Taxing the Financial Transactions in Europe : new empirical investigations

Brief Vitae Brief Vitae Motivations and the story of the FTT project Motivations and the story of the FTT project Motivations and t
Taxing the Financial Transactions in Europe :
new empirical investigations
Olivier Damette (Universiy of Lorraine (France),
BETA-CNRS, CAC member, Climate Economics Chair Paris
Associate)
Eesti Pank Seminar, Tallinn
March 2018
Olivier Damette Taxing the Financial Transactions in Europe : new empirical invest

Brief Vitae
PhD in Economics in 2007, Nancy University (France),
« Essays on Tobin Tax »
Junior Economist-Econometrician, French Central
Bank, Paris, 2008-2009 ; medium-run forecasting,
macroeconometric modeling, academic research
Assistant Professor, Paris 12 University, 2009-2012
Full Professor, Lorraine University, Metz, BETA-CNRS
Research Fellow, CAC Cliometrics and Complexity (Lyon)
and Cimate Economic Chair Paris, since 2015
Invited Researcher (Poland, Canada), grant from the
French government

Research topics related to Central Banks activity
Topics New-Keynesian Macroeconomics : Tobin Tax,
Monetary policy and Cliometrics, Wealth effects
Methodology : Macroeconometrics (NL Times Series and
NS Panel Data), GE modeling (CGE, DSGE)
a few publications
1 2017 : ”Liquidity traps and large-scale ﬁnancial
crises”, (with E. Castelnuovo G. Cagianno, A. Parent, G.
Pellegrino), Journal of Economic Dynamics and Control
2 2016 : ”Did the FED respond to liquidity shortage episodes
during the Great Depression ?” (with A. Parent),
Macroeconomic Dynamics
3 2016 :”Mixture Distribution Hypothesis and the impact of a
Tobin tax on exchange rate volatility : a reassessment”,
Macroeconomic Dynamics
4 2010 : ”Wealth effects on private consumption : The French
case” (with V. Chauvin), Banque de France Working Paper
+ Springer book

Motivations and the story of the FTT project
Since the downturn/subprime crisis/Euro Zone and
Sovereign Debt crisis, Tobin Tax or FTT is viewed as a
possible way of dampening down the deficit of
industrialized countries (like France, TT can generate large
revenues) and to regulate the financial system by taxing
the intermediaries (Robin Hood Tax)
Taxation project for European Countries discussed in
European Parliament : approved in 2011 initially but
still not at work, only unilateral tax in France and in Italy
(only in some equities concerning big companies), why ?
First answer : a very controversial topic !
The original purpose of a Tobin Tax was not to produce
revenues but to stabilize exchange rates (end of BW,
Mundell Incomp Triangle) and reduce finan. volatility

⇒ preserve and strenghten the autonomy of national
monetary policies (Tobin[1978]) : ”monetary policy
becomes, under ﬂoating ER, ER policy”
⇒ reduce the foreign ER and ﬁnancial volatility (Tobin[1974,
1978, 1984, 1996] : short-term round-trip transactions
(speculative ?) are more destabilizing than long-term
transactions (cf ”‘beauty contest”’ of Keynes [1936])
→ tax would reduce volatility by discouraging short
transactions and crowding out speculators and noise
traders from the Forex

However, Tobin’s view has been challenged
1 not all short-term transactions are speculative (hot potato
phenomenon ; liquidity and risk-sharing behavior)
2 indiscriminate tax (liquidity and informative transactions
by fundam traders may be reduced), it would increase the
volatility and be counterproductive
3 a liquidity (but not necessary large ?) market is likely to be
more stable than a tin market (Davidson [1997], Bianconi et
al. [2009])

Still a very controversial project
A very controversial project...
Cameron (ex British Prime Minister) : ”And then theres the
proposal for a Financial Transactions Tax... Even to be
considering this at a time when we are struggling to get our
economies growing is quite simply madness”, idem in
Sweden and Holland, Estonia ?
Krugman (Nobel Laureate) : ”The economic value of all
this trading is dubious at best. In fact, theres considerable
evidence suggesting that too much trading is going on it
suggests that to the extent that taxing ﬁnancial
transactions reduces the volume of wheeling and dealing,
that would be a good thing.”

