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We propose a New Keynesian Dynamic Stochastic General Equilibrium (DSGE) model where a risk aversion shock enters a separable utility function. We analyze five periods from 1971 through 2011, each lasting for twenty years, to follow over time the dynamics of several parameters such as the risk aversion parameter; the Taylor rule coefficients; and the role of the risk aversion shock in output, inflation, interest rate, and real money balances in the Eurozone. Our analysis suggests that risk aversion was a more important component of output and real money balance dynamics between 2006 and 2011 than it was between 1971 and 2006, at least in the short run.
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Risk aversion in the Eurozone
Jonathan Benchimol
January, 2014
Abstract
We propose a New Keynesian Dynamic Stochastic General Equi-
librium (DSGE) model where a risk aversion shock enters a separable
utility function. We analyze …ve periods from 1971 through 2011, each
lasting for twenty years, to follow over time the dynamics of several
parameters such as the risk aversion parameter; the Taylor rule coef-
cients; and the role of the risk aversion shock in output, in‡ation,
interest rate, and real money balances in the Eurozone. Our analysis
suggests that risk aversion was a more important component of output
and real money balance dynamics between 2006 and 2011 than it was
between 1971 and 2006, at least in the short run.
Keywords: Risk aversion, Output, Money, Eurozone, New Keyne-
sian DSGE models, Bayesian estimation.
JEL Classi…cation Number: E23, E31, E51.
Please cite this paper as:
Benchimol, J., 2014. Risk aversion in the Eurozone. Research in
Economics 68 (1), 39–56.
Bank of Israel, Research Department, POB 780, 91007 Jerusalem, Israel. Phone:
+972-2-6552641. Fax: +972-2-6669407. Email: jonathan.benchimol@boi.org.il. This
paper does not necessarily re‡ect the views of the Bank of Israel. I would like to thank
André Fourçans, Christian Bordes, Laurent Clerc, Marc-Alexandre Sénégas, and the two
anonymous referees for their useful comments.
1
1 Introduction
The New Keynesian model, as developed by Galí (2008) and Walsh (2017),
brings together three equations to characterize the dynamic behavior of three
key macroeconomic variables: output, in‡ation, and the nominal interest
rate. The output equation corresponds to the log-linearization of an opti-
mizing household’s Euler equation, linking consumption and output growth
to in‡ation-adjusted return on nominal bonds— that is, to the real interest
rate. The in‡ation equation describes the optimizing behavior of monopolis-
tically competitive …rms that either set prices in a randomly staggered fashion
(Calvo,1983), or face explicit costs of nominal price adjustment (Rotemberg,
1982). The nominal interest rate equation, a monetary policy rule of the kind
proposed by Taylor (1993), dictates that the central bank should adjust the
short-term nominal interest rate in response to a trade-o¤ between changes
in in‡ation and output, and changes in the past interest rate.
Following optimization of household preferences, relative risk aversion
explicitly enters the …rst two equations. It has often been calibrated, esti-
mated, and analyzed as a simple and constant parameter in the literature
(Christiano et al.,2014). Although they deal with issues largely related to
risk aversion by considering log-utilities, some studies are not able to analyze
constant relative risk aversion (Iacoviello,2005). Yet, its role in the economic
dynamics of the Eurozone has not been analyzed further, at least not includ-
ing a relative risk aversion shock in a microfounded new Keynesian Dynamic
Stochastic General Equilibrium (DSGE) model.
Alpanda (2013) highlights the important role played by risk aversion
shocks in the US between 2006 and 2011. However, few studies quantify this
link, or even consider risk aversion as a shock in a New Keynesian DSGE
framework applied to the Eurozone. No studies use Bayesian techniques— as
Fernández-Villaverde (2010) does— to analyze the role of the risk aversion
shock in output, in‡ation, interest rate, and money dynamics in the Euro-
zone.
Constant relative risk aversion may change across di¤erent time periods.
Evidence of time-varying risk aversion can be found in simple applied analy-
sis carried out by Donadelli and Prosperi (2012). For instance, risk aversion
varies in response to news about in‡ation (Brandt and Wang,2003). How-
ever, what is the contribution of a risk aversion shock to the dynamics of
output, in‡ation, and money, over time? Building a simple New Keynesian
DSGE model including a time-varying relative risk aversion variable should
be able to answer this question.
As relative risk aversion measures the willingness to substitute consump-
tion over di¤erent periods, lower the level of risk aversion, more willing the
2
households are to substitute consumption over time. Results on the relation-
ship between relative risk aversion and equity risk premium can be found in
Bansal and Yaron (2004). Additionally, Wachter (2006) and Bekaert et al.
(2010) show that an increase in risk aversion involves an increase in equity
and bond premiums, and may either increase or decrease the real interest
rate through a consumption smoothing e¤ect or a precautionary savings
ect, respectively. Bommier et al. (2012) further show that risk aversion
enhances precautionary savings. These studies con…rm the potential link
between money holdings, output, and risk aversion.
However, most new Keynesian DSGE models do not include money in
agents’utility (MIU). Ireland (2004), Andrés et al. (2006), and Barthélemy
et al. (2011) do not analyze the link between relative risk aversion of house-
holds and the dynamics of key macroeconomic variables.
However, Benchimol and Fourçans (2012) establish a signi…cant link be-
tween money, output, and risk aversion. They show that real money has a
signi…cant role with regard to output if the relative risk aversion level is suf-
ciently high. Even though they study the role of the level of risk aversion in
a non-standard MIU function (CES), they do not include a study of the mi-
crofounded risk aversion shock for the Eurozone, without non-separabilities.
As in other studies, we consider a new Keynesian DSGE model including
standard shocks: a price-markup shock, a monetary policy shock, and a
technology shock. To analyze the role of risk aversion in the dynamics of
other variables, we consider a time-varying relative risk aversion including a
risk aversion shock.
Additionally, we consider a money equation to take account of the be-
haviors of national central banks (before 1999) and the European Central
Bank (after 1999), and to close the model with as many historical variables
as exogenous shocks.
