Master's thesis at HEC on the pass though effects from oil and gas price shocks to GDP growth and equity returns in Central and Eastern Europe

1. ESTIMATION OF THE IMPACT OF OIL AND GAS PRICE SHOCKS
ON GDP GROWTH AND STOCK RETURNS
IN CENTRAL AND EASTERN EUROPE
ALEXEY IVASHCHENKO
MS candidate
HEC PARIS
SEP. 25TH

2. OUTLINE
I.
Rationale
II.
Methodology
III.
Data and estimation approach
IV. Results
V.
Further steps
2

3. WHY IS IT IMPORTANT?
 Fuel prices are volatile
Since 1999 they have been even more volatile than
EM equity asset class
 Energy prices do affect companies’ and
governments’ financials
On corporate level fuel price shocks are transmitted
through expected earnings; on government level
balances of payments are usually affected the most
 If a company or a government isn’t
hedged against energy price shocks, than
financial investor is at risk as well
Annualized volatility of asset classes, %, 1999-2013
25
24,5
20
19,2
15
15,3
10
5
0
Energy
And these are mostly mutual exposures to Russia
I. RATIONALE
DM Equity
Source: MSCI, IMF, author’s calculations
Net import to total primary supply, %, 2012
-38,9
Natural Gas
Hedging opportunities are limited even at the
corporate level; among big national energy traders
only Mexico is hedging oil revenues more or less
successfully
 Central and Eastern Europe (CEE) is
especially exposed to hydrocarbons price
shocks
EM Equity
Russia
Other CEE
86,9
-89,0
Crude Oil
86,8
-100
-75
-50
-25
0
25
50
75
100
Source: IEA, author’s calculations
3

4. MAJOR RELEVANT STUDIES
Oil and economic activity
 Early papers focused on the
pass-through from oil prices per se to
growth (of the U.S. economy), no
consensus achieved
Hamilton (1983), Mork (1989), Raymond and Rich
(1997) etc.
 As a results, some scholars attempted
to find non-linearity in transmission
mechanism
Hamilton (2003)
Oil and stock returns
 As for macroeconomic effects, mostly
transmission from oil price changes to
equity returns was first studied; higher
oil prices were usually associated with
lower stock returns (at least in DM)
Sadorsky (1999), Driesprong, Jacobsen and Maat
(2008) etc.
 This result was often challenged in
country-wise and industry-wise studies
Nandha and Faff (2008), Fayyad and Daly (2011)
 Kilian (2008, 2009), Kilian and Park (2009) proposed new approach to the problem and
attempted to endogenize oil price changes using structural VAR models. This approach
was proved to be successful to study the pass-through from oil shocks to macro activity
and stock returns not only in the U.S.
Apergis and Miller (2009), Basher, Haug and Sadorsky (2012), Wang, Wu and Yang (2013)
II. METHODOLOGY
4

5. KILIAN’S APPROACH
 The corner stone is the global oil market monthly structural VAR model:
j
A0 zt
i 1
Ai zt -i
t
Here 𝑧 𝑡 is a column-vector of three variables: percent change in world crude oil production, index
of global industrial activity and real price of crude oil, 𝛼 is a 31 vector of regression constants, 𝐴 𝑖 are matrices
of regression coefficients and 𝜀 𝑡 is is an i.i.d. 31 error term
 This model is identified as follows:
zt
A0
1
A0
j
1
i 1
Ai zt -i
e1 t global oil production
et
a11
0
0
global
e2t real activity
real
e3t price of oil
a21
a31
a22
a32
0
a33
et
oil supply shock
1t
aggregate demand shock
2t
oil specific demand shock
3t
 Pass-through from identified (quarterly averaged) structural oil-related shocks to GDP
growth is estimated by fixed distributed lag models with growth being dependent
variable and oil shocks being predictors. pass-through to stock returns is estimated by
the same SVAR as before but expanded with the forth equation for equity returns
II. METHODOLOGY
5

