Temperature Dynamics, Volatility, and the UK Demand for Natural Gas
By
Alec John Michael Horton
2010
A Dissertation presented in part consideration for the degree of MA Finance and Investments.
Abstract
The purpose of this paper is to consider how changes in temperature affect the volatility of the
financial markets and overall demand for natural gas as released by the National Grid. Parts 1 to
3 of this paper provide an overview of the gas markets and the literature. Parts 4 and 5 provides
the reader with an in depth analysis into temperature, demand and the financial markets. Part 4
finds a strong relationship between temperature and the demand for natural gas and clear
evidence of seasonality in natural gas demand. Part 5 focuses on volatility and the financial
markets. There is significant evidence that market volatility is greater during the winter months
in comparison to the summer months. The market is also found to be largely inefficient, which is
confirmed when testing the Efficient Market Hypothesis in part 5.1.
Acknowledgements
I would first like to thank my supervisor Dr Monica Giulietti who throughout the preparation of
my dissertation has kept in constant communication and provided me with sound advice. This
dissertation has been completed for Advisory Group AG, I would like to thank all the people at
Advisory Group who helped me obtain my data and have remained in constant communication
throughout this process, and I would also like to thank Advisory Group for several very enjoyable
trips to Zurich throughout the writing of my thesis.
2
CONTENTS
LIST OF FIGURES AND TABLES...........................................................................................................................
LIST OF TERMS ............................................................................................................................................ 5
1 INTRODUCTION......................................................................................................................................... 7
1.1 THEORETICAL VIEW OF THE NATURAL GAS MARKETS ............................................................................... 7
1.12 THE SPOT-FUTURES PARITY AND EFFICIENT MARKET HYPOTHESIS........................................................ 7
1.2 WHAT IS NATURAL GAS? .................................................................................................................... 9
1.3 BACKGROUND ON THE UK NATURAL GAS MARKET ............................................................................... 10
1.31 THE UK IMPORT MARKET ........................................................................................................... 11
1.4 AN OVERVIEW OF UK WEATHER ........................................................................................................ 12
1.41 CLIMATE CHANGE ...................................................................................................................... 14
1.5 WHY ARE THE ENERGY MARKETS SO VOLATILE? ..................................................................................... 14
2 LITERATURE REVIEW................................................................................... ERROR! BOOKMARK NOT DEFINED.
3 GENERAL DATA SUMMARY .......................................................................... ERROR! BOOKMARK NOT DEFINED.
4 THE DEMAND FOR NATURAL GAS AND TEMPERATURE ..................................... ERROR! BOOKMARK NOT DEFINED.
4.1 DATA AND METHODOLOGY ........................................................................................................... 21
4.2 MODEL SPECIFICATION ................................................................................................................. 22
4.3 ESTIMATION RESULTS ................................................................................................................... 22
4.4 NATIONAL GRID DEMAND AND SEASONALITY ................................................................................... 26
5 THE FINANCIAL MARKETS AND TEMPERATURE ................................................ ERROR! BOOKMARK NOT DEFINED.
5.1 TESTING THE EMH....................................................................................................................... 29
5.2 WHICH MARKET IS MORE RESPONSIVE TO CHANGES IN TEMPERATURE? ................................................ 31
5.3 DATA AND METHODOLOGY ........................................................................................................... 31
5.3 ARMA - MODEL SPECIFICATION AND RESULTS ................................................................................. 33
5.4 GARCH - MODEL SPECIFICATION AND RESULTS................................................................................ 36
5.5 T-GARCH - MODEL SPECIFICATION AND RESULTS ............................................................................ 39
5.6 SEASONAL ANALYSIS..................................................................................................................... 40
5.61 MONTHLY GARCH ANALYSIS ...................................................................................................... 40
5.62 SPOT PRICE VOLATILITY AND SEASONALITY ..................................................................................... 41
6 CONCLUSIONS ........................................................................................... ERROR! BOOKMARK NOT DEFINED.
7 BIBLIOGRAPHY........................................................................................................................................ 45
7.1 REFERENCES................................................................................................................................ 45
7.2 APPENDICES................................................................................................................................ 48
3
LIST OF FIGURES AND TABLES
FIGURE 1 – WORLDWIDE MARKETED NATURAL GAS ENERGY CONSUMPTION (QUADRILLION BTU), IEO 2010. ......... 10
FIGURE 2 – NATIONAL DEMAND INDEX VS. LONDON DAILY LOW TEMPERATURE.................................................. 11
FIGURE 3 – FUTURES PRICE VS. BIRMINGHAM DAILY LOW, DECEMBER 2009 – JANUARY 2010. ............................ 15
FIGURE 4 – NATIONAL GRID ESTIMATED DEMAND (MCM), .............................................................................. 29
FIGURE 5 – PRICE BEHAVIOUR AND THE EFFICIENT MARKET HYPOTHESIS, BREALEY ET AL (2008) ........................... 36
FIGURE 6 – HURRICANE’S KATRINA AND RITA, AND THEIR PATH TOWARD THE US MAINLAND, AUGUST-SEPTEMBER
2005. WELLS, 2006 ................................................................................................................................. 54
FIGURE 7 – DAILY NATURAL GAS PRODUCTION FROM THE GULF OF MEXICO FOLLOWING LANDFALLS OF HURRICANES
KATRINA AND RITA. WELLS, 2006 ............................................................................................................... 54
FIGURE 8 – UK LOCAL DISTRIBUTION ZONES, NATIONAL GRID 2009. ................................................................ 55
FIGURE 9 – ACF AND PACF OF SPOT PRICE RETURNS. .................................................................................... 55
TABLE 1 – SUMMARY OF DEMAND, SPOT AND FUTURES DATA. ......................................................................... 19
TABLE 2 – AUGMENTED DICKEY FULLER TEST FOR UNIT ROOTS......................................................................... 23
TABLE 3 – REGRESSION COEFFICIENTS AND N-W S.E. ..................................................................................... 24
TABLE 4 – NATIONAL GRID DEMAND AND SEASONALITY ESTIMATED COEFFICIENTS. ............................................. 27
TABLE 5 – EVIDENCE OF SEASONAL DEMAND FOR NATURAL GAS. ..................................................................... 28
TABLE 6 – SPOT AND FUTURES SUMMARY STATISTICS. .................................................................................... 30
TABLE 7 – ADF, JAN 2006 – MAY 2010. ..................................................................................................... 33
TABLE 8 – PORTMANTEAU TEST FOR WHITE NOISE, JAN 2006 – MAY 2010. ..................................................... 33
TABLE 9 – AKAINE’S INFORMATION CRITERION – AR COMPONENT. ................................................................... 34
TABLE 10 - AKAINE’S INFORMATION CRITERION – MA COMPONENT. ................................................................ 34
TABLE 11 – AKAINE’S INFORMATION CRITERION - ARMA (P,Q) ....................................................................... 35
TABLE 12 – TEST FOR ARCH EFFECTS ........................................................................................................... 37
TABLE 13 – AIC FOR GARCH (P,Q) MODELS.................................................................................................. 38
TABLE 14 – SPOT RETURNS, NOTE THAT (Α1 + Β1=0.983<1). ........................................................................... 38
TABLE 15 - SPOT RETURNS, T-GARCH, NOTE THAT (Α1 - Γ +Β1=0.9502<1). ...................................................... 40
TABLE 16 – MONTHLY DUMMY VARIABLE GARCH (1,1) ANALYSIS. ................................................................. 41
TABLE 17 – SPOT VOLATILITY VS. WSD AND SEASONS. ................................................................................... 42
TABLE 18– GAS SALES AND NUMBERS OF CUSTOMERS AT REGIONAL AND LOCAL AUTHORITY LEVEL, 2007 ............... 48
TABLE 19 - LOCATION OF MEASUREMENT STATIONS AND VARIABLE NAMES, BLOOMBERG (2010). ......................... 49
TABLE 20 - LIST OF WEATHER VARIABLES AND DEFINITIONS. ............................................................................. 49
TABLE 21– LONDON SUMMARY STATISTICS. .................................................................................................. 49
TABLE 22– BIRMINGHAM SUMMARY STATISTICS. ........................................................................................... 50
TABLE 23 –GLASGOW SUMMARY STATISTICS. ................................................................................................ 50
TABLE 24– SUNDERLAND SUMMARY STATISTICS. ........................................................................................... 50
TABLE 25- MANCHESTER SUMMARY STATISTICS. ............................................................................................ 50
TABLE 26– NOTTINGHAM SUMMARY STATISTICS. ........................................................................................... 50
TABLE 27– CARDIFF SUMMARY STATISTICS. ................................................................................................... 51
TABLE 28– SOUTHEND SUMMARY STATISTICS. ............................................................................................... 51
4
TABLE 29– BRIGHTON SUMMARY STATISTICS................................................................................................. 51
TABLE 30- BRISTOL SUMMARY STATISTICS ..................................................................................................... 51
TABLE 31 – SOUTHAMPTON SUMMARY STATISTICS......................................................................................... 51
TABLE 32– CARLISLE SUMMARY STATISTICS. .................................................................................................. 52
TABLE 33- HEATING DEGREE DAYS AND LOCATION CORRELATIONS. ................................................................... 52
TABLE 34 – TEST RESULTS FOR HETEROSCEDASTICITY FOR INITIAL CLRM. ........................................................... 53
TABLE 35 – DURBIN WATSON TESTS OF SERIAL CORRELATION. ......................................................................... 53
LIST OF TERMS
95% Conf.
95 percent confidence
interval
Aikaike Information
Criterion
Augmented Dickey
Fuller Test
Autocorrelation
Function
Autoregressive
GARCH
(p,q)
GWh
Generalised Autoregressive Conditional
Heteroscedasticity
Gigawatt Hour
HDD
Heating Degree Days
ICE
Intercontinental Exchange
IEO
International Energy Outlook
LDZ
Local Distribution Zones
MMcm
Million Cubic Meters
AUT
Autoregressive
Conditional
Heteroscedasticity
Autoregressive
Integrated Moving
Average
Autumn
MDV
Monthly Dummy Variable
BBL
Balgzand-Bacton-Line
MA (q)
Moving Average
Bcm
Billion Cubic Meters
NBP
National Balancing Point
LM
Breusch Godfrey Test
NTS
National Transmission System
BTU
British Thermal Units
N-W
Newey West
CLRM
Classical Linear
Regression Model
OTC
Over-the-Counter
CLRM
Classical Linear
Regression Model
PACF
Partial Autocorrelation Function
CHV
Composite Heating
Degree Day Variable
GBP
Pound Sterling
CHV
Composite Weather
Variable
Composite Weather
Variable
Cooling Degree Days
R2
R-Squared, measure of model fit
NBP97
SPR
Short Term Flat NBP Trading Terms and
Conditions 1997
Spot Prices (EMH)
Daily Actual National
Grid Demand for
Natural Gas
Daily Average
Temperature
Daily Front Month
NBP Closing Prices
(Futures)
Daily High
Temperature
SPRN
Spring
S.E.
Standard Error
SUM
Summer
UK
The United Kingdom
AIC
ADF
ACF
AR (p)
ARCH (q)
ARIMA (p,q)
CWV
CDD
UGASDEMD
MEAN
NBPG1MON
HIGH
5
MIN
GA1NB
Daily Low
Temperature
Daily Prompt NG
Closing Prices (Spot)
Thm
Therm
TGARCH
(p,q)
DD
Threshold Generalised Autoregressive
Conditional Heteroscedasticity
DTI
Department of Trade
and Industry
DW
Durbin Watson Test
WSJ
Wall Street Journal
EMH
Efficient Market
Hypothesis
Energy Information
Administration
WSD
Weather Surprise Dummy Variable
WSV
Weather Surprise Variable
Futures Prices (EMH)
WINT
Winter
EIA
FPR
6
Total Degree Days, HDD + CDD
The first part of this paper will be devoted to an overview of the Natural Gas markets and key
issues that will provide the foundation for analysis in future sections. Sections two and three will
provide a summary of the literature and obtained data. Parts four and five will provide detailed
analysis of both the level of demand and the financial markets for Natural gas, with a focus on the
temperature and volatility. Within section six are conclusions to the analysis in sections four and
five.