This article contributes to the literature in several ways. First, we analyze
the role of a microfounded risk aversion shock in the dynamics of a New
Keynesian DSGE model. Second, a completely microfounded model with a
risk aversion shock is an original development, both in terms of …ndings as
well as an estimation technique.
Mainly inspired by Smets and Wouters (2007) and Galí (2008), our model
explores the role of risk aversion in in‡ation, output, interest rates, real
money balances, as well as in ‡exible-price output.
Speci…c emphasis will be placed on how the risk aversion shock impacts
the dynamics of these key variables over time. We use Bayesian techniques,
as in An and Schorfheide (2007), to estimate …ve subsamples of the Eurozone
between 1971 and 2011, each for a period of twenty years. This framework
allows us to successively analyze the informational content of the last two
3
crises (subprime and sovereign debt crises) in comparison with other crises
that occurred between 1971 and 2006 in the Eurozone.
Bayesian estimations and dynamic analyses of the model, with impulse
response functions and short- and long-run variance decomposition following
structural shocks, yield di¤erent relationships between risk aversion and other
variables. This approach sheds light on the importance of risk aversion, and
its impact on output and real money balances during the last …ve years (2006
to 2011).
This original focus on the last forty years highlights that the e¤ect of risk
aversion shocks on output and real money balances are stronger in recent
years than in the distant past. It also shows that the role of monetary policy
with regard to output in the short run has decreased in recent years.
Finally, using modern theoretical and empirical tools, this study explores
a fundamental question about the role of the perception of economic risks—
the ability of households to consume now or later— in the dynamics of the
main economic variables for the Eurozone.
The remainder of the paper is organized as follows. Section 2describes
the theoretical setup. In Section 3, the model is calibrated and estimated us-
ing Eurozone data. Impulse response functions and variance decompositions
are analyzed in Section 4. Section 5concludes, and the Appendix presents
additional theoretical and empirical results.
2 The model
The model consists of households that supply labor, purchase goods for con-
sumption, and hold money and bonds; and …rms that hire labor, and produce
and sell di¤erentiated products in monopolistically competitive goods mar-
kets. Each …rm sets the price of the good it produces, but not all …rms
reset their price during each period. Households and …rms behave optimally:
households maximize the expected present value of utility, and …rms maxi-
mize prots. Additionally, there is a central bank that controls the nominal
rate of interest.
2.1 Households
We assume a representative in…nitely lived household, seeking to maximize
Et"1
X
k=0
kUt+k#(1)
4
where Utis the period utility function and  < 1is the discount factor. The
household decides allocation of its consumption expenditure among di¤erent
goods. This requires that the consumption index Ctbe maximized for any
given level of expenditure (Galí,2008). Furthermore, conditional on such
optimal behavior, the period budget constraint takes the form
PtCt+Mt+QtBtBt1+WtNt+Mt1(2)
where t= 0;1;2:::,Ptis an aggregate price index; Mtis the quantity of money
holdings at time t;Btis the quantity of one-period nominally riskless discount
bonds purchased in period tand maturing in period t+ 1 (each bond pays
one unit of money at maturity and is priced at Qt, where it=ln Qtis the
short-term nominal rate); Wtis the nominal wage; and Ntdenotes hours of
work (or the measure of household members employed). The above sequence
of period budget constraints is supplemented with a solvency condition.1
Preferences are measured with a common time-separable utility function
(MIU). Under the assumption of a period utility given by
Ut=C1t
t
1t
+
1Mt
Pt1
N1+
t
1 + (3)
consumption, money demand, labor supply, and bond holdings are chosen
to maximize Eq. 1, subject to Eq. 2and the solvency condition. This MIU
utility function depends positively on the consumption of goods, Ct, and real
money balances, Mt
Pt; and negatively on labor, Nt.t>0is the time-varying
coe¢ cient of the relative risk aversion of households (or the inverse of the
intertemporal elasticity of substitution), de…ned as t=+"r
t, where "r
tis a
risk aversion shock (detailed in Section 3.1). is the inverse of the elasticity
of money holdings with respect to the interest rate, and is the inverse of the
elasticity of work e¤ort with respect to the real wage. and are positive
scale parameters.
This setting leads to the following conditions2, which must hold in equi-
librium, in addition to the budget constraint. The resulting log-linear version
of the …rst-order condition corresponding to the demand for contingent bonds
implies that
ct=Et[ct+1]1
t
(itEt[t+1]c)(4)
where ct= ln (Ct)is the logarithm of aggregate consumption, itis the nomi-
nal interest rate, Et[t+1]is the expected in‡ation rate in period t+ 1 with
knowledge of the information in period t, and c=ln ().
1Such as 8tlim
n!1
Et[Bn]0, in order to avoid Ponzi-type schemes.
2See Appendix 6.A
5
The demand for cash that follows from the household’s optimization prob-
lem is given by
tctmptm=a2it(5)
where mpt=mtptare the log linearized real money balances, m=
ln () + a1, and a1and a2are resulting terms of the …rst-order Taylor
approximation of ln (1 Qt) = a1+a2it.
Real cash holdings depend positively on consumption with an elasticity
equal to t
and negatively on the nominal interest rate.3We consider nominal
interest rate as the policy instrument of the central bank.
The resulting log-linear version of the …rst-order condition corresponding
to the optimal consumption-leisure arbitrage implies that
wtpt=tct+ntn(6)
where wtptcorresponds to the log of the real wage, ntdenotes the log of
hours of work, and n=ln ().
Finally, these equations represent the Euler condition for the optimal in-
tratemporal allocation of consumption (Eq. 4), the intertemporal optimality
condition setting the marginal rate of substitution between money and con-
sumption equal to the opportunity cost of holding money (Eq. 5), and the
intratemporal optimality condition setting the marginal rate of substitution
between leisure and consumption equal to the real wage (Eq. 6).
2.2 Firms
Backus et al. (1992) have shown that capital appears to play a rather minor
role in the business cycle. To simplify the analysis and focus on the role of
risk, we do not include the capital accumulation process in this model, as in
Galí (2008).