...but a few academic papers until recently to clarify
the effects of a FTT on finacial activity, macroeconomic
volatility
Question : What would be the effects of a TT ? Will a tax
on financial transactions curb speculative activity and
render financial markets more stable ? Or will it hurt market
liquidity and price discovery, thus, making markets even
more volatile ?
Recently however, a renewed interest for this question
with a new wave of theoretical and empirical papers on
the FTT (often as an equity transaction tax) : Lendvai et al.
(2013, JEDC) ; Berensten et al. (2016, EER), Vives (2017,
RESTUD), Davila (2017, Econometrica ?)

Contribution to the literature
1. Aim of my past research : empirically assess the
impact of a TT on the Forex working
⇒ 1.1 estimate the influence of a TT on trading volume
⇒ 1.2 estimate the influence of a TT on instability (check if
this effect is uniformly distributed across the different Forex
regimes (high appreciation of the Euro, low volume trading,
financial crisis etc) ?
⇒ I will only focus here on the results of my Macroeconomic
Dynamics paper (2016) for time considerations and on
the Forex (seminal project from Tobin, scarcity but the
same methodology)

2. Project in progress with K. Sobczak (Poznan) and
questions to investigate further
⇒ which impact on ﬁnancial and macroeconomic volatility ?
⇒ optimal tax rate and social welfare ?
⇒ contagion and tax coordination issues ?
⇒ need general equilibrium analysis

Tobin Tax and Forex
1. Impact of a Tobin Tax on the Forex working

Literature Forex
Only two existing empirical studies with Forex data
⇒ decentralized nature of the Forex : and thus difﬁculting to collect
data about trading volumes and prices (bid, ask quotations)
⇒ a TT has never been implemented unlike equity and securities
transactions taxes
Aliber et al. [2003] : futures data (CME), datastream, 1
January 1977 to 31 December 1999
⇒ an increase of 0.02 percent in spreads following the introduction
of the TT leads to an increase in volatility of 0.5 percent points
Lanne and Vesala [2010] : Olsen and Associates data,
DM-Dollar and Yen-Dollar, 1 October 1992 to 30 September
1993 : intraday and daily regressions lead to same conclusions

Shortcomings
⇒ Aliber et al. used aggreagated futures data but there is no link
between spots and futures on the Forex (Hartmann [1998])
⇒ Only 3% of total Forex turnover, centralized futures less liquid
⇒ Evidence that changes in transaction costs cause reductions in
volatility and increasing volume and liquidity. But the direction
causality may be the reverse (reductions in volatility lead to
higher liquidity and lower trading costs). Measure of fundamental
volatility (news).
⇒ Data set from Lanne and Vesala is obsolete (data from
1992-93), only linear framework

A new Framework
Concentrate on the volatility/volume instead volatility/spread
(endogeneity bias because of two-way causality)
Volume is weakly exogenous, better understand of the
transmission chanels running from the TT to volatility
Assumption refering to Jeanne and Rose [2002], Haberer
[2004] : the TT effect is conditional on the size and depth of the
market as well as on the structure of the trading volume (NL)
Tobintax → Volume → Volatility
volume = α1 + α2spread + Z1,t + 1,t
Damette[2008,2016]
volatility = β1 + β2volume + Z2,t + 2,t
MDH

TT and trading activity
Transaction costs (spread) used as a proxy of a TT to assess the
impact of a TT on the trading volume
Estimation with daily data from Olsen and Associates data :
signiﬁcant but smaller reduction of the trading volume about 15 to 40%
(ARIMA, GARCH, 3SLS-GMM system)
System to take into account potential endogeneity issues in the
estimates, in line with the seminal paper from Demos and Goodhart
(1996)
Some robustness checks (speciﬁcations, instruments) : elasticity is
between -0.15 and -0.40 for the Euro/Dol over 2008-2010 with daily
data (2006-2012 robustness)
Consistent with another paper (Bismans and Damette, 2008, IE) with
actual(not tick by tick) intraday data (original data set from Reuters) on
several major currency pairs (TS and panel cointegration) : -0.3 to -0.6




ln(volume)t = β1 + β2 ∗ ln(spread)t−k + β3 ∗ ln(volume)t−k + β4 ∗ ln(voly)t−k + 1
ln(voly)t = β5 + β6 ∗ ln(spread)t−k + β7 ∗ ln(volume)t−k + β8 ∗ ln(voly)t−k + 2
ln(spread)t = β9 + β10 ∗ ln(spread)t−k + β11 ∗ ln(volume)t−k + β12 ∗ ln(voly)t−k + 3
(1)