1 INTRODUCTION
1.1 THEORETICAL VIEW OF THE NATURAL GAS MARKETS
When analysing the relationship between temperature and the price of commodities past
literature has generally preferred the use of either Spot or Futures prices. This part of the paper
will briefly discuss the Spot-Futures parity condition and its implications for analysis in further
parts. Fama and French (1987) find that good spot-price data is not available for most
commodities and prefer the use of futures, they state that futures are regulated through organised
regulated exchanges and thus can be assumed to be a true reflection of the market. Spot prices
data is released by reporting agencies such as Bloomberg, Reuters and Platt’s and these prices
often differ across reporting agencies. Mu (2004) verifies Fama and French’s observations and
also prefers the use of futures.
1.12 THE SPOT-FUTURES PARITY AND EFFICIENT MARKET HYPOTHESIS
The theoretically correct relationship between the spot and futures price is known as the SpotFutures Parity, if this relationship fails to hold, arbitrage opportunities arise. There are essentially
two ways to acquire a commodity such as Natural gas. Market participant can purchase the
physical commodity today and store it, or can choose to take a long position in futures, these
two strategies must have the same market determined costs. Commodities are physical goods
and thus have different properties to financial assets, for example, a Natural Gas processing
plant is not purchasing a futures contract to speculate but to consume. In absence of storage
costs, the forward price of a commodity, such as Natural Gas is given by Equation 1. Where F0 is
the Forward Price, S0 the Spot Price, r the risk-free rate of return and T the time period. This
equation must hold to prevent risk free arbitrage profits.
Equation 1
7
If a term ‘u’ is introduced, which represents the present value of all the known storage costs that
will be incurred over the contract period, absorbing funds, it follows Equation 1 that
Equation 2
There are also advantages to owning a physical commodity, if a Natural Gas processing plant is
long futures and there is some unforeseen shock, such as an extremely cold winter, this will
cause an increase in demand for gas, but, the plant cannot convert the futures contracts into
physical delivery before contract maturity. The advantage to the plant of holding the physical
commodity is difficult to quantify, but Hull (2002) prefers the term ‘convenience yield’, denoted
by y and shown in Equation 3.
Equation 3
The convenience yield essentially represents market expectations in regards to the future
availability of the commodity. The greater the likelihood of shortages for example, the higher
the convenience yield. The difference between the futures price and the spot price is called the
‘Basis’, overtime the basis will be volatile but eventually converge. If today’s Futures price is
equal to the expected spot price at maturity then;
Equation 4
Over an extended time period, in rational markets, expectations about futures spot price will
adjust upward as often as downward. Telser (1958) finds that futures prices display no trend as
they approach maturity and accepts the hypothesis that the futures price equals the expected
spot price, Gray (1961) verified Telser’s findings and Dusak (1973) also supports Equation 4.
The spot-futures parity condition is consistent with the Efficient Market Hypothesis (EMH)
(Fama, 1970) which states that the financial markets are informationally efficient. In an efficient
market, new information is reflected instantly in commodity prices, which implies that the futures
price is the optimal forecast of the spot price. No other topic has produced as many articles as
the EMH in the area of finance1. The spot futures parity condition is based on the assumption
that market participants are able to trade in the spot and futures markets at the same time, i.e.
Malkiel (1973) famously stated that a blindfolded chimpanzee throwing darts at the WSJ could select a
portfolio that would so as well as the experts.
1
8
traders can utilize any spot/futures price differentials. If the EMH holds then price patterns are
random and no system based on past market behaviour can earn excess returns.
Walls (1995) founds that the spot price of natural gas was co integrated with the futures price,
that each price conveys the same information about the present and expected underlying value,
that is, the markets are efficient and Shawky (2002) found that many of the characteristics of
the electricity market can be viewed to be broadly consistent with efficient markets.
However, Chang (1985) found that ‘large wheat speculators’ possessed some superior
forecasting ability and provides statistical evidence that is inconsistent with the hypothesis that
commodity futures prices are unbiased estimates of the corresponding future spot prices,
Houthakker (1957) also finds evidence of definite forecasting skill. In terms of the Natural Gas
markets, Herbert (1993) was first to look at markets for US Natural Gas futures and found
inefficiency in the market. Chinn et al (2005) found that futures prices were unbiased predictors
of future spot prices, with the exception those in the natural gas markets at the 3-month
horizon, and Mazighi (2003) rejects the hypothesis of efficiency in the futures markets for
natural gas and concludes that forward prices are far from being optimal predictors of spot
prices.
1.2 WHAT IS NATURAL GAS?
Natural gas is a colourless, odourless and shapeless fossil fuel found underground that is
generated through the slow decomposition of ancient organic matter. This gas is generally found
trapped in pockets of porous rock which is supported by impermeable rock, although natural gas
is also found within oil reservoirs (Associated Natural Gas) or coal deposits (Coal-Bed methane).
Natural gas is extracted through the use of wells drilled into the porous rock and is largely
composed of methane, all other by-products must be removed at a processing plant before being
moved through pipelines to the end consumer.
Natural gas is highly combustible and emits a great deal of energy when burned. Once delivered to
homes it is used for a range of purposes, although in the UK it is primarily used to power central
heating systems, boilers and gas powered ovens and increasingly Natural gas is being used to
generate electricity. Consumers require space conditioning to create a comfortable living and
working environment, electricity drives devices such as fans, air conditioners, chillers, cooling
towers and electric boilers (Gellings, 2009) and energy use in buildings accounts for 53 percent
of total electricity use (Harvey, 2010). Figure 1 illustrates that since 1990 Natural gas
consumption has increased from 75.4 quadrillion Btu in 1990 to an estimated 162.3 quadrillion
9
Btu in 2035 (International Energy Outlook 2010) and Harvey (2010) states that there are
enough Natural gas reserves to last for 80-217 years depending on supply and demand
approximations.
Figure 1 – Worldwide marketed Natural gas Energy Consumption (quadrillion Btu), IEO 2010.
1.3 BACKGROUND ON THE UK NATURAL GAS MARKET
In the early 1980’s the UK gas industry began to liberalise and restructure2, which began with
the privatisation of British Gas in 1986 and the ‘demerger’ of its activities in 1991. Prior to the
liberalisation of Britain’s energy markets British Gas and 14 regional public electricity suppliers
had a monopoly to supply gas and electricity to every domestic energy consumer. Today the
market is very competitive and the demand for Natural gas in mainland UK is categorized
between Local Distribution Zones (LDZs), of which there are thirteen in the National
Transmission System (NTS). Ofgas was created to ensure a smooth transition from a vertically
integrated state owned monopolistic market to a competitive market in which consumer
interests were protected.
The UK is one of the ‘big six’ major European gas markets along with Germany, Italy, France,
Netherlands and Spain. The UK market is the largest volume market and is completely
liberalised, because of this the UK market is also the most active, competitive and volatile gas
trading market in Europe. Approximately 40% of the UK’s primary energy comes from gas, and
there are large summer/winter swings due to ‘central heating’ demand, this is shown in Figure
2, which clearly shows that when temperatures are at their lowest, the demand for natural gas
is highest and vice versa.
The 1982 Oil and Gas Act gave the government the power to dispose of British Gas assets and open up
pipelines to the market.
2
10
Figure 2 – National Demand Index vs. London Daily Low Temperature
The majority of gas in Europe is priced on a long-term contract, and a per country basis, shortterm fluctuations tend to be due to traders trading out daily imbalances, these gas prices are
based on ‘market values’ and competition at the National Balancing Point (NBP). In the UK
market, the majority of demand for natural gas is during the winter months, summers are
usually the lowest demand periods. However, abnormally hot periods can cause an increase in
demand, as consumers demand electricity to cool their homes, this is usually found to be the
case in the US market. Recent Natural Gas market volatility and the changeover of the UK from a
net exporter to a net importer mean that security of supply is also a top priority for the UK.
1.31 THE UK IMPORT MARKET
The UK has historically been a net exporter of Natural Gas, however recently is became a net
importer. In 2008 natural gas production was 70 Billion cubic meters (Bcm) and consumption
was 96Bcm (CIA World Fact Book, 2010). The UK is home to the most developed and liquid hub
in Europe, the NBP, which started trading in 1996. The NBP is a virtual trading hub which
covers the whole British transmission grid, it is a notional point which does not have an
identifiable physical location. It is the trading point of UK short-term natural gas and is key to
the price that domestic consumers pay and unlike the conventional European trading hubs,
trades made at the NBP are not required to be balanced, there is no fixed fee for being out of
balance. The NBP can be seen as the UK equivalent of the Henry Hub in the US, it is the pricing
and delivery point for Natural Gas futures in the UK that are traded on the Intercontinental
Exchange (ICE).
11
The UK has a growing import capacity, the majority of gas enters the NBP system passing
through the five beach terminals in the North Sea, but there are also direct pipelines to Europe.
Langeled is a crucial 1200km pipeline that brings Natural Gas from Norway, and is able to
supply about 26Bcm per year, the Balgzand-Bacton-Line (BBL) from the Netherlands is also able
to supply about 16Bcm per year. Zeebrugge is a physical trading point located in Belgium, this
hub is joined to the Bacton terminal of the NBP through an interconnector pipeline that started
operations in 1998 and has recently been upgraded to be able to supply 17Bcm per year. These
three pipelines are able to meet a large part of the UK’s current demand. The original intention
of the Zeebrugge pipeline for example, was to export gas from the UK North sea to Europe, but
the flow of the Interconnector is often reversed to import gas to the UK market during winter
months. Holz et al (2008) find that this increased pipeline import volume will compensate for
the decline in UK domestic production.
Natural Gas is traded on either the spot or futures markets in the UK. Spot trading is primarily
used by traders who have short-term physical gas imbalances to trade out. The Over-theCounter (OTC) market is an unregulated market which generally consists of bilateral
transactions between shippers, although the terms applicable to these transactions are specified
in the ‘Short Term Flat NBP Trading Terms and Conditions 1997, or ‘NBP97’. The spot market is
generally representative of the physical side of trading, whereas the futures markets are
favoured by speculators, or ‘paper side’ of the market. The exchange markets generally give the
best transparency of pricing as there is a great deal of day settlement pricing for gas.
This paper is focusing on the demand side of Natural Gas, although the supply side is also a key
issue. To meet the projected growth in demand for natural gas worldwide, producers will need
to increase annual production in 2035 to a level that is 46 percent higher than the 2007 total
(IEO 2010). This could prove a problem for the UK market, although Holz et al (2008) finds that
the competitive UK wholesale market will enable UK consumers to maintain their consumption
levels in the future.
1.4 AN OVERVIEW OF UK WEATHER
The UK market differs from the US in regards to the demand for natural gas. In the US market
the industrial sector is the largest consumer, but in the UK the single largest component of
natural gas demand is the domestic consumer, this is largely because about 90% of UK homes
have central heating systems of which 80% are gas-fired (Stewart, 2004). Taylor et al (1977)
find that demand in both industrial and residential sectors are price inelastic in the short-run,
but highly elastic in the long-run. Al-Sahlai (1989) confirms this and finds that industrial,
12
residential and commercial demand are inelastic with respect to price and income in the shortrun.
The UK market for Natural Gas is highly seasonal and because industrial demand for natural gas
is relatively unresponsive in the immediate short-term, the weather is the single most
important factor that causes short-term demand and price volatility. Weather data reaches the
market very quickly, and is available to all participants, thus ‘weather surprises’ will be quickly
reflected in the price of natural gas. If for example there was a sudden freeze in the North East,
consumers in cities such as Newcastle and Sunderland would turn on their central heating
systems. If this low temperature is unexpected, traders will be caught short Natural Gas and
look to the spot market to buy Natural Gas for immediate consumption. This increase in demand
would be reflected by a higher price, ceteris paribus.