6. NATURAL GAS INSTEAD OF CRUDE OIL – WHY NOT?
 Gas is an important fuel in CEE
In 9 out of 12 considered countries the share of
natural gas in TPES is higher than that of crude oil
 Gas prices in Europe are pretty volatile
albeit oil prices fluctuate more
 To adapt Kilian’s approach to gas one
needs to construct monthly European gas
supply and economic activity series
Annualized monthly price volatility, %, 1999-2013
30
28,9
25
Share of fuels in total TPES*, %, 2011
Ukraine
9,8
Turkey
37,3
27,7
Slovenia
19,9
Russia
18,9
15
10
Romania
Poland
0
Crude oil, average
Natural gas, Europe
Source: World Bank Pink Sheets, author’s calculations
II. METHODOLOGY
27,3
54,5
24,1
25,4
Lithuania
31,0
12,5
34,1
Latvia
Hungary
37,6
26,6
25,0
8,7 9,1
Czech
5
10,2
19,9
Estonia
20
32,2
35,3
Slovakia
Oil
Gas
30,4
37,3
20,4
0
16,8
20
40
60
80
Source: IEA, author’s calculations
* TPES – total primary energy supply
6

7. DATA INPUT
 Three-dimension global oil model:
Global monthly oil production (EIA data, m-o-m % growth rates)
Global real activity index (constructed by Kilian, available from his web page)
Based on international freight rates, % deviation from long-term trend, 1st difference used in VARs
Real price of oil (WB data, average of three sorts, US CPI deflated, m-o-m rates)
 Three-dimension European gas model:
OECD Europe natural gas net supply (Eurostat and IEA data, m-o-m rates)
Net supply is production plus import net of export, bunkers and stock changes
European real activity index (see next slide for details)
Based on electricity consumption, % deviation from long-term trend, 1st difference used in VARs
Real price of gas (WB data, average European import price, m-o-m rates)
 Distributed lag models for GDP growth (in addition to structural shocks):
Quarterly real GDP growth rates (WB data in national currencies, q-o-q rates)
 Four-dimension SVARs for stock returns (in addition to 3-dimension models):
MSCI USD Total Return indices for Czech, Estonia, Hungary, Poland, Russia and Turkey,
OMX USD Total Return indices for Lithuania and Latvia, local USD price return indices
for other markets (m-o-m % growth rates for all)
III. DATA AND ESTIMATION APPROACH
7

8. HOW TO CONSTRUCT MONTHLY ACTIVITY INDEX?
% to
HP
% to
linear
Industrial production
0.181
0.337
0.329
0.268
External trade
0.143
0.217
0.278
0.464
OECD electricity cons.
0.144
0.265
0.157
0.638
GDP (quarterly data)
0.015
0.178
0.323
0.570
Source: Lutz Kilian, CPB, WB, EA, ENTSOE, author’s calculations
Kilian’s index and electricity consumption, 2001-2013
60
6
40
4
20
2
0
0
-20
-4
2013
2012
2011
2010
2009
2008
2007
2006
-40
-60
-2
Kilian's index, lhs
OECD electricity consumption, rhs
2005
 So, OECD Europe electricity consumption
(% to trend) was used as European
monthly real activity index in gas model
%
yoy
2004
 OECD electricity consumption (%
deviation from linear trend) turned out
to be strongly correlated with Kilian’s
index!
Indicator
%
mom
2003
 Global industrial production and global
trade in different metrics were tried but
the results were unsatisfactory
Metrics
2002
 Idea: find a good monthly proxy for
Kilian’s global activity index, but one that
can be easily constructed for Europe
Correlation of different global monthly indicators with
Kilian real activity index, 2001-2013
2001
 Kilian’s index, originally constructed for
the global economy using freight rates,
can’t be adapted for Europe
-6
Source: Lutz Kilian, IEA, ENTSOE, author’s calculations
III. DATA AND ESTIMATION APPROACH
8