We assume a continuum of …rms indexed by i2[0;1]. Although each …rm
produces a di¤erentiated good, they all use identical technology, represented
by the following production function
Yt(i) = AtNt(i)1(7)
where At= exp ("a
t)represents the level of technology, assumed to be com-
mon to all …rms, and which evolves exogenously over time, and "a
tis a tech-
nology shock.
All …rms face an identical isoelastic demand schedule and take the aggre-
gate price level Ptand aggregate consumption index Ctas given. As in the
3Since 1
>1,a2>0.
6
standard Calvo (1983) model, our generalization features monopolistic com-
petition and staggered price setting. At any time, t, only a fraction of …rms,
1, with 0<  < 1, can reset their prices optimally, while the remaining
rms index their prices to lagged in‡ation.
2.3 Central bank
The central bank is assumed to set its nominal interest rate according to an
augmented smoothed Taylor (1993) rule such as:
it= (1 i)(tc) + xytyf
t+m(mptmpc)+iit1+"i
t
(8)
where ,x, and mare policy coe¢ cients re‡ecting the weight assigned to
the in‡ation gap, the output gap, and the real money gap, respectively; the
parameter 0< i<1captures the degree of interest rate smoothing; and "i
t
is an exogenous ad hoc shock accounting for ‡uctuations of the nominal in-
terest rate. cis an in‡ation target and mpcis a money target, essentially in-
cluded to account for changes in policies targeting in‡ation (Svensson,1999)
and monetary aggregates (Fourçans,2007). Other studies introduce a rele-
vant money variable in the Eurozone Taylor rule (Andrés et al.,2006,2009;
Barthélemy et al.,2011;Benchimol and Fourçans,2012).
Additionally, mtakes into account the potential money targeting of the
national central bank before the creation of the European Central Bank
(ECB, 1999). After 1999, the ECB followed an explicit money targeting
policy until 2004, called the Two Pillars policy (Barthélemy et al.,2011),
and might have even followed an implicit money targeting policy after that
(Kahn and Benolkin,2007).
3 Empirical results
3.1 DSGE model
Our macro model consists of …ve equations and …ve dependent variables:
in‡ation, nominal interest rate, output, real money balances, and ‡exible-
price output. Flexible-price output is completely determined by shocks.
yf
t=1 +
t(1 ) + +"a
t+(1 )ln (1 ) + nln "
"1
t(1 ) + +(9)
t=Et[t+1 ] + (1 ) (1 ) (t(1 ) + +)
(1 +t)ytyf
t(10)
7
yt=Et[yt+1]1
t(itEt[t+1]c)(11)
mpt=t
yta2
itm
(12)
it= (1 i)(tc) + xytyf
t+m(mptmpc)+iit1+"i
t
(13)
where a1= ln 1e1
1
e
1
1
and a2=1
e
1
1
.
All structural shocks are assumed to follow a …rst-order autoregressive
process with an i.i.d. (independent and identically distributed) normal error
term, such as "k
t=k"k
t1+!k;t, where "k;t N(0; k)for k=fp; i; a; rg.
3.2 Calibration
Following standard conventions, we calibrate beta distributions for parame-
ters that fall between zero and one, inverted gamma distributions for para-
meters that need to be constrained at greater than zero, and normal distri-
butions in other cases.
The parameters of the utility function are assumed to be distributed as
follows. Only the discount factor is …xed at 0:98 in the estimation procedure.
The intertemporal elasticity of substitution (i.e., the level of relative risk
aversion) is set at 2, a mean between the calibrations of Rabanal and Rubio-
Ramírez (2005) and Casares (2007), and consistent with the calibrated value
used by Kollmann (2001) and the value estimated by Lindé et al. (2009).
The inverse of the Frisch elasticity of labor supply is assumed to be approx-
imately 1, as in Galí (2008), and the scale parameters on money and labor
are assumed to be approximately 0:2, as in Benchimol and Fourçans (2012).
The calibration of ,, and "comes from Smets and Wouters (2007),
Casares (2007), and Galí (2008). The smoothed Taylor rule (i,,x,
and m) priors are calibrated following Smets and Wouters (2003), Andrés
et al. (2009), and Barthélemy et al. (2011). To observe both the behavior
of the central bank and risk aversion, we assign a higher standard error
(0:2) and a Normal prior law for the relative risk aversion level and for the
Taylor rule coe¢ cients (including in‡ation and money targets), except for
the smoothing parameter, which is restricted to be positive and less than
one (Beta distribution). The in‡ation target, c, is calibrated to 2percent,
and the money target, mpc, is assumed to be approximately 4percent.
The calibration of the shock persistence parameters and the standard er-
rors of the innovations follow Smets and Wouters (2007). All the standard
errors of shocks are assumed to be distributed according to inverted Gamma
8
distributions, with prior means of 0:01. The latter ensures that these parame-
ters have positive support. The autoregressive parameters are all assumed to
follow Beta distributions. All of these distributions are centered around 0:75,
except for the autoregressive parameter of the monetary policy shock and the
risk aversion shock, which are centered around 0:50, as in Smets and Wouters
(2007). We take a common standard error of 0:15 for the shock persistence
parameters, which is a mean between that of Benchimol and Fourçans (2012)
and Smets and Wouters (2007).
Law Mean Std. Law Mean Std.
beta 0.33 0.10 mnormal 1.00 0.20
beta 0.66 0.10 cnormal 2.00 0.20
normal 2.00 0.20 mpcnormal 4.00 0.20
vnormal 1.50 0.10 abeta 0.75 0.15
"normal 6.00 0.10 pbeta 0.75 0.15
normal 1.00 0.10 ibeta 0.50 0.15
beta 0.20 0.05 rbeta 0.50 0.15
beta 0.20 0.05 ainvgamma 0.01 2.00
ibeta 0.50 0.10 pinvgamma 0.01 2.00
normal 3.00 0.20 iinvgamma 0.01 2.00
xnormal 1.50 0.20 rinvgamma 0.01 2.00
Table 1: Calibration summary
3.3 Eurozone data
In our Eurozone model, tis the detrended in‡ation rate measured as the
yearly log di¤erence of the detrended GDP de‡ator from one quarter to the
same quarter of the previous year; ytis the detrended output per capita
measured as the di¤erence between the log of real GDP per capita and its
trend; and itis the short-term (3-month) detrended nominal interest rate.