Natural Gas used to power UK central heating systems is the most significant demand factor,
although over recent years the power generation market sector has been through considerable
changes. Natural Gas used to generate electricity increased from a market share of 0% in 1990
to a market share of 38% in 2002, displacing coal as the principal fuel for power generation in
the UK (Stewart, 2004). This implies that extremely hot weather surprises, such as a summer
heat wave, may instigate a spike in electricity demand used to power air conditioning systems,
which could have a similar effect on the demand for gas. Mu (2004) finds that in the US market
there is a ‘local peak’ in July and August as cooling demand increases the electric power use of
natural gas. Although this is certainly found to be the case in the US market, section three of this
paper will look to see if this thought can be applied to the UK market.
Two measures of relative temperature that are common in the market place are that of Heating
Degree Days (HDD) and Cooling degree Days (CDD). A day’s HDD is used to quantify the volume
of energy required for heating during the day, and a CDD the volume of energy for cooling
during the day, this is shown in equations 5 and 6.
Equation 5
Equation 6
13
In the US, heating and cooling degree days are primarily used in the valuation of weather
derivative contracts and generally have a threshold temperature of 65°F, for this reason 18°C
will be used as the threshold temperature in future analysis.
1.41 CLIMATE CHANGE
Natural gas is one of the world’s three principal fossil fuels and is the Earths most abundant
fossil fuel (Cocks, 2009). When conducting a study on weather and fossil fuel, climate change
needs a mention, but it is beyond the scope of this paper to look at climate change in great
detail, this paper will be concentrating on short-term, intra-month volatility, so climate change
is not entirely relevant. Harvey (2010) states that global average temperature warming ranges
from 3.5°C to as high as 6.5°C, but refers to the next century, not the next year or decade. In an
attempt to estimate future demand forecasts the National Grid created a ‘Composite Weather
Variable’ (CWV) using 2-hourly temperatures and 4-hourly wind speeds which includes factors
such as Wind chill, cold weather upturn and ‘effective temperature’, but even this complex
variable currently fails to incorporate the effects of climate change.
1.5 WHY ARE THE ENERGY MARKETS SO VOLATILE?
A highly volatile market is one in which prices are changing rapidly and unexpectedly, there is
an extensive range of literature available that examines whether market fundamentals or other
random factors determine price volatility. The price of Natural Gas, like any other commodity, is
fundamentally determined by supply and demand, and volatility, by nature, is a response to
shocks (Engle, 2001). The short-term price paid for gas is determined by various factors, which
include the availability of supply, storage levels and alternative fuel prices. Henning et al (2003)
find that the Natural gas market is one of the most volatile commodity markets, even more so
than the crude oil markets. They also conclude that near-term wellhead production is generally
inelastic, and because the demand for natural gas depends on the weather, which can shift
quickly and unexpectedly, this can creates a demand imbalance that amplifies price volatility.
In the US, ‘weather surprises’ such as hurricanes often cause vast amounts of volatility in the
market for Natural gas. Figures 6 and 7 show the impact that these hurricanes had on wellhead
production during the hurricane season of 2005. Shortly before Katrina and Rita hit, the
demand for Natural gas was already above normal, due to higher-than average late summer
temperatures in the South. This increased the demand for gas to generate electricity, which
consumers then used to cool their homes, when combined with the supply disruptions that
followed, these hurricanes caused a significant spike in prices. Weather incidents can cause
large intra-day price volatility, although in the UK area tropical hurricanes are not possible.
Winter of 2009-10 was the coldest on record for some years (see section three). Figure 3 show
14
the daily low temperature in Birmingham, against both spot and futures prices over the period
December 2009 to January 2010. It shows clearly that this ‘weather surprise’ caused market
volatility and the price paid for Natural gas to both increase. During the period 7th-8th of January,
temperature was at its lowest level and spot prices were at their peak, this is consistent with the
theory that short-term spot prices are largely determined by demand fundamentals and traders
trading out short-term imbalances.
Spot/Futures Price
50
45
40
35
30
25
Spot Prices
Futures Price
27
/0
1/
20
10
13
/0
1/
20
10
20
/0
1/
20
10
30
/1
2/
20
09
06
/0
1/
20
10
20
16
/1
2/
20
09
23
/1
2/
20
09
02
/1
2/
20
09
09
/1
2/
20
09
Temperature
8
6
4
2
0
-2
-4
-6
-8
-10
-12
Birmingham Daily Low
Figure 3 – Futures Price vs. Birmingham Daily Low, December 2009 – January 2010.
2 LITERATURE REVIEW
The data analysis part of this paper will first look at temperature and its relationship to the
demand for Natural gas. Then a brief test of the EMH will be conducted, and if rejected, further
analysis will be conducted to see which financial market is more sensitive to temperature
15
volatility. There is very little literature on weather and the demand for natural gas, the majority of
the literature is based around the US market and concentrates on either the spot or futures
markets. Literature in regards to the UK Natural gas market tends to be composed by government
departments (see DTI, 2001) or the National Grid (see National Grid, 2007). There are clear gaps
in current research on the weather, temperature and their relationships to the UK market for
Natural gas, and the European Gas market has only just recently began to liberalise, thus literature
is scarce.
The EMH is one of the most discussed topics of finance; academics have disputed the theories of
the EMH for decades. The commodity markets are largely unregulated in comparison to the
capital markets, this makes the commodity markets a prime candidate for EMH testing and is
the reason for the extensive range of literature in this area. In general early literature suggests
that the inter-market price behaviour and relative volatility are consistent with the theory of
storage, accepting the hypothesis that the futures price equals the expected spot price (see
Telser, 1958, Gray, 1961, and Dusak, 1973). The EMH has also been tested and confirmed in
markets such as the Electricity markets (see Shawky, 2002) and the wheat markets (Chang,
1985). The majority of studies have focused on long-run properties, arguing that in the long-run
inefficiencies will be traded out via arbitrage (Garbade and Silber, 1983) or that spot and futures
contracts share common stochastic trends (Lien and Root, 1999). However, Houthakker (1957)
found evidence of inefficient markets in the Wheat, Corn and Cotton commodity markets and
Roll (1984) came to the conclusion that Florida Orange Juice futures prices were
informationally inefficient.
The futures markets for commodities such wheat have been studied for decades, but the Natural
gas markets were largely regulated and monopolised by domestic governments until recently,
the US futures markets for example started trading in 1990. Only in recent years has data
become available in the UK market, and continental Europe has only just embarked upon fully
liberalising its markets. Herbert (1993) was the first to study the markets for US Natural gas
futures and found early examples of inefficiency in the markets, Chin et al (2005) and Mazighi
(2003) also reject the hypothesis of efficient markets in their Natural gas data. Whilst there is a
great deal of literature rejecting market efficiency, there is also a wide range of academic studies
that accept market efficiency. Walls (1995) adopted Herbert’s methodology, focusing on co
integration and found the spot and futures prices to be co integrated where Modjtahedi and
Movassagh (2005) observed that trends are due to positive drifts in the random-walk component
of the price. It is clear that various authors agree and disagree on different aspects of the EMH, and
for this reason part five of the paper will first test the EMH before conducting further analysis into
16
the markets for Natural gas, but, whereas all the literature thus far has focused on the US markets,
this paper will test the UK market. Further research in this area is also needed, particularly
focusing on the European and UK markets for Natural gas. Analysis in future sections will be
largely based on work by Mu (2004) and Ates and Wang (2007) who both favoured the use of
GARCH models to measure volatility in their data series.
Mu selected 766 weather stations east of the Rocky mountains from 1949-2000 which were then
used to define a weather surprise variable. The weather surprise variable was then implemented
into a GARCH model. Mu found that the weather surprise variable has a significant effect on the
conditional volatility of natural gas prices. Mu also concluded that the inclusion of the weather
surprise variable in the conditional variance equation has significant effects on volatility
persistence. Ates and Wang define a more complex ARMA X-Threshold GARCH model to define
volatility in their data series. They find that conditional volatility shocks are more persistent in
the futures market than in the spot market and propose that this is because informed traders
prefer to trade in the futures market because of its low trading costs relative to the spot market.
Their analysis also finds that extreme cold weather surprises affect the variation in basis, spot
and futures prices, that the conditional volatility of natural gas spot and futures are higher in
winter and lower in summer months, and the conditional correlations between spot and futures
markets are lower in winter and high in summer months, all of which will be tested in parts 4
and 5 of this paper. Suenaga et al (2006) and Brown and Yucel (2008) both come to similar
conclusions to Ates and Wang and Mu, that volatility is greater in the winter than in the
summer. The reasons they give is because the high marginal cost of natural gas production and
the inelastic winter mean that shocks of even a small magnitude can cause a large price swings.
The authors all agree that there is more volatility and higher prices in the winter than the
summer and the large majority find that the weather, particularly cold temperature has a very
significant impact on Natural Gas prices. This paper will largely adopt the GARCH
methodologies adopted by past literature. The only part of past studies that may have to be
amended is when defining the weather surprise variable, this is because all of the studies are in
the US markets, which are markets for both heating and cooling degree days, this paper expects
to find cooling degree days to be largely insignificant in comparison to heating demand in the
UK markets, thus, the composite weather variable may have to be redefined.
There are clear gaps in the current research, for example, the majority of literature focuses on
the US markets, this may mean that there are some factors overlooked or overstated in past
studies that need to be included or excluded in future studies of the European and UK natural
17
gas markets. The reasons why there is very little literature on the UK markets and European
literature is non-existent is because the markets have only recently been liberalised so further
research into this area is definitely required as the Natural gas markets continue to expand in
upcoming years.
3 GENERAL DATA SUMMARY
This paper obtained daily actual National Grid Demand figures from 1st Jan 2003 until the 31st
May 2010 and daily front month NBP closing prices from the 5th of September 2005 until the
28th of May 2010, both were obtained from Bloomberg. Daily British prompt Natural gas closing
prices from 1st Jan 2003 until the 31st May 2010 were also obtained from Reuters. Natural Gas is
18
measured in pence per therm where the demand figures are measured in million cubic meters
(MMcm). The data is summarized in table 1, the daily demand figures are reported daily, spot
and futures prices are reported on trading days only.
Observations
Min
Max
Mean
2,630
130.52
465.46
280.82
1,932
5
168
1,189
14.7
113.53
(excl missing
Skewness
Kurtosis
67.25
0.14
2.02
35.20
17.73
1.80
8.59
43.34
17.75
0.78
3.37
values)
Demand
‘UGASDEMD’
Spot Prices
‘GA1NB’
Futures Prices
‘NBPG1MON’
Standard
Deviation
Table 1 – Summary of Demand, Spot and Futures data.
The highest demand recorded over the periods was on 8th January 2010, at a time when
temperatures were at a record lows. The smallest demand recorded over the period was on the
30th July 2006, at a time when 60% of the US was experiencing drought conditions (McKenzie,
2006) and the UK was in the midst of a cool summer day relative to previous weeks (Deakin,
2006). The lowest recorded spot price in the periods was the 3rd of September 2006, at a time
when the UK had experienced the warmest September since records began, following the
warmest month on record, July 2006 (Forster, 2006). The highest recorded spot price was also
the day of the highest recorded futures price, the 22nd of November 2005. Prices were high
around this time largely because of supply-side issues, this period was in the midst of RussiaUkraine gas disputes and coincided with the time when cold winter weather began to hit large
parts of the UK.
