consistent policy action.
France, Germany and the UK also responded to the financial crisis with the introduction ofgovernment support packages during 2008 and government guarantees of bank deposits. It is evidentfrom Fig. 6 that the interventionary measures had a positive effect on consumer confidence, althoughnot to the same extent as in the US. This could be attributed to the fact that the US package was largerand more extensive in scope than the others. The US government provided assistance ofapproximately US$700bil during the calendar year 2008, while the governments of France, Germanyand the UK injected approximately US$41bil, US$182bil and US$50bil, respectively (Bank of England,2008).
Japan, on the other hand, shows the largest drop in consumer confidence, followed by a very slowrecovery, significantly below those of all other four countries. Although Japan’s economy is far fromthe US housing and credit bubbles that began the crisis, the impact of the global recession on Japan hasbeen severe. Sheard (2009) argues that this attests to the fact that the Japanese economy never fullyrecovered from the financial crisis and deflation that followed the bursting of the 1980s asset pricebubble. Although the policy goal of overcoming deflation was trumpeted for years in Japan, it was
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–29 27
never convincingly achieved. The major criticism of the Japanese interventionary plan is that theintervention was not timely and not substantial, and that there was a lack of central fiscal policy,sustained fiscal expansion and forcefulness, as well as a lack of determination and coordination in theimplementation of the new policy.
5.2. Convergence results
Having determined the equity price of risk in the finance sector in the countries of interest, weexpand the study to measure the interconnectedness of financial institutions using the Kalman filterbased approach to economic convergence. The models, as described in Section 3.2.2.2, are estimatedusing the weekly finance sector equity price of risk for each country for the period January 2000 toNovember 2009. We compute the difference variable, bt, as the difference for each country’s equityprice of risk. The t-statistic based on Eq. (7) for each country indicates the rejection of the noconvergence hypothesis at the 1% level. Namely, the finance sectors in France, Germany, Japan and UKare showing interconnectedness with the US finance sector over the sample period. In order tounderstand the speed of convergence as well as the steady-state values, we also estimated the modelgiven by Eqs. (11) and (12). The estimated parameters with t-statistics in parentheses are given inTable 4. In addition, treating the US equity price of risk as the attracting time series, we test fordifferences in the convergence process across countries. We recognize that even if all countries areconverging towards the same attracting economy, it may well be the case that they are doing it atdifferent speeds and towards different steady states.
The steady-state coefficient dSS is derived as the difference between the US equity price of risk andthat of the other countries. The dSS coefficient is negative and significant for all countries, implying thatthe US price of risk remains less than that of the other countries. This is consistent with the equityprice of risk findings in Section 5.1. a1 is the short-run speed of convergence of the finance sector ofeach country with that of the US. The short-run speed of convergence is less than one for all countriesand is not statistically significant only for Japan. It is also evident from the results in Table 4 that thevariance s2
h is significant for all countries, which implies that the speed of convergence orinterconnectedness for all countries is time-varying. The quantity of the long-run value of the time-varying speed of convergence b is significant for all four countries, however, it is slightly higher than 1for all except Germany. Values for b of less than one imply that the difference between the equity priceof risk of each country and the US will converge to the steady-state value given by dSS. According to ourresults this is only the case for Germany. However, it should be noted that while values for othercountries are greater than one, they are only marginally greater than one. This might suggest that theuse of a longer time series period might change the results. We were unable to test theinterconnectedness of the finance sectors in the countries of interest for a longer period of time due tolimited availability of information as some of the indices used in this study were created in the late1990s.
Table 4Test of interconnectedness in equity price of risk (with USA).
France Germany Japan UK
Relevant t-Stat. �112.07* �12.81* �20.84* �19.65*
dSS �0.0037 �0.0030 �0.0041 �0.0002
(0.0001) (0.0008) (0.0014) (1.44E�05)
b 1.0006 0.9924 1.0029 1.0010
(0.0056) (0.0055) (0.0026) (0.0069)
a1 �0.1945 0.8046 �0.1465 �0.1773
(0.1026) (0.1208) (0.0988) (0.0901)
s2h 0.0073 0.0001 0.0018 0.0084
(0.0015) (9.42E�05) (0.0010) (0.0035)
* Relevant t-statistic tð f_Þ ¼ ð f
_� 1Þ=ffiffiffiffiffiffiffiffih�1f
qis described in the text. Asterisk indicates the hypothesis of no interconnectedness is rejected
at the 1% level of significance. Critical values are obtained from St. Aubyn (1999).
Other parameters relate to steady-state property of interconnectedness as described in the text. The numbers in the parentheses
below the parameters are the appropriate standard errors.
