Impact of oil and gas price shocks on GDP growth and stock returns in CEE

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

OUTLINE

2

I. Rationale

II. Methodology

III. Data and estimation approach

IV. Results

V. Further steps

WHY IS IT IMPORTANT?

I. RATIONALE 3

24,5

19,2

15,3

0

5

10

15

20

25

Energy EM Equity DM Equity

Annualized volatility of asset classes, %, 1999-2013

Source: MSCI, IMF, author’s calculations

Net import to total primary supply, %, 2012

Source: IEA, author’s calculations

86,8

86,9

-89,0

-38,9

-100 -75 -50 -25 0 25 50 75 100

Crude Oil

Natural GasRussia Other CEE

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

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

And these are mostly mutual exposures to Russia

MAJOR RELEVANT STUDIES

II. METHODOLOGY 4

Oil and economic activity Oil and stock returns

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)

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)

KILIAN’S APPROACH

II. METHODOLOGY 5

The corner stone is the global oil market monthly structural VAR model:

0 -1

j

t i t i ti

A z A z

Here 𝑧𝑡 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 31 vector of regression constants, 𝐴𝑖 are matrices of regression coefficients and 𝜀𝑡 is is an i.i.d. 31 error term

This model is identified as follows:

1 10 0 -

1

1 11 1

2 21 22 2

3 31 32 33

0 0

0

j

t i t i ti

global oil production oil supply shockt t

global real activity aggregate demant t t

real price of oilt

z A A A z e

e a

e e a a

e a a a 3

d shock

oil specific demand shockt

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

NATURAL GAS INSTEAD OF CRUDE OIL – WHY NOT?

II. METHODOLOGY 6

20,4

8,7

25,0

26,6

34,1

25,4

24,1

19,9

19,9

35,3

27,7

9,8

16,8

9,1

37,3

30,4

37,6

12,5

31,0

54,5

27,3

10,2

32,2

37,3

0 20 40 60 80

Czech

Estonia

Hungary

Latvia

Lithuania

Poland

Romania

Russia

Slovakia

Slovenia

Turkey

Ukraine Oil

Gas

Share of fuels in total TPES*, %, 2011

Source: IEA, author’s calculations

* TPES – total primary energy supply

28,9

18,9

0

5

10

15

20

25

30

Crude oil, average Natural gas, Europe

Annualized monthly price volatility, %, 1999-2013

Source: World Bank Pink Sheets, author’s calculations

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

DATA INPUT

III. DATA AND ESTIMATION APPROACH 7

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)

HOW TO CONSTRUCT MONTHLY ACTIVITY INDEX?


Kilian’s index and electricity consumption, 2001-2013

-6

-4

-2

0

2

4

6

-60

-40

-20

0

20

40

60

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

Kilian's index, lhs

OECD electricity consumption, rhs

Source: Lutz Kilian, IEA, ENTSOE, author’s calculations

Metrics Indicator

% mom

% yoy

% 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

Correlation of different global monthly indicators with Kilian real activity index, 2001-2013

Source: Lutz Kilian, CPB, WB, EA, ENTSOE, author’s calculations

Kilian’s index, originally constructed for the global economy using freight rates, can’t be adapted for Europe

Idea: find a good monthly proxy for Kilian’s global activity index, but one that can be easily constructed for Europe

Global industrial production and global trade in different metrics were tried but the results were unsatisfactory

OECD electricity consumption (% deviation from linear trend) turned out to be strongly correlated with Kilian’s index!

So, OECD Europe electricity consumption (% to trend) was used as European monthly real activity index in gas model

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 impulse-response 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 , but with enhanced Newey-West heteroskedasticity and autocorrelation consistent coefficients covariance matrix:

1

2

10,2

ˆ ˆN̂W

Tw t w V w

ˆw

THREE-DIMENSION FUEL MARKET MODELS

IV. RESULTS 10

Global oil model: responses of real oil price

One s.d. shocks, months on horizontal axis

-.10

-.05

.00

.05

.10

.15

5 10 15

Supply shock

-.10

-.05

.00

.05

.10

.15

5 10 15

Aggregate demand shock

-.10

-.05

.00

.05

.10

.15

5 10 15

Oil-specific demand shock

European gas model: responses of real gas price

-.10

-.05

.00

.05

.10

.15

5 10 15

Supply shock

-.10

-.05

.00

.05

.10

.15

5 10 15


-.10

-.05

.00

.05

.10

.15

5 10 15

Gas-specific demand shock

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

GLOBAL OIL TO GDP PASS-THROUGH

IV. RESULTS 11

Central tendency

Lower 95% confidence

Upper 95% confidence

CZ 1,34 0,65 2,01

EE 1,86 -0,42 4,13

HU 0,68 -0,31 1,67

LV 1,69 -0,48 3,86

LT 1,42 -0,42 3,27

PL 0,81 0,52 1,09

RO 1,14 0,24 2,04

RU 1,45 0,23 2,67

SK 1,18 0,23 2,14

SI 1,40 0,26 2,54

TR 0,85 -0,55 2,24

UA 2,10 0,20 3,99

Cumulative real GDP changes 4 quarters after the global aggregate demand shock, %