Although this paper has obtained daily demand figures for the whole of the UK mainland, the
national grid segments the UK into thirteen different LDZs (see Figure 8). The methodology
employed when attaining weather data was to select the largest consumer, where possible, in
each LDZ and attain daily weather data3. Gas sales and customer data were obtained from the
National Grid, and the following cities were selected, Glasgow, Carlisle, Sunderland, Manchester,
Nottingham, Birmingham, Wrexham, Cardiff, South end-on-sea, Hammersmith and Fulham,
Brighton and Hove, Southampton and Bristol, this data is summarised in table 18.
Based on these locations, weather data was obtained from Bloomberg from 1st January 1973
until the 28th June 2010. To remain as consistent as possible across the dataset and to reduce
Hammersmith and Fulham although not the largest consumer in the ‘North Thames’ LDZ, is the closest
large urban area to the chosen weather station.
3
19
basis risk4 all of the data was taken from the closest Airport and all the temperature
measurements were taken in mid afternoon, between 13.20 and 13.50, this methodology is
summarised in table 19. The dataset consists of observations on temperature, detailed in Table
20: daily maximum (.HIGH), minimum (.MIN), average (.MEAN), heating degree days (.HDD) and
cooling degree days (.CDD). The data set contains occasional missing observations due to failure
of measuring stations to report to Bloomberg, although this is rare and largely insignificant as
there are almost 900,000 data points within the historical weather data. ‘Wrexham’ has been
removed entirely from future analysis as the data set was largely inconsistent and suffered from
clear misreporting of data, tables 21-32 provide summary statistics of the selected locations.
The reasons for collecting such as vast amount of data will become more apparent when a
‘weather shock’ variable is defined in later sections. Although the market in the UK isn’t as
geographically broad as the US market for Natural Gas (Mu, 2004, selects 766 weather stations
east of the Rocky Mountains from 1949-2000) the rationale for selecting weather stations at
various locations is to ultimately create a variable that includes all of the locations specified
above.
4 THE DEMAND FOR NATURAL GAS AND TEMPERATURE
This part of the analysis will look at temperature and Natural Gas demand. Mu (2004) states that
the weather impacts about fifty percent of U.S. natural gas demand, and as explained in previous
sections, the UK markets is much more reliant on Natural Gas than the US market. UK Households
are now kept warmer than in the past. In 1970 5.6m homes were centrally heated, this increased
to 21.7m by 2000, likewise, average internal temperatures increased from 13°c in 1970 to 18°c in
2000 (DTI, 2001). With living standards expected to increase over time, a similar trend is likely to
continue in the future, for this reason this paper expects to find a strong relationship between
temperature and demand in the UK market.
The risk that the temperature differs in the measurement station to that of the main rural area which
was selected based on consumption data.
4
20
4.1 DATA AND METHODOLOGY
National Grid actual demand figures from the 1st Jan 2003 until the 31st May 2010 were
obtained from Bloomberg, summary statistics are provided in Table 1. As explained in section 3,
the majority of the weather stations are located at airports closest in proximity to the highest
consuming area in that particular LDZ. Although every feasible step has been taken to ensure the
reliability of the weather data some issues may still persist, for example, when estimating their
CWV the National Grid notes that one weather station is on the top of an office building and is
affected by the heat from the building in very cold weather (pg 25, Gas Demand Forecasting
Methodology 2007). To ensure complete accuracy of the data, each location would have to be
checked individually and any anomalies adjusted for, this is beyond the scope of this paper.
Ates and Wang (2007) use just one city, Chicago, in their regression analysis. Chicago is generally
favoured as it is the third most populous city in the US and one of the largest consumers of Natural
Gas. Mu (2007) uses ‘total degree days’ as the explanatory variable in the regression analysis,
similar to that shown in Equation 7.
Equation 7
The reason why ‘total degree days’ (DD) are favoured when using historical data from cities such
as Chicago is because the summers are comparatively much warmer that those of the UK’s largest
cities, and thus consumers require more cooling energy. The historical July and August average
high in Chicago is about 7-8°c higher than in London, this is a significant enough variation to have
differing impacts on CDD’s across these two locations. Table 33 shows that Heating Degree Days
have the highest correlation to Natural gas demand and Cooling Degree Days the lowest across all
the chosen UK cities, it is for this reason that HDD’s will be preferred in further analysis. A
Classical Linear Regression Model similar to that implemented by the National Grid will be first
estimated using OLS. Each location will be regressed against the demand data, in addition to this a
‘Composite HDD Variable’ (CHV) will also estimated, representing the sum of all the locations. The
reasons behind regressing each location in addition to the CHV is to discover if any one location,
London for example, is as accurate as using a composite variable. Ates and Wang (2007) elected to
use only one location in their analysis into the weather and temperature, this is in comparison to
Mu (2004) who, as explained briefly above, choose to use numerous locations.
The formulated research question is ‘Do low temperatures have a significant impact on National
Grid demand data?’ the Null hypothesis is that HDD’s at each location do not impact national grid
demand.
21
4.2 MODEL SPECIFICATION
The methodology preferred by the National Grid when evaluating the relationship between
demand and the CWV is shown in Equation 8, where A&B are constants, CWVi the estimated
Composite Weather Variable and ui the error term and i a ‘non-holiday weekday’.
Equation 8
A similar simple regression model will be estimated using OLS, see Equation 9, where
UGASDEMDi is the actual daily demand for Natural Gas, in MMcm. nHDDi the Heating Degree Days
at location n, and εi the error term.
Equation 9
This regression will be estimated against each location and the CHV (see Equation 11), where
UGASDEMDi is the actual daily demand for Natural Gas, CHVi the Composite Heating Degree Day
Variable, and εi the error term.
Equation 10
The CHV is an arithmetic mean and is computed using the methodology shown in Equation 11,
where n represents the number of weather stations, with each locations Heating Degree Days
denoted by HDDi.
Equation 11
As the dataset contains two leap years, the 29th of February 2004 and 2008 have been removed from
the analysis to insure that the CHV is consistent across the dataset. Section 4.3 interprets the
estimation results. Firstly the Augmented Dickey Fuller (ADF) test was conducted to check for
stationarity. The Breush-Pagan and White tests were then employed to test for heteroscedasticity.
Finally, the Durbin Watson (DW) and Breusch-Godfrey (LM) tests were used to check for serial
correlation.
4.3 ESTIMATION RESULTS
An important assumption in regression analysis with regards to time series data is that the data
fluctuates around a mean, that is, the data is stationary. Running OLS on a non-stationary series can
22
lead to a spurious regression, to avoid this all variables in the model are required to be stationary.
For example, generally one would expect commodities to be stationary, over time they are expected
to follow an upward trend, this is because as world population grows, people demand more food
and more energy and people generally expect their standard of living to increase, all of these factors
imply and upward trend in future years. The EMH implies that the behaviour of commodity prices
should follow a random walk, that the data should be non-stationary, thus, the following hypothesis
will be tested;
H0: δ=0 Non-Stationarity
H1: δ<0 Stationarity
Table 2 compares the computed tau-statistics with the critical tau values. The ADF is first conducted
with just one lag, then an AR (p) model is estimated to determine up to which point lags become
insignificant. In both cases up to three lags are significant, beyond this point lags become
insignificant, thus 3-lags will be used to test the ADF. Both Natural Gas demand and the CHV are
found to be stationary. For demand data, ten lags would have to be used the confirm the null of
non-stationarity at the 1% level and for the CHV twelve lags are needed to verify the null.
ADF Test
Computed τ, 1 lag
Demand
CHV
Computed τ, ARIMA
Reject the Null
method
Hypothesis? (1% level)
-5.959
-4.680 (3-lags)
Yes/ Yes
-8.789
-6.558 (3-lags)
Yes/Yes
Table 2 – Augmented Dickey Fuller Test for Unit Roots
Equation 9 was estimated across each location, the estimated coefficients and model fit are shown
in table 3. In all of the regressions the HDD’s and Gas demand show a positive relationship, which
is consistent with previous literature. As HDD’s increase, equivalent to the temperature failing, the
demand for Natural gas increases in all locations. All of the p-values on the estimated coefficients
are very significant and all regressions show evidence of strong model fit, based on R2 values.
The location with the largest Model fit is London, the London Metropolitan area is the most highly
populated area and is responsible for the biggest demand for Natural gas in the UK, so the fact that
London explains more of the variation in Natural gas demand is not surprising. Introducing the
CHV as the dependent variable increases model fit slightly, but it is clear that choosing one large
city, such as London or Chicago (Ates and Wang, 2007), will produce results similar to creating a
composite weather variable, but creating a CHV generates a slightly more efficient slope
coefficient.
23
95% Conf.
London
Birmingham
Glasgow
Sunderland
Manchester
Nottingham
Cardiff
South end
Brighton
Bristol
Southampton
Carlisle
CHV
Coefficients
N-W S.E.
t-value
199.2084
1.359501
146.53
11.49701
0.1394825
82.43
188.6218
1.480893
127.37
11.54471
0.141975
81.32
178.5206
1.843107
96.86
1.89268
0.1817026
64.45
178.3121
0.1716902
100.32
11.93757
1.777476
69.53
185.1597
1.665936
111.14
11.82768
0.1652612
71.57
191.2574
1.4648
130.57
11.76738
0.14637
80.39
185.2261
1.517523
122.05
12.66905
0.1562882
81.06
200.6933
1.285398
156.13
11.77499
0.1375053
85.63
191.2164
1.413662
135.26
12.16372
0.1448235
83.99
187.0117
1.517392
123.25
1.92083
0.1522111
78.32
193.3418
1.483988
130.29
11.82598
0.1478421
79.99
193.2944
1.500767
128.80
11.76573
0.1644963
71.53
184.1587
1.463701
125.82
12.46919
0.1542591
80.83
Model Fit, R2
0.8430
0.8228
0.7531
0.7758
0.8044
0.8304
0.8305
0.8347
0.8256
0.8350
0.8005
0.8013
0.8490
Table 3 – Regression Coefficients and N-W S.E.
The Breusch-Pagan and White tests will be conducted to test for Heteroscedasticity. The Null
Hypothesis is that the error term has a constant variance, the error term is homoscedastic
(Equation 12) and the alternate hypothesis is that the error variance varies with the dependent
variables (Equation 13).
H0: Homoscedasticity
Equation 12
H1:Heteroscedasticity
24
Equation 13
Table 34 illustrates that, with the exception of Manchester, all of the chosen locations either fail
the Breusch-Pagan or White test, with Glasgow, Nottingham, Cardiff and Carlisle failing both
tests. These two tests were also applied to the CHV regression, heteroscedasticity was found to
be present using the White test, but the error term was found to be homoscedastic using the
Breusch-Pagan test. Problems with heteroscedasticity cannot be ignored, it causes the
estimators to be inefficient and have much larger variance, changing according to different
values of the explanatory variables, hetero-consistent Standard Errors (S.E.) will be calculated
after the models have been tested for serial correlation. As the regression is of AR (1) in nature,
the Durbin Watson test of Serial Correlation will be applied to the above regressions. Equation
14 will be tested, with the following hypothesis;
H0: ρ=0
H1:ρ≠0
Equation 14
If the error term is genuinely a random error term, ut, no serial correlation would imply that ρ=0.
Table 35 illustrates that in all of the tests there is evidence of positive serial correlation, which
indicates that errors from the previous day carry over into the future, this could cause an
overestimate in one day, leading to an overestimate in succeeding days. The OLS standard errors will
be smaller than the true standard errors and the parameter estimates will give the impression of
being more precise than they really are.
‘Under both heteroscedasticity and autocorrelation the usual OLS estimators, although linear,
unbiased, and asymptotically normally distributed, are no longer minimum variance among all linear
unbiased estimators. In short, they are not efficient relative to other linear and unbiased estimators.
Put differently, they may not be BLUE. As a result, the usual, t, F, and χ2 may not be valid’ (pg 442,
Gujarati, 2003).