These data are extracted from the AWM (Area Wide Model) database (Fagan
et al.,2001). mptis the detrended real money balance per capita measured
as the di¤erence between real money per capita and its trend, where real
money per capita is measured as the log di¤erence between money stock per
capita and the GDP de‡ator. We use the M3monetary aggregate from the
Eurostat database.
9
3.4 Results
The model is estimated with 160 observations from the …rst quarter of 1971 to
the …rst quarter of 2011, with Bayesian techniques, as in Smets and Wouters
(2007). However, to capture derent policies and risk perceptions in the
Eurozone between 1971 and 2011, and more speci…cally between 2006 and
2011, we divide this large sample into …ve subsamples, each consisting of 80
observations (20 years).
This procedure allows us to analyze …ve di¤erent periods with a su¢ -
ciently large sample, as speci…ed in Fernández-Villaverde and Rubio-Ramírez
(2004). Accordingly, we estimate our model over …ve di¤erent periods: from
1971 Q1 to 1991 Q1 (P1); from 1976 Q1 to 1996 Q1 (P2); from 1981 Q1 to
2001 Q1 (P3); from 1986 Q1 to 2006 Q1 (P4); and from 1991 Q1 to 2011 Q1
(P5).
0.5
0.6
0.7
1.2
1.4
0.7
0.8
0.9
0
0.2
0.4
1.9
2
2.1
3.98
4
4.02
2.6
2.8
0.92
0.94
0.96
0.4
0.45
0.5
0.99
1
1.01
6
6.005
6.01
1.9
2
2.1
0.198
0.2
0.202
0.2
0.21
0.22
0.98
1
P1 P2 P3 P4 P5
0.6
0.8
1
P1 P2 P3 P4 P5
0.35
0.4
0.45
P1 P2 P3 P4 P5
0.9
0.95
1
Figure 1: Bayesian estimation of parameters over the selected periods
The estimation of the implied posterior distribution of the parameters
across the …ve periods (Fig. 1) is performed using the Metropolis-Hastings al-
gorithm (10 distinct chains, each of 100;000 draws). The average acceptance
rates per chain are included in the interval [0:19; 0:22], and the Student’s
t-tests are all above 1:96 in absolute terms.
10
To assess the model validation, we ensure convergence of the proposed
distribution to the target distribution for each period. Appendix 6.Bshows
that the estimation results are valid and that convergence is obtained for all
estimations and all moments.
Distribution of priors and posteriors are presented in Appendix 6.C. It
shows that the maximum posterior distribution reaches the posterior mean
of each estimated parameter. The estimation is relatively well identi…ed, and
the data is quite informative for most of the estimated microparameters.
4 Interpretation
We analyze the forecast error variance decomposition of each variable follow-
ing exogenous structural shocks.
0
10
20
Output
Risk avers ion
0
2
4
6Price-markup
0
20
40
60 Monetary policy
0
50
100 Technology
0
0.05
0.1
Inflation
0
50
100
0
0.05
0.1
0
5
10
15
0
5
10
In ter e st r a te
0
50
100
0
50
100
0
5
10
15
P1 P2 P3 P4 P5
0
20
40
60
Real money
P1 P2 P3 P4 P5
0
5
10
P1 P2 P3 P4 P5
0
10
20
30
P1 P2 P3 P4 P5
0
50
100
Short run v arianc e Long run variance
Figure 2: Variance decomposition over the selected periods
The analysis is conducted via an unconditional variance decomposition to
analyze long-term variance decomposition (the gray bar in Fig. 2), and con-
ditional variance decomposition, conditionally to the …rst period, to analyze
short-run variance decomposition (the black bar in Fig. 2).
11
Fig. 2shows that output is mainly explained by the technology shock
in the long run (approximately 90%), and by the monetary policy shock
(approximately 35%) and the technology shock (approximately 50%) in the
short run. Rest of the variance in output is explained by the risk aversion
shock (approximately 5% from P1 to P4, and more than 15% for P5) in the
short term, whereas risk aversion shock has a limited role in output variance
in the long run.
Fig. 2also shows that, in accordance with the literature, in‡ation is
mainly explained by the price-markup shock, and interest rate variance is
mainly driven by monetary policy in the short run and monetary policy
and price-markup in the long run. Furthermore, most of the variance in
real money balances is induced by the risk aversion shock (approximately
40%) and the monetary policy shock (approximately 25%) in the short run,
whereas in the long run, real money balance variance is mainly driven by the
technology shock. All these results are in line with the literature.
Sections 4.1 to 4.4 present the role of each shock in the ‡uctuations of the
macroeconomic variables, and the response of these key variables to struc-
tural shocks over the study period.
4.1 Price-markup shock
Fig. 2shows that the price-markup shock explains almost all of the variability
in the in‡ation rate, at least in the short run. Its role in the long run on real
money balance ‡uctuations is halved across the study period.
12
020 40
0
0.2
0.4
0.6
0.8 Inflation
020 40
-0.4
-0.3
-0.2
-0.1
0Output
020 40
-0.4
-0.3
-0.2
-0.1
0Output gap
020 40
0
0.2
0.4
0.6
0.8 Nominal interest rate
020 40
-0.2
-0.15
-0.1
-0.05
0
0.05 R e al inte r es t r ate
020 40
-0.8
-0.6
-0.4
-0.2
0Real money balances
P5 P4 P3 P2 P1
Figure 3: Impulse response function with respect to a price-markup shock
Fig. 3shows that from P1 to P5, the impact of the price-markup shock
on output, in‡ation, nominal interest rate, and real money balances is at
least halved. While transitioning from one period to another, the impact
of the price-markup shock on the overall economy reduces. Regardless of
the period under study, after a positive price-markup shock, in‡ation rate
increases, thus, nominal interest rate increases, decreasing real interest rate,
output, output gap, and real money balances.
4.2 Technology shock
Fig. 2indicates that technology plays an increasingly important role in the
short term for the ination rate and, thus, for the interest rate in the selected
period.