The volume of the market would decline by 24% if a tax level would lead
to an increase of 100% of the transactions costs (spreads) actually at
work on the market (tax of few basis points 0.02% for instance)
⇒ Implications
Lead to a better evaluation of the potential revenues of a TT since
elasticity of the volume to the tax give us the tax base
Lead to a better understanding of the impact of a TT on instability
since volume and volatility are linked (2d question of this seminar)

Tobin Tax and Volatility
Question tackled : is the market speculative and with irrational
exuberance (Shiller) or is the market efﬁcient (Fama) ?
Is it the sign that the market is too volatile, characterized by lot of
high trading frequence, speculative motivations in line with Tobin
and Keynes ? Or not ?
Is a FTT suitable to reduce the ﬁnancial instability and the
probability of a future crisis ?
We try to assess this question by linking reduction of trading
volume and volatility via the MDH hypothesis

A wide range of theoretical studies
Wide range of theoretical studies
⇒ heterogeneous agent models based on market microstructure :
coexistence of fundamental/chartist and informed traders (Frankel and Froot
[1990], Jeanne and Rose [2002]) and (Frankel [1996], Shi and Xu [2009])
⇒ simulation models of intercative agents (Ehrenstein et al. [2005],
Westerhoff and Dieci [2006], Pellizzari and Westerhoff [2009] through the use
of ”‘zero intelligence”’ models
⇒ laboratory experiments (Bloomﬁeld et al. [2009], Hanke et al. [2010])
⇒ minority games (Bianconi et al. [2009])

Heterogeneous agent models (Jeanne and Rose [2002], Shi and
Xu [2009] by introducing a Tobin Tax)
⇒ Noise and informed traders : the volatility is driven by the
proportion of noise who use technical analysis or have
misinformation
⇒ Benchmark model interesting because it mixes elements from the
macro theory of ER and the noise trading approach by modelling
traders’ heterogeneous expectations and micro optimization portfolio
⇒ ER volatility as a function of fundamental (macro from informed
traders) and excess volatiltiy (noise traders)
Salient output from their model
⇒ Derivation of 3 possible equilibria with low to high entry by noise
and thus low to high excess volatility
⇒ Different levels of aggregate volatility for the same levels of
fundamental volatility : multiple equilibria (traders have two
counteracting roles), Tobin Tax ?

Introducing NL in the Volatility/Volume link
Threshold and non linear effects have been neglected by
existing studies
⇒ Jeanne and Rose [2002] and Shi and Xu [2009] prove analytically the
existence of a U shape between volume and volatility
⇒ Simulation models and laboratory experiments : the TT effect is
dependent on the market size, see also Haberer [2004]
Mixture distribution may occur only above or below certain
levels of volume activity (and volatility)
⇒ Olsen Financial Technologies data, 1 October 2006 to 31 October
2010
volt =
t=1
T [log(Rt /Rt−1)]2

A Smooth Transition Regression
Mixture distribution may occur only above or below certain levels of
volume activity (and liquidity)
⇒ STR modeling, Ter¨asvirta [1994, 1998], Franses and Van Dijk
[2000], Ter¨asvirta and Franses [2002]
⇒ STR-GMM, Areosa et al. (2011), Brueggemann and Riedel (2011) :
changing set of instruments, initial condiitions but qualitative
properties remained consistent
⇒ volt = β1zt + β2zt G(γ, c, st ) + ut zt = (wt , xt ) , wt = (1, yt−1, ..., yt−p)
G(γ, c, st ) = 1 + exp −γ
K
k=1(st − ck
−1

Results
Main results 1
⇒ The MDH is stronger in periods of high trading volume (evidence
of NL)
⇒ The semi-elasticity of the trading volume increases from 0.506 to
1.732 (increasing role of the information on the volatility
dynamics) when the lagged trading volume exceeds an
estimated threshold of 12.41
⇒ Exploring the historical trading volumes data and comparing
them to threshold values of the transition variable : the non linear
regime (strenghting relation) occured at the November to
December 2008 period (ensuing from the Black October)
⇒ Also applies in the last part of the sample (April to October 2010)

Main results 2
⇒ Automn 2008 was a period with very high Forex volume and
activity
⇒ A calm period from the end 2008 to the spring 2010, slowing
down of the Forex with smaller trading volume and a narrower
volatility volume correlation
⇒ A ﬁnal non monotonic period (April to October 2010)
⇒ Results in constrast with Galati [2000] : positive during normal
times but negative in stress periods (emerging)
⇒ Our results need to be qualiﬁed : why MDH is increasing in high
trading and volatility periods ?