If conclusions are drawn and inferences made despite heteroscedastiity they may be misleading,
although ‘Heteroscedasticity has never been a reason to throw out a good model’ (Mankiw, 1990).
Further statistical testing can be conducted post-regression to test whether or not the coefficients
are biased, future analysis in this section will test only the Compounded Weather Variable (CHV).
25
In regards to the CHV, the null hypothesis of Homoscedasticity is not rejected using the BreuschPagan test and only just rejected using the white test, a p-value of 0.007 is only slightly below the
0.01 rejection region. The White test results suggest that the coefficients are biased and are no
longer valid to construct t-statistics and make inferences. White developed an estimator for
standard errors that is robust to the presence of heteroscedasticity, although a large sample size is
required, with a sample size of 2,472 in this analysis this solution is acceptable. Before calculating
the hetero-robust S.E. a test of serial correlation will be conducted to determine whether to use
Robust or Newey-West adjusted S.E.
Time series data are, by definition, ordered in time, what occurs at time t is the best indicator of
what will occur at time t+1, as a result the difference between the predicted and actual error in one
time period are related to the error in the text time period. The DW test above tested for serial
correlation of an AR (1) nature, the estimated statistic fell into the reject region suggesting strong
positive autocorrelation, this suggests that the OLS estimators are no longer efficient and the
estimated R2 is not a reliable estimate of the true R2. To check for serial correlation of a higher than
AR (1) nature the DW is not suitable, for this the Breusch-Godfrey (LM) test will be favoured, which
is statistically a more powerful tool than the DW. For this part of the analysis three lagged variables
were chosen, the LM tests confirms that there is a problem with autocorrelation as all the p-values
are extremely significant. As a result of these tests this paper considered implementing lagged
variables into a regression, this analysis will be conducted in part five. As a result of the above tests,
the Newey-West method will be implemented, which corrects the standard errors for serial
correlation and heteroscedasticity, these corrected standard errors can then be used for inference.
The Newey -West p-value’s came in very significant, (see Table 3) so we can conclude that the
relationship between the CHV and Natural Gas demand is true and strong.
The formulated research question at the start of this analysis was ‘Do low temperatures have a
significant impact on National Grid demand data?’ Although the CLRM may overestimate the effects,
the above analysis and previous literature (see Ates and Wang, 2007, Mu, 2004) clearly
demonstrates that temperature has a significant impact on the demand for Natural gas. The basic
laws of Supply and Demand dictate that if demand increases, price should increase, ceteris paribus.
Part 3.4 of the paper will look at National Grid Demand and Seasonality.
4.4 NATIONAL GRID DEMAND AND SEASONALITY
26
The methodology preferred in this part of the analysis will involve estimating National Grid Demand
against the Weather Surprise Dummy Variable (see Equation 21) and the Monthly Dummy Variable
(see Equation 41).
Equation 15
Where Dt is National Grid actual demand at time t, WSVt the Weather surprise variable at time t and
μDVt the Monthly Dummy Variable at month μ and time t. Eleven dummy variables were included in
the analysis to capture the twelve months, table 4 shows the estimation results where β2 is the
coefficient for January, β3 for February... and β13 Decembers coefficient, August was omitted from
the regression to prevent problems of perfect multicollinarity.
Demand
Coefficients
Std. Err.
t
Robust S.E
Robust t
VIF
β0
189.8395
2.679182
70.86
1.975296
96.11
β1
33.01779
2.529292
13.05
2.453172
13.46
2.94
β2
148.0643
4.312705
34.33
4.59691
32.21
3.00
β3
139.8438
4.435837
31.53
4.319888
32.37
2.94
β4
112.9477
4.329204
26.09
4.15421
27.19
2.95
Equation 15
β5
83.39094
3.870468
21.55
3.019107
27.62
2.26
Estimation
β6
55.93134
3.624846
15.43
2.94491
18.99
2.07
Results
β7
17.26326
3.777905
4.57
2.661994
6.49
1.86
β8
9.340919
3.746121
2.49
2.578551
3.62
1.88
β9
(omitted)
β10
20.27319
3.768107
5.38
2.777722
7.30
1.87
β11
67.40618
3.866743
17.43
2.875138
23.44
1.97
β12
96.40087
4.25406
22.66
3.386343
28.47
2.33
β13
135.7499
4.461677
30.43
4.795564
28.31
2.45
Table 4 – National Grid Demand and Seasonality Estimated Coefficients.
The model specification was then tested, tests of multicollinarity, heteroscedasticity and
autocorrelation were conducted equivalent to those implemented in the earlier section. VIF values
were estimated to check for multicollineratity, no VIF value was found to be above 10 so it appears
there is no problem of multicollinearity. The Breusch-Pagan statistics came in significantly below
0.05 and the White test came significantly below 0.01, both tests conclude there are problems of
heteroscedasticity. The Breusch-Godfrey (LM) test was implemented with various lengths of lags,
27
there was no evidence of serial correlation. To correct for heteroscedasticity, robust standard errors
were calculated and are displayed in table 4.
Month
Estimated Natural Gas Demand
(β0 + βi)
Estimated Demand with Weather
Surprise
(β0 + βi + β1)
January
337.9038
370.9216
February
329.6833
362.7011
March
302.7872
335.805
April
273.2304
306.2482
May
245.7708
278.7886
June
207.1028
240.1206
July
199.1804
232.1982
August
189.8395
222.8573
September
210.1127
243.1305
October
257.2457
290.2635
November
286.2404
319.2582
December
325.5894
358.6072
Table 5 – Evidence of Seasonal Demand for Natural Gas.
Table 5 is constructed using the results from table 4 and the following inferences can be made.
January is the month with the highest demand for Natural gas, August the lowest and ‘weather
shock’ increases the demand for Natural gas by an estimated 33 Mcm, a visual representation is
shown below in Figure 4, with total demand shown on the vertical axis and the date shown on the
horizontal. Mu (2004) finds evidence of a demand ‘spike’ around June and July (hot weather causes
an increase in Cooling Demand), although figure 4 suggests that there is no evidence to suggest that
this exists in the UK market for Natural Gas
28
National Grid Estimated Demand (Mcm)
350
300
250
200
150
National Grid Estimated Demand (Mcm)
Figure 4 – National Grid Estimated Demand (Mcm),
Further analysis was also conducted to include Macroeconomic factors and domestic heating prices,
to test if the weather was the most significant determinant of Natural Gas demand. The majority of
the variables added were found to be insignificant in the short-term, only domestic prices affected
consumer demand, but there were large lags and thus are largely beyond the scope of this paper.
5 THE FINANCIAL MARKETS AND TEMPERATURE
As explained in the above sections, theoretically, as Natural Gas can be stored, any disparity
between spot and futures prices create arbitrage opportunities, this should ensure a close
relationship between spot and futures prices. The aim of this section of the paper is to find if
temperature is a key reason for abnormal disparity, or volatility, in the markets, and if so which
market is most sensitive to changes in temperature, the spot or futures market. The second part
of this section will then look to examine Campbell and Diebold (2000) findings that the winter
months are more volatile than summer months, and that prices are higher in the winter than the
summer. But, before we can conduct any of the above analysis we first need to test the EMH.
5.1 TESTING THE EMH
29
The spot and future prices should behave similarly over time, they should be co-integrated,
Herbert (1993) was the first to test the relationship between spot and futures contracts in the
Natural Gas markets, Herbet’s methodology will be adopted for this part of the analysis.
The first step is to estimate the simple regression, using OLS, shown in equation 16.
Equation 16
Where Sprt are daily British prompt natural gas prices and Fprt daily first month NBP Natural
Gas prices. If the estimated coefficient for c is not statistically different from zero and the
estimated coefficient for d is not significantly different from one, this suggests that the market is
efficient.
Equation 17
Herbert found that both series needed to be differentiated twice before a white noise series was
obtained and thus concluded that both series are integrated of order two, the results of Herbert
(1993) regression is reported in Equation 18.
Equation 18
Table 6 provides some summary statistics on the Spot and Futures prices for British natural gas,
obtained from Bloomberg and Reuters, prices are quoted in GBP per Therm.
Sept 05-May 10
Mean
Standard Deviation
Variance
Skewness
Kurtosis
GA1NB Spot Prices
40.98
18.82
354.12
1.50
7.52
NBPG1MON Futures Prices
43.24
17.75
315.06
0.77
3.37
Table 6 – Spot and Futures Summary Statistics.
Some observations at the end of 2005 were inconsistent and possibly misreported, for this
reason 1,108 observations were selected from January 2006 until May 2010. Campbell and
Diebold (2000) looked at temperature data in the US from the 1960’s until the early part of
2001 and found that all of their distributions had moderate skewness and moderate excess
kurtosis. The most likely reason for the large Kurtosis in this data set, particularly the spot data,
as explained in section three, is that the Russia-Ukraine gas dispute caused very large daily
movement in the spot markets in late 2005, for this reason it is excluded from the above
30
analysis. This data was used to run a regression similar to Herbert (1993). The results are
shown in Equation 19.
Equation 19
The estimated coefficients are both significant and it can be concluded that the coefficients c and
d are statistically different from 0 and 1 at the 5% level. The results above are consistent with
Herbert (1993) findings and demonstrate that the market is inefficient. The following section
will now test which market is most sensitive to temperature volatility.
5.2 WHICH MARKET IS MORE RESPONSIVE TO CHANGES IN TEMPERATURE?
Many of the more public critics, such as your daily newspapers, often blame the low cost of
trading in the futures markets for excess speculation, and thus market volatility. This may be
the case, but in terms of the weather, recent literature, most notably Campbell and Diebold
(2000), find that low temperatures have stronger effects on the volatility of spot price changes
then on futures price changes, Henning et al (2003) also find that the volatility of prices in the
futures market has the propensity to be much lower than the volatility in the spot market..
The next section will focus on the relationship between temperature and price volatility. In
particular it will test if conditional volatility shocks are more persistent in the futures than the
spot market (Ates and Wang, 2007) or vice versa (Campbell and Diebold, 2000). Further
analysis of the hypothesis that volatility is higher in the winter and lower in the summer (Ates
and Wang, 2007) and thus prices are higher in the winter than the summer (Campbell and
Diebold, 2000, Brown and Yucel, 2008 and Suenaga et al, 2006) will also be considered.
5.3 DATA AND METHODOLOGY
The terms conditional variance and volatility will be used interchangeably, and price volatility is
defined as the returns on daily price movements. Both the spot and futures data display signs of
excess skewness and kurtosis, as prices are bounded by zero on the downside but are limitless
on the upside, the distribution of price data is often skewed. In order to create a more normal
data distribution the continuously compounded log return will be used to measure intra-day
volatility, shown in Equation 20, this methodology is generally favoured across the literature.
Equation 20
Non-trading days are excluded from the analysis, these include weekends and holiday periods
such as the Christmas and Easter periods. A weather surprise variable (WSV) will be created
31
similar to that generated by Mu (2004) and Ates and Wang (2007). Equation 21 defines the
criteria that is used to establish the thresholds of what constitutes a ’weather surprise’.
Equation 21
Where CHVt denotes the sum of Heating Degree Days at all locations at time t and HDD.AV is an
integer which represents an approximate average HDD figure across all locations from 1970 to
June 2010. The reason why data as far back as 1970 is used to estimate the ‘shock’ factor is
because this period includes many shocks and is a much more efficient predictor of future
weather patterns, more effective than using the last two years of temperature data for example.
Historically Bristol has the lowest HDD’s at 7.17, Sunderland the highest of 9.77, to keep the
Weather Surprise Variable estimations simple HDD.AV will take the value of ‘8’ in future
analysis. As a fixed integer has been selected it is easier to see that the more the temperature
deviates from normal, the greater is the weather surprise. A ‘WSV Dummy variable’ will also be
created, the variable takes a value of 1 if the WSV at time t is greater than zero, i.e. there is a
‘cold weather surprise’ and a value of 0 is taken if the WSV at time t is zero, i.e. there is no
weather surprise, see Equation 22. As HDD’s cannot be negative, the downside of the ‘weather
surprise’ is limited to zero.