13
020 40
-0.08
-0.07
-0.06
-0.05
-0.04
-0.03
-0.02 Inflatio n
020 40
0.4
0.6
0.8
1
1.2 Output
020 40
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1 Output gap
020 40
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02 Nominal interest rate
020 40
-0.04
-0.03
-0.02
-0.01
0
0.01 R e al inte r es t r a te
020 40
0.7
0.8
0.9
1
1.1
1.2
1.3
Real money balances
P5 P4 P3 P2 P1
Figure 4: Impulse response function with respect to a technology shock
Fig. 4highlights that in response to a positive technology shock, output
increases but in‡ation decreases, resulting in an increase in real money hold-
ing, and a slight decrease in nominal interest rate and output gap. The im-
provement in technology is partly accommodated by the central bank, which
lowers the nominal and real interest rate, while increasing the quantity of
money in circulation.
Interestingly, note that maximum sensitivity of output to a technology
shock was observed in P1 and P2.
4.3 Monetary policy shock
Fig. 2shows that compared to the previous periods (P1 to P4), monetary
policy has a smaller role in the short run output variability over the last
period (P5)–from around 37% to 22%, respectively. This highlights a switch
from the role of the monetary policy to the role of risk aversion during recent
years. This con…rms the declining in‡uence of European monetary policy
relative to the in‡uence of risk aversion shocks in recent years.
14
020 40
-0.01
-0.008
-0.006
-0.004
-0.002
0Inflation
020 40
-1
-0 .8
-0 .6
-0 .4
-0 .2
0Output
020 40
-1
-0 .8
-0 .6
-0 .4
-0 .2
0Output gap
020 40
0
0.2
0.4
0.6
0.8
1N o min a l in ter e s t ra te
020 40
0
0.2
0.4
0.6
0.8
1Real interest rate
020 40
-1 .5
-1
-0 .5
0Real money bala nces
P5 P4 P3 P2 P1
Figure 5: Impulse response function with respect to a monetary policy shock
Fig. 5indicates that in response to an interest rate shock, in‡ation rate,
output, output gap, and real money balances fall. The nominal and real
interest rates rise. A positive monetary policy shock could also induce a fall
in interest rates due to a low enough degree of intertemporal substitution (i.e.,
the risk aversion parameter is high enough), which generates a large impact
response of current consumption relative to future consumption (Jeanne,
1994;Christiano et al.,1997). Note that in P5, nominal and real interest
rates are the least sensitive of all other periods, suggesting the largest relative
risk aversion compared to all other periods.
4.4 Risk aversion shock
Fig. 2shows that output and real money balance variances have an important
risk aversion shock component. This …nding shows the leading role of relative
risk aversion in the dynamics of output (Black and Dowd,2011) and real
money balances (Benchimol,2011).
Although in‡ation rate, nominal interest rate, and ‡exible-price output
are strong components of output, risk aversion has a minor role to play in
15
the variance of in‡ation and interest rate, and it does not play a role with
regard to the ‡exible-price output (less than 0:2% in the short- and long-run),
which is completely determined by the technology shock. It also shows that
in‡ation and interest rate variances are quasi-una¤ected by the introduction
of the risk aversion shock, allowing these variables to be mainly explained by
the price-markup shock and the monetary policy shock, respectively.
Additionally, Fig. 2shows that risk aversion plays a more signi…cant
role in output dynamics in the last period (P5) than in other periods (P1 to
P4) in the short run. This …nding re‡ects the increasing role played by risk
aversion in more recent years (between 2006 and 2011) as compared to the
past (between 1971 and 2006).
020 40
0
0.002
0.004
0.006
0.008
0.01
0.012 In fla tion
020 40
-0.5
-0.4
-0.3
-0.2
-0.1
0Output
020 40
-0.4
-0.3
-0.2
-0.1
0Output gap
020 40
0
0.1
0.2
0.3
0.4 Nominal interest rate
020 40
0
0.1
0.2
0.3
0.4 Real interest rate
020 40
0
0.5
1
1.5
2Real money balances
P5 P4 P3 P2 P1
Figure 6: Impulse response function with respect to a risk aversion shock
This case is very interesting because Fig. 6shows that a risk aversion
shock leads to a decrease in output and an increase in in‡ation. This implies a
tightening of monetary policy (because of the strong weight that the central
bank places on in‡ation) and its strength depends on the period (strong
monetary policy tightening in P1 and low monetary policy tightening in P5).
The risk aversion shock also implies an increase in real money balances and
real money growth, and a decrease in the output gap. Our risk aversion
16
shock also suggests that uncertainty is counter cyclical (Baker and Bloom,
2013).
Household consumption reduces (decreasing output), and companies in-
crease their price (to face high risk aversion and possibly, low consumption),
which implies an increase in the in‡ation rate, constrained by a tightening
of monetary policy.
Fig. 6exhibits that the risk aversion shock has a longer impact in P5 than
it does in the other periods. This is due to the increase of the autoregressive
parameter of the risk aversion shock, r, over the periods, as shown in Fig.
1. Although the sensitivity of monetary policy with respect to risk aversion
shock is lower in P5 than during other periods, it is more persistent in P5
than in the other periods. This highlights that the central bank reacts less
strongly after a risk aversion shock, but the persistence of the impact of this
risk aversion shock on nominal interest rate is stronger over time.
Bloom (2009) simulates a macro uncertainty shock, which produces a
rapid drop in aggregate output, mainly because higher uncertainty causes
rms to temporarily pause their investment and hiring. Our results suggest
that the impact of the risk aversion shock— a micro uncertainty shock—
on output and output gap is very important during the last period, P5,
as compared to the other periods. As a matter of fact, an important part
of the variation in output is dependent, through the risk aversion shock,
on major shocks such as crises, news, and disasters (Bloom,2009;Baker
and Bloom,2013). Controlling these media-parameters— for instance, by
communication–could attenuate their impact on growth.
The leading role of the risk aversion shock in the dynamics of real money
balance in the short run is another important …nding. Fig. 2indicates that
real money balances are mainly explained by the technology shock (approxi-
mately 80%) in the long run, whereas in the short run, real money balances
are mainly explained by the risk aversion shock and the monetary policy
shock. Last but not the least, in line with Benchimol and Fourçans (2012),
a risk aversion shock drastically increases real money balances response, de-
spite increasing in‡ation and nominal interest rate responses (Fig. 6).
