Analysis of factors influencing the Henry Hub natural gas price based on factor analysis
Analysis of factors influencing the Henry Hub natural gas price based on factor analysis
Hong Li 0 1 2 3
Hui-Ming Zhang 0 1 2 3
Yuan-Tao Xie 0 1 2 3
Di Wang 0 1 2 3
0 Center for National Resource Economy Studies , Beijing 100871 , People's Republic of China
1 School of Economics, Peking University , Beijing 100871 , People's Republic of China
2 Edited by Xiu-Qin Zhu
3 School of Insurance and Economics, University of International Business and Economics , Beijing 100029 , People's Republic of China
Henry Hub as an important transaction hub for natural gas sets the gas price standard in the USA. In this paper, the factors influencing Henry Hub natural gas prices are analyzed, and the major factors determining the price levels in the period between January 1997 and December 2016 are studied. It is found that economic conditions, total energy demand, US dollar exchange rate and gas consumption are the major factors. The mechanism of each factor influencing the Henry Hub natural gas price is also explored in the paper.
Natural gas; Henry Hub; Factor analysis
Natural gas is one of the most important types of energy
used for industrial production and household use, and its
price can have a great impact on the economic
development of a country, including total production and inflation.
The Henry Hub natural gas price is an important price level
benchmark for the gas price not only in the USA but also in
the worldwide market. Also, as a relatively cleaner energy
than coal, wood and oil, natural gas can be exploited more
broadly, and advancements in technology show a brighter
future for the use of natural gas. This paper discusses the
factors influencing the Henry Hub natural gas price.
van Goor and Scholtens (2014) mainly studied the
explanation of the fluctuation of natural gas prices in
England, and they found that the model based on supply
and demand can successfully explain the instability in the
analyzes how weather influences the
mean value and fluctuation of gas future prices and draws a
conclusion that the market fundamentals are an important
element affecting the gas price, but there are still some
aspects of the price fluctuation that is not explained by the
analyzed how the gas
stock affects price and fluctuation.
Nick and Thoenes
studied the factors that influence the gas price in
Germany in three cutoff periods and concluded that the
lack of reserve and supply is the leading factor in short
term, but in long term, the gas price is mainly influenced by
the price of crude oil and coal and conditioned on the
economic conditions and alternative energy sources.
summarized the factors impacting on gas
prices and also analyzes common factors that affect the
global price and some special factors that influence the
local price and found that the common factors include
supply, demand, alternative energy and cost, and the major
factors differ from area to area.
used the system
dynamics method, established the ISM model, analyzing
the superficial reasons, deep reasons and fundamental
reasons for global gas prices, and found that the market
supply, demand and competition are the most directly
Li and Wei (2003)
compared the gas price
within and outside China and put forward that the major
impact factors of the domestic market are the upstream
exploitation, transportation pipeline construction and
Li et al. (2015)
established a model for the
relationship between the gas price and alternative energy,
taking the theoretical price of gas as the correlation
function of alternative energy
(Ren and Sovacool 2014b)
made a forecast of the future price trend (Olsen et al. 2015).
Current literature from China and abroad mainly focuses
on particular factors
(Cheng and Guo 2007)
, such as the
supply and demand fundamentals of natural gas price
and Wang 2007)
. But there are many other factors affecting
natural gas prices, such as exchange rate
financial market risk, total energy demand and alternative
(Li et al. 2013)
. We should find out the main factors
affecting the price of natural gas from these influencing
variables. This paper refers to the major factors in the
present literature, including supply, consumption, GDP and
oil consumption, and also the most commonly
acknowledged economic index, including industrial production
(Jiao et al. 2004)
. Twenty variables are selected, and a
factor analysis and linear regression models are combined
to study the factors
(Ren et al. 2014; Ren and Sovacool
2 Model and variable
Factor analysis is the analysis method that takes variance
contribution as its choice index to select the most important
underlying factors driving changes in the studied
attributive variable. The general form of factor analysis is
where Ykt is the tth observed value of the kth variable, bkn is
the loading of the nth factor of the kth variable, Fnt is the
tth observed value of the nth factor and ukt is the tth
particularity of the kth variable.