Equation 22
Firstly, each price series is tested for the presence of a unit root using the Augmented DickeyFuller (ADF) test. It is important to check for stationarity as, if energy consumption is stationary
shocks will be transitory whereas if energy consumption is non-stationary (i.e. contains a unit
root), shocks will be permanent. This is important when predicting future forecasts as if energy
consumption is stationary, then the past behaviour of energy consumption serves a role in the
generation of forecasts. On the other hand, if energy consumption is non- stationary, then the
past behaviour of energy consumption serves little or no use in forecasting (Apergis et al, 2010).
In part three of this paper tests of stationarity were conducted on HDD’s and Demand data, a
similar ADF test for unit root will be conducted below. In this section an ARIMA model will be
estimated to determine the optimal number of lags required to test for stationarity (Said and
Dickey, 1984, used a similar approach). In all instances, the null hypothesis of non-stationarity
32
is tested. Table 7 shows that in all three time series the null is rejected, all are found to be
stationary, which is consistent with results in the US markets (Brown and Yucel, 2008).
Variable
Number of Lags
Test Statistic
Reject the Null?
Spot Returns
7
-15.047
Yes
Future Returns
3
-17.492
Yes
WSV
9
-4.332
Yes
Table 7 – ADF, Jan 2006 – May 2010.
Graphical analysis was also conducted, correlogram and partial correlograms were estimated to
check for evidence of autocorrelation. Mu (2004) provides an autocorrelation function (ACF)
table with coefficient estimates of 10 lags, Gujarati (2003) states that a rule of thumb is to
compute Autocorrelation Functions (ACF) up to one-third to one-quarter the length of the time
series. As the time series in this study are so large the portmanteau test for white noise, which
tests whether the selected group of autocorrelations are different from zero will be favoured.
Table 8 reports the Portmanteau test statistics, both Spot return and the WSV are found to be
significant, but Futures prices display random walk characteristics. That is, futures price returns
today are not correlated with returns from previous periods. As the main aim of this paper is to
ultimately provide a future prognosis for these markets futures price returns will be excluded
from further analysis based on the results displayed in table 8.
Number of
Spot
Prob > Chi2
Futures
Prob > Chi2
WSV
Prob > Chi2
Q(2)
16.02
0.0003
1.55
0.4617
1536
0.0000
Q(20)
54.76
0.0000
15.17
0.7615
8142
0.0000
Q(150)
224.25
0.0001
170.88
0.1167
20763
0.0000
Q(300)
343.60
0.0042
338.36
0.0629
36829
0.0000
Lags
Table 8 – Portmanteau test for White Noise, Jan 2006 – May 2010.
5.3 ARMA - MODEL SPECIFICATION AND RESULTS
Before using any statistical modelling, a measure of volatility must first be defined. Equations 23
explain the basic steps in defining spot price volatility in this paper
Equation 23
Where dY*t is the relative change in spot returns and Xt is the mean-adjusted relative change in spot
returns. X2t will be used as a measure of volatility. Autocorrelation and Partial Autocorrelation
33
graphs were first constructed, there was strong evidence of both in the Spot Price volatility variable
Xt. A correlogram was then examined to identify the nature of the time series process(es).
An ARMA model allows Yt to be explained by the past, or lagged, values of Y itself and stochastic
error terms. An ARMA (1,0) model was first constructed and is shown in Equation 24.
Equation 24
This model was estimated and the coefficients were found to be significant. To determine the
optimal lag length of this model the Alkaike Information Criterion (AIC) will be implemented, which is
used to find the model that helps best fit the data with the minimum of parameters. The AIC
imposes a penalty when adding more regressors to an equation, a penalty harsher than the adjusted
R2, the model with the lowest AIC will be favoured. The PACF suggested that an AR(4) model could
be the best fit of the data and table 9 shows that an ARMA (4,0) model has the lowest AIC value.
AIC
ARMA (1,0)
ARMA (2,0)
ARMA (3,0)
ARMA (4,0)
ARMA (8,0)
-4295.22
-4293.26
-4395.96
-4401.26
-4395.29
ARMA
(20,0)
-4376.74
Table 9 – Akaine’s Information Criterion – AR Component.
To find the order of the MA component the ACF was estimated, and suggested that an MA (7) would
best fit the data. Table 10 shows that an MA (7) model produced the lowest AIC value.
AIC
ARMA (0,1)
ARMA (0,2)
ARMA (0,3)
ARMA (0,4)
ARMA (0,7)
-4,309.73
-4,308.02
-4,367.07
-4391.60
-4,396.44
ARMA
(0,10)
-4392.68
Table 10 - Akaine’s Information Criterion – MA Component.
As the intention is to construct an ARMA (p,q) model, 39 different combination orders of both the
AR and MA components were tested to find the grouping with the best model fit, table 11
demonstrates that an AR (3,1) model was found to have the lowest overall AIC value.
AR(0)
AR(1)
AR(2)
AR(3)
AR(4)
MA(0)
-
-4295.220
-4293.260
-4395.960
-4401.260
MA(1)
-4309.730
-4308.364
-4334.702
-4402.084
-4400.418
MA(2)
-4308.020
-4344.193
-4382.152
-4400.170
-4399.976
MA(3)
-4367.070
-4397.271
-4395.288
-4398.352
-4398.244
MA(4)
-4391.600
-4395.316
-4393.668
-4396.995
-4396.573
MA(5)
-4391.508
-4396.442
-4394.701
-4396.404
-4394.624
MA(6)
-4391.416
-4396.442
-4393.087
-4394.461
-4392.825
34
MA(7)
-4396.440
-4394.599
-4393.500
-4393.070
-4391.095
Table 11 – Akaine’s Information Criterion - ARMA (p,q)
The ARMA (3,1) model (Equation 25) was estimated and the coefficients are shown in Equation 26.
Equation 25
Equation 26
The second AR coefficient was found to be insignificant at the 5% level. Therefore Equation 26 was
improved by dropping the second lagged AR variable, the refined model is shown in Equation 27 and
the coefficients shown in Equation 28. It must also be noted that the AIC value of the new refined
model is -4,404.013, so using the AIC criterion dropping the second lagged AR variable improved the
model.
Equation 27
Equation 28
After fitting the new refined model to the Spot Volatility variable the residuals terms need to be
checked for serial correlation. A Portmanteau test for white noise was conducted at various lag
lengths and the residuals were found to behave as white noise processes, which confirms this fitted
model is adequate. There is evidence in the above analysis to suggest that the UK natural gas spot
market is illiquid and inefficient, for example, equation 28 demonstrates that spot price volatility
three days ago has more impact on spot price volatility today then yesterday’s volatility. Figure 4,
adapted from Brealey et al (2008) shows that the fashion in which price reacts is dependent on
market efficiency. Part 5.1 of the paper tested the UK Natural gas spot and futures markets and
found evidence of market inefficiency. The results in equation 28 suggest that the market reacts
with a three-day lag to information, which is consistent with Line 1 (Figure 5), or, ‘Slow Reaction’.
There is also evidence, in the fact that the 2-day lag was statistically insignificant to suggest that the
market is persistently inefficient, or, Line 4 (Figure 5).
35
Figure 5 – Price Behaviour and the Efficient Market Hypothesis, Brealey et al (2008)
Spot prices estimated by reporting firms and are based on informal polls from traders, this could
mean that the reported spot prices are unreliable, which may well be a key factor for market
inefficiency.
Bloomberg Energy Service, for example, reports only bids and offers. But unlike exchange dealers,
traders are not required to honour them. Consequently, bids and offers may not be accurate
indicators of the actual range of sales prices on natural gas spot markets (EIA, 2002).
The weather surprise variable was added to the above ARMA analysis, but the variable was found to
be highly insignificant and reduced the model’s explanatory power.
Time series such as the UK Natural gas spot markets often exhibit volatility clustering. The
subsequent part of the paper will aim to construct a model that best fits spot price return data.
5.4 GARCH - MODEL SPECIFICATION AND RESULTS
‘Given that news can lead to various interpretations, and also given that specific economic events like
an oil (or Natural Gas) crisis can last for some time, we often observe that large positive and large
negative observations in financial time series tend to appear in clusters’ (Franses, pg 155, 1998).
In this part of paper a conditional heteroscedastic model will be constructed to measure volatility of
spot price returns. A Generalised Autoreggresive Conditional Heteroscedasticity (GARCH) model,
following Bollerslev (1986), will be estimated to capture volatility. The ACF and PACF’s of Spot Price
return were first constructed, there are some large spikes in the return data (see Figure 9), such
spike suggest that the percentage changes are not serially dependent and have some ARCH effects.
Firstly, this paper tested for ARCH (q) effects based on 1,119 observations. The null hypotheses of
‘No ARCH(q) effects’ was tested, where ARCH effects of q-order based on T observations. The null is
rejected is the calculated statistic is greater than the tabulated chi-squared value.
36
Equation 29
Table 12 shows that ARCH effects were tested for up to ARCH(4) and in all cases the null hypothesis
of ‘No ARCH(q) effects’ was rejected.
Test Statistic
Chi2 (1%
confidence)
Reject Null?
AIC
ARCH(1)
222.23
13.816
Yes
-4,296.84
ARCH(2)
221.14
16.266
Yes
-4,299.29
ARCH(4)
306.90
20.515
Yes
-4,388.43
Table 12 – Test for ARCH effects
Mu, 2004 found that a GARCH (1,1) model fitted Henry Hub Natural Gas data well, but, table 13
suggests that a GARCH (2,2) may fit UK Natural gas returns best. Tsay (2005) states that only lower
order GARCH models are used in most applications, say, GARCH (1,1), GARCH (2,1) and GARCH (1,2)
models (pg. 116, Tsay 2005). For these reasons a methodology similar to that adopted above,
calculating AIC values or competing models, will be implemented up to GARCH (2,2) to find out
which model fits the data most effectively.
The weather surprise variable will be included into the mean equation (see Equation 30), which will
then be estimated, where Yt is spot returns, φ0 a constant, WSVt the weather surprise variable and εt
reflects the news/shocks.
Equation 30
Equation 31
Equation 32
Equation 31 states that the ‘news’ at time t is normally distributed with time-varying variance (ht),
conditional on the information available at time t-1 (It-1) where εt is conditionally heteroskedastic.
Although a GARCH (1,2) model has the lowest AIC value (see table 13) for simplicity a GARCH(1,1)
model will be preferred in future analysis, this can be justified based on past literature and the fact
the AIC values for GARCH (1,1) to GARCH (2,2) models are not significantly different. The WSV
37
variable was also found to be insignificant in the analysis, table 13 shows that dropping the WSV
from analysis improves model specification, models were also tested using the Weather surprise
Dummy Variable (Equation 21), this approach was adopted by Ates and Wang (2007) and Mu (2004)
in the US Natural gas markets, but the dummy variable was also found to be very insignificant using
UK Natural Gas Spot price return data.
WSV included
WSV excluded
GARCH (1,1)
-2677.883
-2679.847
GARCH (2,1)
-2677.883
-2688.299
GARCH (1,2)
-2694.083
-2696.03
GARCH (2,2)
-2692.834
-2694.79
Table 13 – AIC for GARCH (p,q) models
A new mean equation is specified which excludes the WSV (see Equation 33), Equation 34 is
estimated and results are shown in Equations 35 and 26.
Equation 33
Equation 34
Equation 35
Equation 36
Firstly, the estimated constant in the mean equation, which is the average percentage rate of return
on the spot price is negative, 0.0014%. This is because during the period in which the analysis has
been conducted (Jan 2006- May 2010) Natural gas prices have fallen significantly, largely because of
technological advances in the extraction sector, which has caused supply glut in recent periods.