5 Conclusion
Risk aversion as a concept in economics and …nance is based on the behavior
of consumers and investors who are exposed to uncertainty. It is the reluc-
tance of a person to accept a bargain with an uncertain pay, rather than
one that o¤ers a more certain, but possibly lower, expected payo¤.
This paper presents a standard New Keynesian DSGE model that includes
17
a risk aversion shock. It shows the involvement of this risk aversion shock in
the dynamics of the economy: it increases in‡ation, decreases output (Fig.
6), and diminishes the impact of the action by the central bank on output
variance, at least in the short run (Fig. 2).
Additionally, risk aversion plays an important role in output and real
money balance dynamics. It is clearly identi…ed that risk aversion plays a
negative role in determining output, whereas it increases real money balances
and real money growth in the initial period (Fig. 6).
Moreover, while estimations are quite robust (Fig. 7to Fig. 12), they
show that risk aversion shock has a stronger impact on output dynamics
during the last twenty years (P5) as compared to other analyzed periods
(P1 to P4). This result is explained by the inclusion of the subprime and
sovereign debt crises in P5 from 2007 to 2011.
This enhanced baseline model shows the importance of such a parameter
to the economy, especially its impact on output, money, and monetary policy.
It also serves to show the importance of controlling shocks to the agents’risk
aversion, for instance, by communication.
6 Appendix
A Solving the model
Price dynamics
Let us assume a set of …rms not reoptimizing their posted prices in period
t. Using the de…nition of the aggregate price level and the fact that all …rms
resetting prices choose an identical price P
t, leads to
Pt=hP 1t
t1+ (1 ) (P
t)1ti1
1t(14)
where t= 1 + 1
1
"1+"p
t
is the elasticity of substitution between consumption
goods in period t, and t
t1is the markup of prices over marginal costs (time
varying). Dividing both sides by Pt1and log-linearizing around P
t=Pt1
yields
t= (1 ) (p
tpt1):(15)
In this setup, we do not assume inertial dynamics of prices. In‡ation
results from …rms reoptimizing their price plans in any given period, and
choosing a price that di¤ers from the economy’s average price in the previous
period.
18
Price setting
A …rm reoptimizing in period tchooses price P
tthat maximizes the cur-
rent market value of the pro…ts generated, while the price remains e¤ective.
This problem resolves and leads to a …rst-order Taylor expansion around the
zero in‡ation steady state:
p
tpt1= (1 )
1
X
k=0
()kEtcmct+kjt+ (pt+kpt1)(16)
where cmct+kjt=mct+kjtmc denotes the log deviation of marginal cost from
its steady state value mc =, and = ln "
"1is the log of the desired
gross markup.
Equilibrium
Market clearing in the goods market requires Yt(i) = Ct(i)for all i2[0;1]
and all t. Aggregate output is de…ned as Yt=R1
0Yt(i)11
tdit
t1; it
follows that Yt=Ctmust hold for all t. One can combine the above goods
market clearing condition with the consumer’s Euler equation (4) to yield
the equilibrium condition
yt=Et[yt+1]1
t(itEt[t+1]c)(17)
Market clearing in the labor market requires Nt=R1
0Nt(i)di. With
the production function (7) and taking logs, one can express the following
approximate relationship between aggregate output, employment, and tech-
nology as
yt="a
t+ (1 )nt(18)
An expression is derived for the marginal cost of an individual …rm in
terms of the economy’s average real marginal cost:
mct= (wtpt)mpnt(19)
=wtpt1
1("a
tyt)ln (1 )
for all t, where mpntde…nes the economy’s average marginal product of labor.
As mct+kjt= (wt+kpt+k)mpnt+kjt, we have
mct+kjt=mct+kt
1(p
tpt+k)(20)
19
where the second equality follows from the demand schedule combined with
the market clearing condition ct=yt. Substituting (20) into (16) yields
p
tpt1= (1 )
1
X
k=0
t+k()kEt[cmct+k] +
1
X
k=0
()kEt[t+k](21)
where t=1
1+t1is time varying to take into account the markup
shock.
Finally, (15) and (21) yield the in‡ation equation
t=Et[t+1 ] + mctcmct(22)
where ,mct=(1)(1)
t.mctis strictly decreasing in the index of
price stickiness , in the measure of decreasing returns , and in the demand
elasticity t.
Next, a relationship is derived between the economy’s real marginal cost
and a measure of aggregate economic activity. From (6) and (18), the average
real marginal cost can be expressed as
mct=t++
1yt1 +
1"a
tlog (1 )n(23)
Under ‡exible prices, the real marginal cost is constant and equal to mc =
. De…ning the natural level of output, denoted by yf
t, as the equilibrium
level of output under ‡exible prices leads to
mc =t++
1yf
t1 +
1"a
tlog (1 )n(24)
thus, implying
yf
t=a"a
t+c(25)
where a=1+
t(1)++and c=(1)(ln(1)+nln("
"1))
t(1)++. Subtracting (26)
from (25) yields
cmct=t++
1ytyf
t(26)
where cmct=mctmc is the real marginal cost gap and ytyf
tis the output
gap. Combining the above equation with (24), we obtain
t=Et[t+1 ] + tytyf
t(27)
where t=(1)(1)(t(1)++)
(1+t), and ytyf
tis the output gap.
20
The second key equation describing the equilibrium of the model is ob-
tained by rewriting (20) to determine output
yt=Et[yt+1]1
t(itEt[t+1]c)(28)
Equation (28) is thus, a dynamic IS equation including real money bal-
ances.
The third key equation describes the behavior of real money balances.
From (5), we obtain
mpt=t
yta2
itm
(29)
B Model validation
The red and blue lines in Fig. 7represent an aggregate measure based on the
eigenvalues of the variance-covariance matrix of each parameter both within
and between chains. Each graph represents speci…c convergence measures
and has two distinct lines that represent the results within and between
chains. These measures are related to the analysis of the parameter’s mean
(…rst moment), variance (second moment), and third moment of the model
for the relevant period. Convergence requires both lines for each of the three
measures to become relatively constant and converge to each other.