In this paper, 20 variables are selected, including US
total gas consumption, US gas imports and US personal
consumption expenditure. We take monthly data, if there
are only seasonal data, suppose the variable has average
incremental change during the season, and if there are only
daily data, we take the data of the last day in that month.
The data come from the US Energy Information Agency
(EIA) and the Federal Reserve Bank. There are in total 240
sets of data from January 1997 to December 2016. The
descriptive statistics are listed in Table 1.
3 Empirical results and analysis
3.1 The applicability of factor analysis (Pan and Nie 2014)
Bartlett’s test proves that using the 20 variables to do
factor analysis is applicable.
The Kaiser–Meyer–Olkin (KMO) test value is 0.74,
which suggests that it is in an ideal condition to do
The sample size. In this paper, if we add more
variables that might be related to the fluctuation of
natural gas future price, such as the frequency of
earthquakes, we may be able to find out more related
variables. But the KMO will rapidly decline in this
case, which means the applicability of factor analysis
is smaller. The purpose of this work is to find out the
most important factor influencing gas price by factor
analysis and to study the influencing extent of each
variable related to factors. So, only 20 variables are
chosen. Besides, the size of samples should be large
enough for the number of variables, and 240 sets of
data are sufficient to carry the analysis.
JAN1996 JAN1998 JAN2000 JAN2002 JAN2004 JAN2006 JAN2008 JAN2010 JAN2012 JAN2014 JAN2016 JAN2018
Gas consumption factor
Total energe demand factor
3.2 Factors selection
Factor eigenvalue and variance contribution of the model
are listed in Table 2. In Table 2, we can see that the
eigenvalues of the five factors are 6.64, 3.67, 3.34, 2.29 and
1.40, respectively. They are all greater than 1. The variance
contributions of the five factors are 0.36, 0.20, 0.18, 0.12
and 0.08, respectively. The accumulate variance
contribution of the total five factors is 0.93. These five factors are
sufficient to represent all of the variables.
3.2.1 The choice of the number of factors
We sorted the 20 factors in descending order of eigenvalue.
Among them, the first five factors’ eigenvalues are all
above 1. Table 2 shows that the accumulate variance
contribution of the fifth factor is 93%. It is thus reasonable
to choose the first five factors as the major price factors.
3.2.2 Matrix rotation of factors
The principle of each factor’s varimax should follow that
the variance of each variable’s square will be a maximum,
and no correlation is allowed between its factors.
3.3 Factor loading
Table 3 shows the factor loading after rotation. As listed in
Table 3, the high absolute value of factor loading means
that the variances of variable and factor overlap. The
positive or negative sign of the value represents the
difference of the change direction between variable and
factor. The loading of factor 1 is ordered in the descending
order of the absolute value—the pay level, industrial
production, GDP, personal consumption and stock market.
According to the naming principle of the maximum
loading, we name factor 1 as the economic factor. The variables
with comparatively low loading in the economic condition
factors are term premium, risk premium and total carbon
dioxide emissions. Thus, this naming is reasonable. Factor
2 has the biggest loading of term premium, risk-free
interest rate and unemployment rate. It is thus named the
interest rate factor. The variables with relatively low
loading include total coal consumption, gas consumption,
oil consumption, carbon dioxide emissions and gas
imports. Thus, it is reasonable to name it total energy
demand factor. Factor 4 has its biggest loading in
tradeweighted dollar index, oil price, unemployment rate and
CPI. The relationship between oil price and exchange rate
is negative. CPI is related to exchange rate, and thus, it is
named the dollar factor. The biggest loading of factor 5
appears in total gas consumption, and the loading is 0.93.
The other loadings are all small. This means that the factor
5 should be taken as an independent factor, and it is named
the gas consumption factor.