Since the coefficients of the lagged terms are highly significant (see Table 14), it seems that volatility
clustering is present.
GARCH (1,1)
Coefficients
Std. Err.
Z
-0.0013559
0.0019233
-0.70
0.1641658
0.0122222
13.43
0.8185932
0.0097264
84.16
0.0002248
0.0000208
10.81
Table 14 – Spot Returns, note that (α1 + β1=0.983<1).
The next step is to test the model; analysis will be conducted to check if the above estimated GARCH
model is adequate. If the mean equation is adequate, we expect the standardised residual term
38
(Equation 37) to be a white noise process. The Ljung-Box for serial correlation is conducted to test to
see if the standardised residual is serially correlated.
Equation 37
Using the Portmanteau test for white noise we conclude that the standardised residual term is a
white noise process, suggesting that the fitted mean equation is adequate. The same approach was
taken to test the adequacy of the variance equation (Equation 34). A test of the squared
standardised residual was conducted and found not to exhibit serial correlation suggesting that the
fitted variance equation is adequate too. Thus, the above GARCH (1,1) model appears to be
adequate in describing the linear dependence in the return and volatility series.
5.5 T-GARCH - MODEL SPECIFICATION AND RESULTS
The GARCH model has a weakness in that it assumes that positive and negative shocks, or good and
bad news affects volatility in the same way, i.e. it assumes the effects are symmetric. However,
there are reasons to believe that the effects of negative and positive shocks are asymmetric, the
Threshold-GARCH model (Zakoian, 1994) allows for asymmetric effects.
Equation 38
Equation 39
Equation 40
Equation 38 is a standard mean equation, where rt is spot returns, φ0 a constant and εt is the news
term. Equation 39 is also a typical Variance Equation with one exception, the γ term. The coefficient
γ is called the leverage term and captures the asymmetric effects. In this model if y is significant and
positive (negative), good (bad) news creates greater volatility in the spot price returns, table 15
provides the results of the analysis.
TGARCH (1,1)
γ
Coefficients
Std. Err.
Z
0.0021719
0.0019779
-1.10
0.1879674
0.017065
11.01
-0.067359
0.0272176
-2.47
39
0.8296099
0.0096325
86.13
0.000204
0.0000199
10.27
Table 15 - Spot Returns, T-GARCH, note that (α1 - γ +β1=0.9502<1).
The leverage term is statistically significant and negative, which means that negative news increases
spot price return volatility by about 7%. To test model adequacy the methodology applied to the
GARCH (1,1) model in section 5.4, the Portmanteau test for white noise is applied. It is concluded
that the fitted mean equation and fitted variance equations are adequate. Thus, the above T-GARCH
(1,1) model appears to be adequate.
5.6 SEASONAL ANALYSIS
The UK Demand for Natural has peaks in the winter, supply and demand fundamentals dictate that
this should cause higher prices during the period, and thus higher volatility, ceteris paribus. Firstly it
must be noted that the standard deviation of spot price returns is higher than the mean, which
implies high volatility. Mu (2004) finds that the standard deviations in winter are larger than other
seasons because natural gas demand peaks in the winter when supply is tight. The first part of this
analysis will apply GARCH to spot returns for the months of January to December. In the second part
of this seasonal analysis the measure of volatility that was defined in Equation 22 will be regressed
against the four seasons; Winter, Spring, Summer and Autumn. Previous US market literature and
economic theory suggest that spot price returns will be lower in the summer months and volatility
higher in the winter months.
5.61 MONTHLY GARCH ANALYSIS
Brown and Yucel (2008) found that because natural gas consumption is seasonal there are
higher winter prices and lower summer prices. Twelve ‘Monthly Dummy Variables’ (MDV’s)
were generated (see Equation 41), which take a value of 1 if it is month ψ and a value of zero if
not.
Equation 41
These dummy variables were implemented into a mean equation (Equation 42) and a standard
GARCH (1,1) model estimated.
Equation 42
40
August and September were found to be very significant, and February fairly significant (pvalue <0.10). The months January, March, April, May, Jun, Jul, October, November and December
were dropped from the analysis (all had p-values >0.10) and the model was re-run as Equation 43.
Equation 43
Removing the insignificant MDV’s from the analysis improved the AIC value from -2,673 to -2,686,
the estimated coefficients are displayed in table 16, the z values are reported in future tables as pvalues are generally 0.000.
Mean Equation of
GARCH (1,1)
Coefficients
Std. Err.
Z
0.0011208
0.0021228
0.53
-0.0117697
0.0067546
-1.74
-0.0263273
0.0035202
-7.48
-0.0166198
0.0021228
-3.60
0.000196
0.0000252
7.78
0.179872
0.0137602
13.07
0.8119904
0.0103297
78.61
Table 16 – Monthly Dummy Variable GARCH (1,1) Analysis.
Reflecting on Table 16 the following inferences can be made. If the month is August, which is
statistically the most significant month, spot prices returns are expected to be 2.5% lower, ceteris
paribus. If the month is September, which is statistically the second most significant month, but still
very significant, spot prices returns are expected to be 1.5% lower, ceteris paribus. If the month is
February, which is fairly significant, spot prices returns are expected to be 1% lower, ceteris paribus.
Model adequacy was again verified using the methods applied in 5.4 and as the lagged GARCH terms
are very significant we can confirm volatility clustering is again present in this time series.
5.62 SPOT PRICE VOLATILITY AND SEASONALITY
‘Volatility is greater in the winter than in the summer, this is because the high marginal cost of
natural gas production and the inelastic winter mean that shocks of even a small magnitude can
cause a large price swings’ Suenaga et al (2006).
Four new variables were generated, each indicative of a weather season. Winter (Equation 44),
Spring (Equation 45), Summer (Equation 46) and Autumn (Equation 47) dummy variables were
constructed and are defined below.
Equation 44
41
Equation 45
Equation 46
Equation 47
In initial testing the coefficient of ‘Autumn’ was found not to be statistically significant, this variable
was dropped from the analysis and regression model re-run, this is shown below in Equation 48.
Equation 48
Equation 49
Where
denotes spot volatility (defined in Equation 22). Table 17 displays the estimated
coefficients and their significant levels (95% Conf.). Summer and Spring were found to be very
significant, and Winter and WSDummy were found to be significant.
Spot
Volatility
Equation 48
Coefficients
Std. Err.
t
Robust S.E
Robust t
VIF
0.0207614
0.0050475
8.03
0.0051286
4.05
-0.0063683
0.0031445
-2.03
0.0038773
-1.64
1.80
-0.0088985
0.0035025
-2.54
0.0029643
-3.00
1.71
-0.012708
0.002939
-4.32
0.0033467
-3.80
1.26
-0.0126159
0.003400
-3.71
0.0047558
-2.65
1.50
Table 17 – Spot Volatility vs. WSD and Seasons.
To test the adequacy of Equation 48, tests of multicollinarity, heteroscedasticity and autocorrelation
were conducted equivalent to those in section 4.3. Variance Inflation Factors (VIF) values were
estimated to check for multicolineratity, no VIF value was found to be above 10 so it appears there is
no problem of multicollinearity. The Breusch-Pagan statistics came in significantly below 0.05 and
the White test came significantly below 0.01, both tests conclude there are problems of
heteroscedasticity. The LM test was implemented with various lengths of lags, there was no
evidence of serial correlation. To correct for heteroscedasticity, robust standard errors were
42
calculated and are displayed in table 17. Spring, Summer and Winter were found to be very
significant, but the Weather Dummy insignificant, Equation 50 displays the regression results.
Equation 50
Inferences can made from the results shown in equation 49. Spot return volatility is estimated to be
about 1.2% in the winter and 0.8% in the summer, which implies that volatility is approximately 50%
higher in the winter than the summer months. The Weather Variable was found to be just
insignificant when correcting for heteroscedasticity, this has been the case numerous times during
the analysis of spot price returns.
6 CONCLUSIONS
The methodology and models provided and tested above were fitted based on some very simple
logic, when temperatures fall, consumers turn on their central heating systems, and as in the UK
market these are largely gas powered this will increase demand and thus the price of gas. This paper
chose to focus on short term fundamentals that determine gas price levels. Generally it can be
concluded that the demand for natural gas will overtake crude eventually, and there is evidence to
suggest that global warming will become a significant factor in the future, but at the moment these
factors are occurring slowly. The main aim of this paper was to find if strong relationships exist
between gas demand, prices and the weather, the aim was not to forecast future price levels and
this is largely the reason why seasonality wasn’t removed from the financial data. In past literature
Cooling demand was found to be significant in the US markets (Mu, 2004) but this paper found this
not to be the case in the UK markets which implies that future research is certainly needed in this
area.
Part 4 focused on temperature and Natural Gas demand, HDD’s were preferred and a CLRM was
used to estimate coefficients. It was established that ‘daily low’ was the variable found to have the
largest impact on demand, but as the data does not show how long or at what time in the day this
temperature occurred results would be inconsistent, thus, ‘average temperatures’ were used to
calculate HDD’s which were then regressed against the demand data. London was found to be the
43
most significant city in the UK market and the composite weather variable was found to provide a
slightly better model fit. Overall it can be concluded from part 4 that the temperature has a very
significant impact on Natural gas demand, this is consistent with the discussions in parts 1-2 of this
paper. Problems with heteroscedasticity and autocorrelation were found when the models were
tested, although table 3 shows that using the Newey-West method, the adjusted Standard Errors of
the residuals can still be concluded to be significant. The model fits were also high, as shown by the
R2 values in table 3, but this can largely be concluded as clear evidence of seasonality. Seasonality
was tested in part 4.4 where it was found that January was the month with the highest demand for
natural gas and August the lowest. The key problems with the analysis in part 4 is that of model
under fitting, that is relevant variables may have been omitted. This would cause the estimated
coefficient to be biased, inconsistent, and the usual confidence interval and hypothesis-testing
procedures would most probably give misleading conclusions about the statistical significance of the
estimated parameters. As explained briefly above, other macro factors were included in early
models, to prevent the problem of model under fitting. These other variables included GDP per
capital, retail price inflation and other indicators that determine a consumer standard of living.
These were found to be largely insignificant in the short-run testing. The key variable that has been
excluded from this analysis is that of Storage, this paper was conducted on behalf of a sponsor, who
hired two students too look at very similar issues, this paper focused on temperature and the other
on the role of storage. Although the role of storage is very important and shouldn’t be overlooked,
this paper chose to ignore storage to prevent these two papers from inevitably leading to
overlapping research, it is important to note that excluding a storage variable has possibly lead to
model misspecification. Although these problems may be present it is important again to note that
this paper aimed to look for relationships, not future econometric forecasts, based on the analysis in
part 5 it can be concluded that a weather surprise increases the demand for natural gas by an
estimated 33mcm.
The aim of Part 5 was to test for market efficiency, to test which financial market, the spot or future
market, is most sensitive to changes in temperature and to try to find if winter months are more
volatility than the summer months. Testing in part 5.1 found the UK Natural gas market to be
inefficient and this was also confirmed in part 5.3 where ARMA testing suggested that spot-volatility
today is a reflection on spot price volatility in the past three days. GARCH analysis in part 5.4 found
the WSV to be insignificant in explaining spot price volatility, but found clear evidence of volatility
clustering. The T-GARCH testing in part 5.5 found that negative news increased spot price volatility
by 7%. In parts 5.61-63 tests of seasonality were conducted. In 5.61 it was found that the demand
44
for natural gas peaked in the winter months, basic supply and demand fundamentals dictate that
this will cause higher prices and greater volatility, this was confirmed in 5.62 where spot price
volatility was found to be 50% higher in the winter months than the summer months.