Diagnoses of the numerical maximization of the posterior kernel indicate
that the optimization procedure was able to obtain a robust maximum for the
posterior kernel. A diagnosis of the overall convergence for the Metropolis-
Hastings sampling algorithm is provided in Fig. 7.
Diagnoses for each individual parameter were also obtained, following the
same structure used for overall convergence. Most of the parameters do not
seem to exhibit convergence problems, notwithstanding that this evidence is
stronger for some parameters than it is for others.
21
7
8
9
10
P1
Firs t moment
8
10
12
14 Second moment
40
60
80
100 Third moment
6
8
10
12
P2
5
10
15
40
60
80
100
7
8
9
10
P3
5
10
15
0
50
100
7
8
9
10
P4
8
10
12
14
40
60
80
0 5 10
x 104
7
8
9
Iterations
P5
0 5 10
x 104
5
10
15
Iterations 0 5 10
x 104
0
50
100
Iterations
Within variance Betw een and within variance
Figure 7: Multivariate Metropolis-Hastings convergence diagnosis
22
C Priors and posteriors
The following …gures present the priors and posteriors of the estimated struc-
tural parameters over the study period.
00.45 0 .9
0
2
00.94
0
10
20
02.5
0
1
2
0 2
0
2
4
00.99
0
2
06.01
0
2
00.2 0.4 0.6
0
5
00.2 0.4 0.6
0
5
0 2
0
1
0 4
0
1
00.54
0
2
4
02.8
0
1
2
01.76
0
1
2
00.73 1.46
0
1
2
00.99
0
50
00.39 0.78
0
5
00.96
0
10
20
00.52 1.04
0
2
4
00.05
0
50
100
00.05
0
50
100
00.05
0
50
100
00.05
0
100
Posterior distribution Prior distribution Pos terior mode
Figure 8: Priors and posteriors of the estimated parameters (P1)
23
00.46 0.92
0
2
00.93
0
10
20
02.6
0
1
2
0 2
0
2
0 1
0
2
06.01
0
2
4
00.2 0.4
0
5
00.21 0.42
0
5
0 2
0
1
2
0 4
0
1
00.52
0
2
4
02.82
0
1
2
01.78
0
1
2
00.430.861.291.72
0
1
2
00.99
0
50
00.42 0.84
0
5
00.95
0
10
00.77
0
5
00.05
0
50
100
00.05
0
50
100
00.05
0
50
100
00.05
0
100
Posterior distribution Prior dis tribution Posterior mode
Figure 9: Priors and posteriors of the estimated parameters (P2)
24
00.43 0.86
0
2
00.94
0
10
20
02.54
0
1
2
02.04
0
2
4
00.99
0
2
4
0 6
0
2
4
00.2 0.4
0
5
00.2 0.4 0.6
0
5
01.97
0
1
0 4
0
1
00.57
0
5
02.91
0
1
2
01.69
0
1
2
00.64 1.28 1.92
0
1
2
00.99
0
50
00.42 0.84
0
5
00.94
0
10
00.66
0
5
00.05
0
100
00.05
0
100
00.05
0
100
00.05
0
100
200
Posterior distribution Prior dis tribution Posterior mode
Figure 10: Priors and posteriors of the estimated parameters (P3)
25
00.43 0.86
0
2
00.93
0
10
20
02.5
0
1
2
02.06
0
2
4
00.99
0
2
0 6
0
2
4
00.2 0.4
0
5
00.21 0.42
0
5
01.95
0
1
03.99
0
1
00.62
0
5
02.95
0
1
2
01.66
0
1
2
00.45 0 .9 1.35 1.8
0
1
2
00.99
0
50
100
00.46
0
5
00.94
0
10
20
00.81
0
5
00.05
0
100
00.05
0
100
200
00.05
0
100
200
00.05
0
100
Posterior distribution Prior distribution Posterior mode
Figure 11: Priors and posteriors of the estimated parameters (P4)
26
00.43 0.86
0
2
00.95
0
20
02.51
0
1
2
02.05
0
2
4
00.99
0
2
0 6
0
2
00.2 0.4 0.6
0
5
00.2 0.4
0
5
01.98
0
1
0 4
0
1
00.56
0
5
02.95
0
1
2
01.66
0
1
2
00.73 1.46
0
1
2
00.99
0
50
00.42 0.84
0
5
00.95
0
10
20
00.9
0
10
00.05
0
100
00.05
0
100
200
00.05
0
100
00.05
0
100
200
Posterior distribution Prior dis tribution Posterior mode
Figure 12: Priors and posteriors of the estimated parameters (P5)
27
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Uncertainty about an economy's regime can change drastically around a crisis. An imported crisis such as the global financial crisis in the euro area highlights the effect of foreign shocks. Estimating an open-economy nonlinear dynamic stochastic general equilibrium model for the euro area and the United States including Markov-switching volatility shocks, we show that these shocks were significant during the global financial crisis compared with periods of calm. We describe how US shocks from both the real economy and financial markets affected the euro area economy and how bond reallocation occurred between short- and long-term maturities during the global financial crisis. Importantly, the estimated nonlinearities when domestic and foreign financial markets influence the economy, should not be neglected. The nonlinear behavior of market-related variables highlights the importance of higher-order estimation for providing additional interpretations to policymakers.
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The risk attitude of investors is a key factor determining financial asset prices and market trends. Changes in risk attitude may be due to the interference of macro-level (business cycle) and micro-level (individual experience) effects. We investigate the impact of individual experience on the subsequent risk-taking attitude of professionals via the analysis of the trading activity of 351 non-financial firms and (non-bank) financial institutions (insurance companies, financial intermediaries, etc.) covering 57,039 FX forward transactions in a highly volatile period between January 2008 and November 2012. Panel regressions for all firms and institutions do not show significant behavioral patterns. When investigating each client separately, however, we find that 39.7% of the clients having enough transactions to analyze statistically tend to increase their risk exposure irrationally after large gains or losses which can be the manifestation of the break-even and house-money effects well-documented in the literature for non-professionals. This irrational behavior may destroy value, so both market players and regulators should pay attention to monitor and control it.