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–2928
In summary, there is short-run time-varying finance sector interconnectedness for France,Germany and the UK with the US, while steady-state long-term interconnectedness is evident onlybetween Germany and the US. The presence of interconnectedness between France, Germany and theUK with the US may be reflective of the common sector response to government interventionprogrammes in the wake of the financial crisis. Similar to the US, the governments in all threecountries intervened quickly with financial institutions bailouts, guaranteed bank deposits, stimuluspackages encouraging consumer spending and investments in infrastructure projects. The longer termsteady-state interconnectedness of the German and US finance sectors might indicate greateroperational and structural alignment of financial institutions operating in these countries andsimilarity in investor behaviour and broader market conditions.
Japan’s finance sector, on the other hand, is very different to that of the US and the government’sapproach to crisis management has been very different both historically and of recent times. After thebursting of the 1980s asset price bubble and struggling with financial crisis and deflation for anotherdecade, Japan’s economy began to recover in 2002 driven by exports and capital investment linked toexports. The recovery was fuelled by the US consumption boom and China’s post WTO-entry capitalinvestment boom, not Japanese domestic demand. The Japanese economy suffered significantlyduring the latest financial crisis due to reduced Chinese and US import demand and foreign net sellingof Japanese equities triggered by the crisis. In light of Japan’s history of challenges associated withdealing with past financial crisis and deflation, the consumer and business response to governmentintervention and regulatory policy as a result of the late financial crisis is expected to be less positivethan that in the US.
Based on the findings and analysis in the paper which indicate the presence of short-terminterconnectedness of financial sectors, which is indicative of market integration and reflective ofcommon government and market behaviour in times of financial crisis, and weak evidence of long-term steady-state interconnectedness which indicates generic uniqueness of individual financialsystems which is expected to be more pronounced in times of steady economic conditions, weconclude that coordinated global finance regulatory policy with discretionary allocation ofinterventionary funds and measures at a national level is the preferred regulatory structure goingforward.
6. Conclusion
In this paper we use appropriate and well-developed empirical methodology to measure theinterconnectedness of financial institutions in five developed economies – France, Germany, Japan, UKand USA – using finance sector index data from January 2000 to November 2009, capturing the periodsbefore and during the global financial crisis. We use the time-varying price of risk methodology tomeasure the dynamics of the equity price of risk in the finance sector in each of these economies,which allows us to investigate the comparative behaviour of these markets during the selected periodof observation. After inferring the price for risk, we extend the study of financial institutions’interconnectedness to a deeper level by investigating the level and speed of convergence of thefinancial sector equity price of risk in each country with the US financial sector equity price of risk.
The results for the measure of time-varying equity price of risk in the finance sectors of thecountries of interest present evident similarities in the perception of market risk in the French,German and Japanese financial services sectors in terms of scale and movement, and similarities in theperception of risk in the US and UK financial services sectors. The generic trend, especially duringthe global financial crisis, appears to be quite similar in all five markets. However, while the dip in theprice of risk curve for France, Germany and Japan is quite pronounced around January 2009, followedby gradual recovery in France and Germany and very slow and uncertain recovery in Japan, the UK andthe US have seen a relatively lower price of risk decline, followed by a relatively sound recovery,especially in the US. It is noted that the size and trend in the time-varying equity price of risk might beillustrative of the effectiveness of government intervention in response to the global financial crisis,and, more importantly, consumer and business confidence in the effectiveness of governmentintervention policies.
R. Bhar, B. Nikolova / Economic Systems 37 (2013) 17–29 29
Furthermore, the measure of interconnectedness using the Kalman filter tests indicate that there isshort-run time-varying finance sector interconnectedness between France, Germany and the UK withthe US, and steady-state long-term interconnectedness is evident only between Germany and the US.The presence of interconnectedness between France, Germany and the UK with the US may bereflective of the common sector response to government intervention programmes in the wake of thefinancial crisis. Japan’s finance sector on the other hand is very different to that of the US and thegovernment’s approach to crisis management has been different both historically and in recent times,hence the lack of interconnectedness between the finance sectors of the two countries.
The results of the analysis conducted in this paper indicate the presence of short-terminterconnectedness of the financial sectors in France, Germany and the UK with that of the US, which isindicative of market integration and reflective of common government and market behaviour in timesof financial crisis. There is weak evidence of long-term steady-state interconnectedness, exceptbetween Germany and the US, which indicates a generic uniqueness of individual financial systemsusually more pronounced in times of steady economic conditions. Based on these findings, weconclude that a coordinated global financial regulatory policy with discretionary allocation ofinterventionary funds and execution strategy at a national level is the preferred financial markets’regulatory structure going forward.
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