9. NOTES ON ESTIMATION PROCEDURES
 Sample period: Feb’1997 – Mar’2013 (all inputs were seasonally adjusted)
 Consistency of time-series properties: all time series used in VAR models were I(0) at 1%
confidence level except for activity indices – for them 1st differences were used
 Estimation of SVARs: EViews 7.0 was used, confidence intervals (CI) for impulseresponse functions (IRFs) were computed using built-in analytical approach
 Lag specification in SVARs: 24 months by default (as in Kilian’s original model), if
estimated model was not stable, lag was reduced to 12 or (if again not stable) to 6
months (only gas models for Slovenia and Romania, and oil model for Romania)
 Lag specification in FDLs for GDP growth: 8 quarters to correspond with lag in SVARs
 IRFs of real GDP to oil- and gas-related shocks: since FDLs (estimated by simple OLS)
were run on growth rates, accumulated IRFs (with CI) were computed using simple test
for linear combination of regression coefficients w ˆ , but with enhanced Newey-West
heteroskedasticity and autocorrelation consistent coefficients covariance matrix:
w ˆ
t
III. DATA AND ESTIMATION APPROACH
T 10,
ˆ
w VNW ˆ w
1
2
2
9

10. THREE-DIMENSION FUEL MARKET MODELS
Global oil model:
responses of real oil price
European gas model:
responses of real gas price
Supply shock
.15
.10
.05
.00
-.05
-.10
5
10
Supply shock
15
.15
.10
.05
.00
-.05
-.10
Aggregate demand shock
.15
.10
.05
.00
-.05
-.10
5
10
15
5
10
15
.15
.10
.05
.00
-.05
-.10
15
5
10
15
Gas-specific demand shock
.15
.10
.05
.00
-.05
-.10
5
One s.d. shocks, months on horizontal axis
IV. RESULTS
10
Aggregate demand shock
Oil-specific demand shock
.15
.10
.05
.00
-.05
-.10
5
10
15
 Kilian’s oil model endogenizes real
oil price changes: supply shocks are
irrelevant for oil price dynamics
while aggregate demand and
precautionary oil demand shocks
do matter on 6-9 months horizon
 European gas model demonstrates
comparable effects, but their
duration is different: aggregate
demand shocks significantly impact
real gas prices on 12+ months
horizon while gas specific shocks
are more short-lived
 Both on global oil markets and
European gas markets price shocks
are mostly demand-driven
phenomena, but different demand
shocks shouldn’t be treated equally
10

11. GLOBAL OIL TO GDP PASS-THROUGH
Cumulative real GDP changes 4 quarters after the
global aggregate demand shock, %
CZ
EE
HU
LV
LT
PL
RO
RU
SK
SI
TR
UA
Central
tendency
1,34
1,86
0,68
1,69
1,42
0,81
1,14
1,45
1,18
1,40
0,85
2,10
Lower 95%
confidence
0,65
-0,42
-0,31
-0,48
-0,42
0,52
0,24
0,23
0,23
0,26
-0,55
0,20
Upper 95%
confidence
2,01
4,13
1,67
3,86
3,27
1,09
2,04
2,67
2,14
2,54
2,24
3,99
Czech Republic:
GDP responses
Poland:
GDP responses
Aggregate demand shock
2,5
2,0
1,5
1,0
0,5
0,0
-0,5
0123456789
Aggregate demand shock
2,5
2,0
1,5
1,0
0,5
0,0
-0,5
0123456789
Oil-specific demand shock
1,5
1,0
0,5
0,0
-0,5
-1,0
-1,5
-2,0
0123456789
Oil-specific demand shock
1,5
1,0
0,5
0,0
-0,5
-1,0
-1,5
-2,0
0123456789
One s.d. shocks, quarters on horizontal axis
 Russia, the Ukraine, Slovenia and Czech Republic are among economies which react on
global aggregate demand shocks the most, while Poland shows the tightest CIs of IRFs
 Positive global aggregate demand shocks are clearly GDP-increasing ones (if significant),
but oil-specific demand shocks tend to suppress growth over longer terms
IV. RESULTS
11