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El presente trabajo se enfoca en el creciente interés a nivel global por el estudio de las finanzas tecnológicas (Fintech) en lo general, y el fondeo colectivo en lo particular, como herramientas tecnológicas que permiten la financiación de diversas iniciativas de emprendimiento. El objetivo es identificar los factores que impactan en el éxito de financiamiento de las Pymes que participan en las plataformas de fondeo colectivo, mediante la técnica de análisis de la covarianza (Ancova) en las calificaciones de riesgo mientras se controlan estadísticamente el monto del crédito, el plazo, la tasa y el número de fondeadores. Los resultados muestran que el riesgo impacta significativamente el éxito de fondeo, resultando además ser covariables relevantes: la tasa de interés pagada, el monto financiado, y el número de fondeadores, no siendo el caso para el plazo del crédito pues es una covariable poco significativa. Del análisis del éxito de fondeo y su relación con el riesgo, se destaca la tasa de retorno como principal incentivo en el fondeo de las empresas, sin embargo, resta profundizar las investigaciones desde el impacto ético, social, y ambiental en las decisiones de fondeo.
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Using the Blue Ocean strategy as an underlying strategic element, this dissertation analyses whether this statement holds true for the rather more conservative banking sector in Germany and the overall risk-averse mindset of the German population by using both quantitative and qualitative elements to assess the current market share of FinTech companies in the Federal Republic, as well as grasp a potential outlook on the future development. A literature review of the strategic framework, the banking sector in Germany and the FinTech sector is carried out accordingly. Subsequently, a formal verification as to whether the banking sector is a "Red Ocean" and if the FinTech industry is a "Blue Ocean" is carried out using case studies from both sectors. A quantitative analysis of banking customers in Germany and their use of FinTech companies is conducted by way of an online survey, with selected participants being interviewed thereafter to gain additional insights. Data evaluation is made using pivotal analysis and cross tabulation of survey results and interview findings, along with extrapolating indicators to reflect the full size of the German banking sector and transactional volumes per segment are provided and examined for signs of elevated FinTech use in the market. Despite several limitations from where ideas for future research are derived, the outcomes provide an overview of existing trends for the use of FinTechs in Germany. The main finding is that with the notable exception of payment solutions, Germans do not have a high affinity towards FinTechs, rendering them a byproduct of the financial service industry, with limited market share and low potential.
Thesis
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Using the Blue Ocean strategy as an underlying strategic element, this dissertation analyses whether this statement holds true for the rather more conservative banking sector in Germany and the overall risk-averse mindset of the German population by using both quantitative and qualitative elements to assess the current market share of FinTech companies in the Federal Republic, as well as grasp a potential outlook on the future development. A literature review of the strategic framework, the banking sector in Germany and the FinTech sector is carried out accordingly. Subsequently, a formal verification as to whether the banking sector is a ‘Red Ocean’ and if the FinTech industry is a ‘Blue Ocean’ is carried out using case studies from both sectors. A quantitative analysis of banking customers in Germany and their use of FinTech companies is conducted by way of an online survey, with selected participants being interviewed thereafter to gain additional insights. Data evaluation is made using pivotal analysis and cross tabulation of survey results and interview findings, along with extrapolating indicators to reflect the full size of the German banking sector and transactional volumes per segment are provided and examined for signs of elevated FinTech use in the market. Despite several limitations from where ideas for future research are derived, the outcomes provide an overview of existing trends for the use of FinTechs in Germany. The main finding is that with the notable exception of payment solutions, Germans do not have a high affinity towards FinTechs, rendering them a byproduct of the financial service industry, with limited market share and low potential.
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We present and test a model of the Eurozone, with a special emphasis on the role of risk aversion and money. The model follows the New Keynesian DSGE framework, money being introduced in the utility function with a non-separability assumption. Money is also introduced in the Taylor rule. By using Bayesian estimation techniques, we shed light on the determinants of output, inflation, money, interest rate, flexible-price output, and flexible-price real money balance dynamics. The role of money is investigated further. Its impact on output depends on the degree of risk aversion. Money plays a minor role in the estimated model. Yet, a higher level of risk aversion would imply that money had significant quantitative effects on business cycle fluctuations.
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Thesis
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This thesis presents three theoretical and empirical models of the Eurozone, highlighting the role of risk aversion and money on different macroeconomic variables. These intertemporal dynamic stochastic general equilibrium (DSGE) models respect the New Keynesian framework. In a first basic model, we show that risk aversion affects output, contributing to its decline, especially during crisis periods. During these crises (European Monetary System, 1992; Internet, 2000, Subprime, 2007), risk aversion significantly impacts real money holdings. In a second model, in which money is considered as a factor of production, the latter has no significant role in the dynamics of other variables. The assumption of constant returns to scale is also rejected. In a third model, using a non-separable utility function between consumption and real money balances, we show that the role of the latter on output depends on the degree of agents' risk aversion, becoming significant when this level is twice higher than the standard degree. Finally, we test and compare this model with the first basic model during the three periods mentioned above. Money explains a significant part of output's variance during crises. Moreover, our analysis shows that a non-separability assumption between consumption and real balances has better predictive power than a separable model, at least during crisis periods.
Conference Paper
If decision costs lead agents to update consumption every D periods, then econometricians will find an anomalously low correlation between equity returns and consumption growth (Lynch, 1996). We analytically characterize the dynamic properties of an economy composed of consumers who have such delayed updating. In our setting, an econometrician using an Euler equation procedure would infer a coefficient of relative risk aversion biased up by a factor of 6D. Hence with quarterly data, if agents adjust their consumption every D=4 quarters, the imputed coefficient of relative risk aversion will be 24 times greater than the true value. High levels of risk aversion implied by the equity premium and violations of the Hansen-Jagannathan bounds cease to be puzzles. The neoclassical model with delayed adjustment explains the consumption behavior of shareholders. Once limited participation is taken into account, the model matches most properties of aggregate consumption and equity returns, including new evidence that the covariance between ln(Ct+h/C-t) and Rt+1 slowly rises with h.
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