Czech Republic: GDP responses

-0,50,00,51,01,52,02,5

0 1 2 3 4 5 6 7 8 9



-2,0-1,5-1,0-0,50,00,51,01,5

0 1 2 3 4 5 6 7 8 9

One s.d. shocks, quarters on horizontal axis

Poland: GDP responses

-0,50,00,51,01,52,02,5

0 1 2 3 4 5 6 7 8 9



-2,0-1,5-1,0-0,50,00,51,01,5

0 1 2 3 4 5 6 7 8 9

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

EUROPEAN GAS TO GDP PASS-THROUGH

IV. RESULTS 12

Central tendency

Lower 95% confidence

Upper 95% confidence

CZ 1,56 0,89 2,24

EE 2,21 -0,46 4,87

HU 1,48 0,63 2,33

LV 2,61 -0,10 5,35

LT 2,24 -0,01 4,49

PL 0,57 0,11 1,04

RO 1,98 0,84 3,12

RU 1,72 -0,02 3,46

SK 1,31 0,18 2,44

SI 1,82 0,67 2,97

TR 0,87 -1,14 2,87

UA 2,45 -0,24 5,15

Cumulative real GDP changes 4 quarters after the European aggregate demand shock, %

-0,50,00,51,01,52,02,5

0 1 2 3 4 5 6 7 8 9

One s.d. shocks, quarters on horizontal axis

Hungary: GDP responses



-2,0-1,5-1,0-0,50,00,51,01,5

0 1 2 3 4 5 6 7 8 9

-0,50,00,51,01,52,02,5

0 1 2 3 4 5 6 7 8 9

Poland: GDP responses



-2,0-1,5-1,0-0,50,00,51,01,5

0 1 2 3 4 5 6 7 8 9

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

GLOBAL OIL TO STOCK RETURNS PASS-THROUGH

IV. RESULTS 13

Russia: stock returns responses

Ukraine: stock returns responses

Poland: stock returns responses

Romania: stock returns responses

-20

-10

0

10

20

5 10 15


-20

-10

0

10

20

5 10 15


-30

-20

-10

0

10

20

30

5 10 15


-30

-20

-10

0

10

20

30

5 10 15


-10

-5

0

5

10

5 10 15


-10

-5

0

5

10

5 10 15


-16

-8

0

8

16

5 10 15


-16

-8

0

8

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

EUROPEAN GAS TO STOCK RETURNS PASS-THROUGH

IV. RESULTS 14

Central tendency

-2 s.e. + 2 s.e.

CZ 4,31 0,91 7,71

EE 5,13 -0,69 10,90

HU 9,05 2,49 15,60

LV 4,91 0,81 9,01

LT 4,48 -1,02 9,98

PL 8,30 1,56 15,00

RO 7,18 -1,24 15,60

RU 8,36 1,26 15,50

SK 3,82 -0,66 8,30

SI 3,85 -2,37 10,10

TR 5,87 -2,37 14,10

UA 7,32 0,70 13,90

USD stock returns over 6 months after the European aggregate demand shock, %

One s.d. shocks, months on horizontal axis

Russia: stock returns responses

Hungary: stock returns responses

-30

-20

-10

0

10

20

30

5 10 15


-30

-20

-10

0

10

20

30

5 10 15


-30

-20

-10

0

10

20

5 10 15


-30

-20

-10

0

10

20

5 10 15


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 demand-related shocks. Aggregate demand shocks imply higher stock returns over shorter terms, but gas-specific shocks result in significantly negative returns over longer terms

STOCK RETURNS VARIANCE DECOMPOSITION

IV. RESULTS 15

6,7

5,8

5,1

5,0

6,7

13,2

16,0

13,9

11,5

12,3

11,0

13,3

8,7

9,5

10,4

6,9

11,8

9,8

14,8

23,3

19,5

23,1

22,7

23,7

8,9

10,2

10,1

18,1

13,2

8,8

9,2

10,4

17,1

15,4

17,5

17,1

75,7

74,5

74,4

70,0

68,3

68,2

60,0

52,4

51,9

49,2

48,8

45,9

0 10 20 30 40 50 60 70 80 90 100

Czech Rpb.

Lithuania

Romania

Slovenia

Latvia

Ukraine

Slovakia

Turkey

Estonia

Poland

Russia

Hungary

Gas supply in Europe Aggregate demand in Europe

Gas-specific demand shock Other shocks to stock returns

Historical stock returns variance decomposition based on gas model 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 gas-related 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

SUMMARY OF MAIN FINDINGS

IV. RESULTS 16

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 gas-related 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.

MORE FUELS AND MORE SECTORS

V. FURTHER STEPS 17

Replacement of broad equity indices by sector stock market indices in the four-dimension 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

THANK YOU!

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Impact of oil and gas price shocks on GDP growth and stock returns in CEE