12. EUROPEAN GAS TO GDP PASS-THROUGH
Cumulative real GDP changes 4 quarters after the
European aggregate demand shock, %
CZ
EE
HU
LV
LT
PL
RO
RU
SK
SI
TR
UA
Central
tendency
1,56
2,21
1,48
2,61
2,24
0,57
1,98
1,72
1,31
1,82
0,87
2,45
Lower 95%
confidence
0,89
-0,46
0,63
-0,10
-0,01
0,11
0,84
-0,02
0,18
0,67
-1,14
-0,24
Upper 95%
confidence
2,24
4,87
2,33
5,35
4,49
1,04
3,12
3,46
2,44
2,97
2,87
5,15
Hungary:
GDP responses
Poland:
GDP responses
Aggregate demand shock
Aggregate demand shock
2,5
2,5
2,0
2,0
1,5
1,5
1,0
1,0
0,5
0,5
0,0
0,0
-0,5
-0,5
01234567 89
01234567 89
Gas-specific demand shock
1,5
1,0
0,5
0,0
-0,5
-1,0
-1,5
-2,0
0123456789
Gas-specific demand shock
1,5
1,0
0,5
0,0
-0,5
-1,0
-1,5
-2,0
0123456789
One s.d. shocks, quarters on horizontal axis
 Slovenia and Czech Republic are again in the list of countries the most exposed to
European aggregate demand shocks, this time accompanied by Romania
 Positive gas-specific demand shocks provide negative impact on GDP growth in some
countries (see Hungary) over longer terms unlike aggregate demand shocks
IV. RESULTS
12

13. GLOBAL OIL TO STOCK RETURNS PASS-THROUGH
Russia: stock returns
responses
Ukraine: stock returns
responses
Aggregate demand shock
Aggregate demand shock
20
10
0
-10
-20
5
10
15
30
20
10
0
-10
-20
-30
Oil-specific demand shock
20
10
0
-10
-20
5
10
Poland: stock returns
responses
Aggregate demand shock
Aggregate demand shock
10
5
15
0
-5
10
8
0
5
16
-8
-10
Oil-specific demand shock
30
20
10
0
-10
-20
15 -30
Romania: stock returns
responses
5
10
15
-16
Oil-specific demand shock
10
0
-5
15
15
8
0
10
10
16
5
5
5
Oil-specific demand shock
-8
-10
5
10
15
-16
5
10
15
 Kilian’s global oil model doesn’t give satisfactory results in estimation of the impact of
oil-related shocks on stock returns in CEE
 Effects revealed in oil-to-GDP pass-through estimation seem to be less regular for stock
returns. Oil-specific demand shocks imply higher stock returns over first couple of
months in some countries while negative longer-term impact was found only in the
Ukraine
IV. RESULTS
13

14. EUROPEAN GAS TO STOCK RETURNS PASS-THROUGH
USD stock returns over 6 months after the European
aggregate demand shock, %
CZ
EE
HU
LV
LT
PL
RO
RU
SK
SI
TR
UA
Central
tendency
4,31
5,13
9,05
4,91
4,48
8,30
7,18
8,36
3,82
3,85
5,87
7,32
-2 s.e.
+ 2 s.e.
0,91
-0,69
2,49
0,81
-1,02
1,56
-1,24
1,26
-0,66
-2,37
-2,37
0,70
7,71
10,90
15,60
9,01
9,98
15,00
15,60
15,50
8,30
10,10
14,10
13,90
Russia: stock returns
responses
Hungary: stock returns
responses
Aggregate demand shock
30
20
10
0
-10
-20
-30
Aggregate demand shock
20
10
0
-10
-20
5
10
15
-30
Gas-specific demand shock
30
20
10
0
-10
-20
-30
5
10
15
Gas-specific demand shock
20
10
0
-10
-20
5
10
15
-30
5
10
15
One s.d. shocks, months on horizontal axis
 European aggregate demand shocks impact stock returns significantly in major CEE
equity markets (apart from Turkey), but Polish market is more responsive to global ones
 Gas model clearly shows the importance of distinguishing between different demandrelated shocks. Aggregate demand shocks imply higher stock returns over shorter
terms, but gas-specific shocks result in significantly negative returns over longer terms
IV. RESULTS
14