In comparison to the key literature, the results in Part 4 are consistent with those found by Mu
(2004), who stated that because industrial demand does not fluctuate in the short term, weather
variation is a good indicator of changes in short-term natural gas demand. The results from part 5
largely found the WSV variable to be insignificant, although Mu and Ates and Wang (2007) both found
their weather variables to be very significant in their volatility analysis. This paper believes that this can
be put down to the immature nature of the UK natural gas markets at present, which have only
recently been fully liberalised, this causes large inefficiencies in the day to day data. This paper has
found clear evidence to suggest that volatility is greater in the winter, which is consistent with Suenaga
et al (2006) and Brown and Yucel (2008).
7 BIBLIOGRAPHY
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7.2 APPENDICES
1,856,200
59,589.8
North was joint with North
West in 2007 statistics
Glasgow City
Number of
Domestic
Consumers at
Location
231,900
Carlisle
40,600
703.3
1,078,100
31,085.6
Sunderland
117,900
2,199.7
2,841,500
81,190.4
Manchester
173,700
2,905.9
1,701,000
46,290.4
Nottingham
113,700
1,874.4
2,066,500
56,197.9
Birmingham
380,500
6,855.5
Wales North and South were
joint in 2007 statistics
Wrexham
47,100
787.4
1,087,600*
30,937.7*
Cardiff
132,300
2,217.2
1,983,700
53,075.6
South end-on-sea
71,100
1,297.6
3,005,600
74,359.3
Hammersmith
74,000
1,110.1
South East (SE)
3,104,200
77,572.5
106,700
1,635
Southern (SO)
Southern was together with
North East in 2007 statistics
Brighton and
Hove
Southampton
79,400
1,185
South West
(SW)
1,704,600
Bristol
157,900
2,494.9
National Grid
LDZ
Scotland (SC)
North (NO)
North East
(NE)
North West
(NW)
East Midlands
(EM)
West Midlands
(WM)
Wales North
(WN)
Wales South
(WS)
Eastern (EA)
North Thames
(NT)
Total
Number of
Consumers
Total Sales (2007
GWh)
41,052.3
Weather Station
Location
Total Domestic
Sales (2007
GWh)
3,618.2
Table 18– Gas Sales and numbers of customers at regional and local authority level, 2007
48
National Grid LDZ
Measuring Station
Bloomberg Code
Variable Name
Scotland (SC)
North (NO)
Glasgow Airport
Carlisle Airport
WEUKEG PF
WEUKEG NC
PF
NC
North East (NE)
Newcastle
International Airport
WEUKEG NT
NT
North West (NW)
Manchester
International Airport
WEUKEG CC
CC
East Midlands (EM)
East Midlands
Airport
WEUKEG NX
NX
West Midlands (WM)
Birmingham
International Airport
WEUKEG BB
BB
Wales North (WN)
Shaw bury RAF
WEUKEG OS
OS
Wales South (WS)
Cardiff International
Airport
WEUKEG FF
FF
Eastern (EA)
London South end
Airport
WEUKEG MC
MC
North Thames (NT)
Northolt RAF
WEUKEG WU
WU
WEUKEG KA
KA
WEUKEG HI
WEUKEG DL
HI
DL
South East (SE)
Southern (SO)
South West (SW)
Shoreham-by-sea
Airport
Southampton Airport
Lyne ham RAF
Table 19 - Location of measurement stations and variable names, Bloomberg (2010).
Variable
.HIGH
MIN
.MEAN
.HDD
.CDD
Definition
Daily Maximum Temperature
Daily Minimum Temperature
Daily Average Temperature, (HIGH+MIN)/2
Daily Heating Degree Days, max (0, 18-T)
Daily Cooling Degree Days, max (T-18,0)
Table 20 - List of Weather variables and definitions.
Number of
Min
Max
Mean
High
-5
38
14.88
Low
-28
21
-8.5
HDD
CDD
Observations
London
Mean
13,680
Standard
Skewness
Kurtosis
6.54
0.147
2.50
6.97
5.36
-0.31
3.10
27.5
10.92
5.67
-0.03
2.38
0
27
7.61
5.33
0.25
2.17
0
9
0.20
0.81
5.32
35.84
Deviation
Table 21– London Summary Statistics.
Number of
Observations
High
Birmingham
Low
Mean
13,691
Min
Max
Mean
-6
35
13.59
-16
19
-8.5
25
49
Standard
Skewness
Kurtosis
6.05
0.13
2.54
6.05
4.94
-0.24
2.53
9.88
5.28
-0.05
2.41
Deviation
HDD
0
26.5
8.36
5.13
0.19
2.25
CDD
0
7
0.08
0.47
7.67
71.04
Table 22– Birmingham Summary Statistics.
Number of
Min
Max
Mean
High
-12
31
12.61
Low
-20
18
-16
HDD
CDD
Observations
Glasgow
Mean
13,689
Standard
Skewness
Kurtosis
5.47
0.05
2.78
5.23
5.13
-0.401
2.99
22.5
8.98
5.02
-0.23
2.75
0
34.5
9.54
4.96
0.28
2.68
0
4
0.02
0.206
11.97
162.93
Deviation
Table 23 –Glasgow Summary Statistics.
Number of
Min
Max
Mean
High
-6
32
11.92
Low
-16
19
-9
HDD
CDD
Observations
Sunderland
Mean
13,683
Standard
Skewness
Kurtosis
5.64
0.10
2.50
5.57
4.66
-0.17
2.65
25
8.99
4.95
-0.05
2.45
0
28
9.77
4.91
0.10
2.37
0
6.5
0.017
0.19
14.99
292.77
Deviation
Table 24– Sunderland Summary Statistics.
Number of
Min
Max
Mean
High
-6
34
13.30
Low
-19
21
-10
HDD
CDD
Observations
Manchester
Mean
13,689
Standard
Skewness
Kurtosis
5.90
0.17
2.62
6.47
4.95
-0.20
2.59
26.5
9.89
5.24
-0.02
2.47
0
29
8.61
5.08
0.16
2.26
0
8
0.08
0.49
8.07
78.99
Deviation
Table 25- Manchester Summary Statistics.
Number of
Min
Max
Mean
High
-5
35
13.26
Low
-17
30
-11
HDD
CDD
Observations
Nottingham
Mean
13,686
Standard
Skewness
Kurtosis
6.30
0.16
2.56
6.24
4.96
-0.12
2.52
30
9.80
5.44
0.001
2.43
0
29.5
8.82
5.27
0.141
2.23
0
11.5
0.09
0.51
7.81
79.89
Deviation
Table 26– Nottingham Summary Statistics.
Number of
Observations
Min
Max
50
Mean
Standard
Deviation
Skewness
Kurtosis
Cardiff
High
-7
36
13.97
6.20
0.05
2.41
Low
-13
21
7.23
5.18
-0.15
2.38
9.5
27
10.65
5.52
-0.06
2.32
HDD
0
28
8.02
5.30
0.21
2.17
CDD
0
8
0.12
0.57
6.30
49.95
Mean
13,686
Table 27– Cardiff Summary Statistics.
Number of
Min
Max
Mean
High
-6
34
13.53
Low
-12
24
-8
HDD
CDD
Observations
South
end
Mean
13,691
Standard
Skewness
Kurtosis
5.55
0.06
2.57
7.82
5.33
-0.20
2.47
27.5
10.68
5.17
-0.12
2.39
0
26
7.44
5.00
0.27
2.24
0
9.5
0.10
0.55
7.14
63.91
Deviation
Table 28– South end Summary Statistics.
Number of
Min
Max
Mean
High
-7
35
13.48
Low
-15
19
-10.5
HDD
CDD
Observations
Brighton
Mean
13,691
Standard
Skewness
Kurtosis
6.19
0.11
2.63
6.26
4.93
-0.22
2.54
26.5
9.87
5.38
-0.06
2.51
0
29.5
8.63
5.22
0.20
2.34
0
8
0.08
0.50
7.99
77.33
Deviation
Table 29– Brighton Summary Statistics.
Number of
Min
Max
Mean
High
-5
35
14.67
Low
-10
21
-7.5
HDD
CDD
Observations
Bristol
Mean
13,693
Standard
Skewness
Kurtosis
5.90
0.13
2.61
7.53
5.06
-0.24
2.43
28
11.15
5.24
-0.6
2.43
0
25.5
7.17
4.97
0.29
2.25
0
10
0.17
0.71
5.85
44.72
Deviation
Table 30- Bristol Summary Statistics
Number of
Min
Max
Mean
High
-5
35
12.60
Low
-21
23
-9
HDD
CDD
Observations
Southampton
Mean
13,691
Standard
Skewness
Kurtosis
5.61
0.13
2.73
6.68
4.24
-0.19
2.72
27
9.77
5.21
-0.07
2.53
0
27.5
8.84
5.07
0.19
2.36
0
8.5
0.06
0.40
9.77
119.62
Deviation
Table 31 – Southampton Summary Statistics.
51
Number of
Min
Max
Mean
High
-5
32
12.59
Low
-21
23
-14
HDD
CDD
Observations
Carlisle
Mean
13,658
Standard
Skewness
Kurtosis
5.62
0.11
2.64
6.66
6.68
-0.29
3.59
27
9.75
9.77
-0.87
2.60
0
33
8.86
8.88
0.21
2.44
0
8.5
0.55
0.06
9.79
119.96
Deviation
Table 32– Carlisle Summary Statistics.
Location
London
Birmingham
Glasgow
Sunderland
Manchester
Nottingham
Cardiff
South end
Brighton
Bristol
Southampton
Carlisle
Degree Days
National Grid Demand
HDD
0.9134
CDD
-0.3233
HDD
0.9072
CDD
-0.1590
HDD
0.8678
CDD
-0.1590
HDD
0.8808
CDD
-0.1762
HDD
0.8970
CDD
-0.2227
HDD
0.9114
CDD
-0.2668
HDD
0.9116
CDD
-0.1871
HDD
0.9137
CDD
-0.3469
HDD
0.9088
CDD
-0.2261
HDD
0.9139
CDD
-0.2308
HDD
0.8950
CDD
-0.2215
HDD
0.8952
CDD
-0.2476
Table 33- Heating Degree Days and Location correlations.
Location
Breusch-Pagan Test pvalue
White Test p-value
Reject the Null?
London
0.0697
0.0000
No/Yes
Birmingham
0.0934
0.0000
No/Yes
52
Glasgow
0.0221
0.0002
Yes/Yes
Sunderland
0.5415
0.0000
No/Yes
Manchester
0.3512
0.2964
No/No
Nottingham
0.0170
0.0002
Yes/Yes
Cardiff
0.0280
0.0004
Yes/Yes
South end
0.6796
0.0000
No/Yes
Brighton
0.1663
0.0000
No/Yes
Bristol
0.0453
0.0315
Yes/No
Southampton
0.1698
0.0000
No/Yes
Carlisle
0.0009
0.0037
Yes/Yes
CHV
0.1502
0.0065
No/Yes
Table 34 – Test results for Heteroscedasticity for initial CLRM.
Location
Durbin Watson
DW<dL
Reject the Null?
London
.565
Yes
Yes
Birmingham
.634
Yes
Yes
Glasgow
.604
Yes
Yes
Sunderland
.571
Yes
Yes
Manchester
.540
Yes
Yes
Nottingham
.574
Yes
Yes
Cardiff
.568
Yes
Yes
South end
.587
Yes
Yes
Brighton
.650
Yes
Yes
Bristol
.517
Yes
Yes
Southampton
.658
Yes
Yes
Carlisle
.597
Yes
Yes
CHV
.452
Yes
Yes
Table 35 – Durbin Watson tests of Serial Correlation.
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Figure 6 – Hurricane’s Katrina and Rita, and their path toward the US mainland, August-September 2005. Wells, 2006
Figure 7 – Daily Natural Gas Production from the Gulf of Mexico following landfalls of Hurricanes Katrina and Rita.
Wells, 2006
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Figure 8 – UK Local Distribution Zones, National Grid 2009.
Figure 9 – ACF and PACF of Spot Price Returns.
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