Main results 3
⇒ The ﬁrst period, spreads were particularly high, high volume and
volatility, crisis period (gloomy situation in the wake of the Black
October) : News about US depression, European recession,
ECB decisions about interest rates and recovery plans...
⇒ In crisis period, each trader want to get out its postions, leading
to large volume
⇒ The second period of increasing MDH seems to be of different
nature : volume was unnaturally high and spreads remained
moderate
⇒ The surge in the volume (May-August) 2010 : large deprectiation
of the Euro caused by the eruption of the sovereign debt crisis
(wave of speculation against the Euro)
⇒ The last period is somewhat different : optimistic expectations
and high-risk positions regarding the US recovery

Policy implications
A tax would be on average an effective instrument for reducing
Forex volatility (MDH) in contrast with the two previous empirical
studies but in accordance with lot of theoretical papers.
All other things beign equal (with constant elasticity), a tax would
have a higher marginal effect for reducing volatility when the
volume and volatility are abnormally high (higher semi-elasticiy in
the non linear regime) as in automn 2008 and spring 2010. Valuable
tool in times of boom trading volume and turbulent period.
Results are consistent with Tobin [1974, 1978] and with the
right-hand part of the U-shape of the Jeanne and Rose (2002)
model : increasing volume is accompanied by an increase in the
number of the noise traders and in the excess volatility.

Limitations and robustness
Assumption : If the proportion of noise traders is constant or
increasing, then as the volume increases, so too the number of noise
traders and the MDH becomes more highly correlated.
Our analysis could be biased if the trading volume become
endogenous in times of crisis (a two-way causality between volume
and volatility)
Whe check it by implementing Granger causality tests on different
sub-samples refering to the linear and non linear parts detected by
the model
Evidence of unidirectionnal causality from trading volume to
volatility only in the second turbulent period (390-515) but no
causality link in the calm period (150-350) and causality from volatility
to volume in the ﬁrst part (1-50)
To go further : a longer period and other data sets and a better
treatment of simultaneity issues (STR-VAR)

Table 7. Granger causality tests (p-values)
Sample Causality p=1 p=2 p=3
1-50 (1st
linear part) Volume Volatility→ 0.6373 0.3537 0.4265
1-50 Volatility Volume→ 0.3937 0.1150 0.0089
150-350 (linear part) Volume Volatility→ 0.3350 0.5798 0.7733
390-515 (2d non linear part) Volume Volatility→ 0.0021 0.0066 0.0209
Note: → denotes no causality for order p.
To test the granger causality from volume to volatility, we consider the following model, for i=Euro/Dollar,
Euro/Yen: ( ) ( )0 , ,
1 1
p p
t j i t j j i t j t
j j
Volatility Volatility p Volume p uφ φ α− −
= =
= + + +∑ ∑
for
1p ≥
.
The null hypothesis is:
0 1: ... 0pH α α= = =
.

Comparison with recent empirical studies about FTT impact on
volatility :
New study using Euronext 100 and 150 from Capelle-Blancard G.
O. Havrylchyk, 2017, The impact of the French securities transaction
tax on market liquidity and volatility, International Review of Financial
Analysis, French and Italian FTT, interesting to conduct some ﬁeld
experiments
France : tax of 0.2% on acquisitions of French equity securities
and similar instruments (companies whose headquarters are located
in France and with market values of more than 1 billion Euros on
January 1st of the year of taxation)
no effect of equity transaction tax on volatility in France or weak
effect...
...but a tax on 109 companies and no tax on high frequency
trading !