15. STOCK RETURNS VARIANCE DECOMPOSITION
Historical stock returns variance decomposition based on gas model
Hungary
13,3
Russia
11,0
Poland
12,3
Estonia
11,5
Turkey
Latvia
19,5
13,2
11,8
48,8
15,4
49,2
17,1
23,3
14,8
9,8
45,9
17,5
23,1
16,0
6,7
17,1
22,7
13,9
Slovakia
Ukraine
23,7
51,9
10,4
52,4
9,2
60,0
8,8
68,2
13,2
68,3
Slovenia 5,0 6,9
18,1
Romania
5,1 10,4
10,1
74,4
Lithuania
5,8 9,5
10,2
74,5
Czech Rpb.
6,7 8,7
8,9
75,7
0
10 20 30
Gas supply in Europe
Gas-specific demand shock
IV. RESULTS
70,0
40
50 60 70 80 90 100
Aggregate demand in Europe
Other shocks to stock returns
 European gas model is
statistically good enough to
built and analyze a historical
equity returns variance
decomposition
 Three big markets (Hungary,
Russia and Poland) were
found to be driven by gasrelated shocks to a
considerable degree – less
than 50% of USD stock
returns variance comes from
disturbances not related to
gas markets
 Even well-diversified equity
investor in CEE region is
hugely exposed to gas market
shocks which are rarely on
the radar of investors
15

16. SUMMARY OF MAIN FINDINGS
 One can’t make any reasonable conclusions regarding the impact of oil or gas price
shocks on GDP growth and stock returns in CEE without knowing the factors which
stand behind price increases – aggregate and precautionary demand shocks have
different transmission mechanisms. Results for the pass-through from European gasrelated shocks were found to be more pronounced than for oil-related shocks.
 Aggregate European demand shocks imply on average stronger GDP jumps in CEE
economies than global demand shocks (except for Poland). Significant drag on growth
in Turkey, Latvia and the Ukraine resulting from gas-specific demand shock was found.
 Poland stands out as an economy reacting stronger on global either than European
shocks. An investor exposed to the whole CEE region should overweight Poland each
time he anticipates more positive surprises from global either than European demand.
 More than 50% of historical stock returns variance in Russia, Poland and Hungary is due
to gas-related shocks, which is more than in comparable studies for oil-related shocks.
 If a diversified equity investor in CE3 stock markets (Poland, Czech, Hungary) faces a
European gas price jump following an unanticipated expansion of aggregate demand in
Europe, he may expect some profits over the next 6-8 months. But if that gas price
jump was due to gas-specific demand shocks, an investor should be aware of potential
losses over 10-12 months horizon.
IV. RESULTS
16

17. MORE FUELS AND MORE SECTORS
 Replacement of broad equity indices by sector stock market indices in the fourdimension structural VAR models
This will show explicitly how different sectors react on oil and gas-related shocks, which is usually of much interest
for an equity investor
 Expansion of the four-dimension VAR model with another equation for monthly activity
indicator
This will allow to consider local economic activity and stock market reaction on structural shocks jointly
 One can consider building comparable models to study the effects of metals-related or
any other commodity-related shocks
For some CEE countries like the Ukraine, for instance, it definitely makes practical sense
V. FURTHER STEPS
17

18. THANK YOU!
Q&A