Project in progress : a new DSGE model
2. A GE analysis of the FTT effects (in progress, with K. Sobczak)

Project in progress : a new DSGE model
Scarcity of social welfare and general equilibrium analysis to
investigate the global effects of a FTT (not only on Forex but equities,
derivatives...)
Only one DSGE from Lendvai, Raciborski and Vogel (2013, JDEC
and European Commission) but in a closed one country economy
with no tax evasion issues, no tax competition issues and with quite
exogenous financial noise trading
Challenge : a 2-country model to assess financial contagion, tax
evasion and capital flows between two zones (Tobin and not Tobin)

Special features of our framework
A ﬁnancial intermediary/bank/trader that collect deposits from
households and invest these funds into corporate equities
A part of traders are noise traders (heterogeneity)
Noise traders have noisy expectations about shock equity returns
and may deviate from rational expectations (De Long et al., 1990)

In a one country model, the FTT will directly reduce noise trade
through which financial and, indirectly, real volatilities are affected.
But in a 2 country model, what about the contagion of financial
and real volatilities ?
In a one country, an FTT will increase the capital cost of the firms,
reduce physical investment, GDP but also financial volatility. The
financial volatility decreasing has little impact on the volatility of
real variables but in a global model, we expect higger effect of
FTT on volatilities
Indeed, we can introduce some contagions from noise traders
expectations/shocks in one country to expectations from noise
traders in another country (see equations)
In addition we endogeneize the part of noise traders by introducing
an utility threshold (to enter ou not in the equity market) ; that would
amplify boom-bust behaviour and dynamics

Equity market demand(sum of portfolios) equal to supply :
nt SNt + (1 − nt )SIt = St
Trader maximizes its discounted wealth value :
MaxSj
t ,SFj
t
Ej
t β δt+1
δt
NWj
t+1
Wealth’s intermediary dynamics over time with FTT :
NWt+1 = (PS
t+1 + Divt+1)St + (et+1PFS
t+1 + et+1DivF
t+1)SF
− Rt Ft −
τFTT
f(Pt St − et (1 − s∗
n) − et τFTT
f(PFS
t SF
t )
Balance of the j intermediary : PS
t Sj
t + et PFS
t SFj
t = NWj
t
Noise traders expectations of future discounted
returns :domestic equities
EN
t β δt+1
δt
[RS
t+1] = EN
t β δt+1
δt
[RS
t+1evR
t ]
Idem for foreign equities
EN
t β δt+1
δt
[et+1RS
t+1] = EN
t β δt+1
δt
[et+1eve
t RFS
t+1evRF
t ]

Optimal portfolio of intermediaries :
SI
t =
EI
t RS
t+1
2τFTT NPS
t
+ S
F
SFI
t =
EI
t et+1RFS
t+1
2et τFTT NPFS
t
+ S
SN
t =
EN
t RS
t+1
2τFTT NPS
t
+ S
SFN
t =
EN
t et+1RFS
t+1
2et τFTT NPFS
t
+ S
F
Sum of portfolios in endogeneous case :
nt (SN
t + SFN
t )
Noise
+ (1 − nt )(SI
t + SFI
t )
Informed
= St + SF
t
DomE+ForeignE

Endogeneity of noise intermediaries :
ci entry cost paid only paid by noise ones (like in Xu’s paper)
c∗
t : cut-off value of entry cost depends on how far are noise
expectations from rational and noise portfolios from average ones
c∗
t increases if noise portfolio is far from average and bigger
c∗
t decreases if noise portfolio is far from average and smaller
26 equations per country (+4 in endog. case), steady state and
computations

Expected results
In the way of the HOS model in international economics in which
through traded goods, productions factors are also implicitly traded
across countries, we expect that through international equities ﬂows,
some noise traders can move from one country to another one
and change the effects of noise trading and so the effects of a
FTT on volatility across countries
For example, a noise trader in France might move in Estonia and can
increase volatility in Estonia and thus instability
Finally, in a 2 country DSGE considering traders ﬂows and contagion
effects, we can investigate in which conditions, it would be
interesting to stay out the taxed zone or not
In the short run, it could be interesting for a country to be out the
FTT zone but maybe in the long run, the FTT could dampen the
volatility in the taxed zone and increase it in the other zones (like in
UK and Estonia)

Conclusion
Tobin Tax or FTT is not a so bad idea...
Generate revenues, decrease the noise trading behaviour in
ﬁnancial markets and no or small positive effect on volatility
But social welfare, optimal taxation and coordination issues
need to be investigated

Thank you for your invitation and your attention

Taxing the Financial Transactions in Europe : new empirical investigations

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