3.4 Significance test
Significance test results are listed in Table 4, where t stands
for t statistics while p value is the probability for the
statistical summary, which would be the same as or of greater
magnitude than the actual observed results given the null
hypothesis is true. Judging from the p value, except for the
interest rate factor, the other four tests reject the null
hypothesis. They have palpable effect upon the Henry Hub
gas price, and the interest rate factor and gas consumption
factor have opposite influences compared with others.
Considering the economic factor, when the economic
conditions are better (e.g., the higher the US industrial
production), the economic factor carries more weight.
Thus, the value 0.92 means that the gas price rises when
US economy is strong. Judging from the variables and
coefficient of the interest rate factor, the rise of risk-free
interest will lead to a rise in the gas price. The rise of risk
premium and term premium will lead to a decline in the gas
price. The interest rate factor and the gas price fluctuation
are independent, and the p value is 0.91. Thus, with the
10% significance level, the null hypothesis cannot be
rejected. The total energy demand factor includes
consumption of coal and oil, representing the amount of
carbon dioxide emissions and gas imports. The coefficient is
1.41; thus, a rise in total energy demand leads to a rise in
the gas price. The loading of the dollar index in the dollar
factor is negative, but the CPI is positive. The factor
coefficient is 0.21, and the p value is 0.04; thus, when
dollar appreciates, the gas price falls. And when CPI rises,
the gas price also rises. The coefficient of total gas
consumption is - 0.33. When total gas consumption rises, the
gas price reduces.
3.5 Variation trend of factors and price fluctuation mechanism
The four factors are shown in Fig. 1.
The total energy demand factor and the gas consumption
factor are obviously influenced by season. But the
mechanisms are different. The total energy demand includes oil
and coal, which are mainly used as industrial raw material
and power-generating resources. The largest demand
appears at November (back-to-school season) and
December (Christmas consumption season). This is
accordant with the US consumer habits. Thus, the choice
and naming of factors are satisfactorily reasonable. The
significance test shows that the total energy demand and
natural gas price are positively correlated. This means that
the substitutional relationship between alternative energy
and natural gas is not that obvious. The rise of other
alternative energy does not mean a decline in the demand
for gas. The impetus for alternative energy and gas
consumption is probably the total demand for energy. When
the total energy demand rises (including the gas demand),
the gas price will rise. The gas consumption factor reaches
its peak during January and March (winter) and reaches its
valley in summer. Its variation trend is opposite to that of
the total energy demand factor. But in winter, there is a
maximum due to the heating energy consumption. This
explains the difference existing among different kinds of
energy. Natural gas is mainly used for house heating and
cooking. It is also partly used as an industrial raw material.
The peak that appears each November shows that the rise
of demand for mass consumer product leads to the rise in
The economic factor rises in accordance with time, but
there is fluctuation sometimes. This is agreed with the
developing trend of the US economy. The influence of the
economic factor upon gas price is mainly like this—when
the economy is strong, the rise of personal expenditure and
demand of products will lead to a rise in demand for
energy. In this way, the gas price rises.
The dollar factor shows an obvious declining trend from
2002 to 2008. Before 2002, it reduces gradually. From
2002 to 2008, it rises gradually. After a short-term
fluctuation in 2008, it falls rapidly. The exchange rate is related
to the economy and financial markets, and also currency
policy and interest rates. The dollar exchange rate affects
the cost of imports and exports of gas. If the dollar
exchange rate rises, the nominal price of gas will decline.
The actual export price of gas will rise compared with that
of the overseas gas. A decline in demand lowers the gas
price. CPI is also another influential factor. It has a positive
correlation with gas price. WTI oil price makes up a large
part in the dollar factor. But considering the negative
correlation between oil price and exchange rate, the dollar
factor is mainly affected by the exchange rate.
3.6 Analysis of factor loading matrix
The analytical result of variables and factor loading matrix
is shown in Figs. 2, 3 and 4. A factor loading is the
correlation between a variable and a factor that has been
extracted from the data. From the factor loading matrix
analysis, we can see the relationship between a variable
and a factor obtained from orthogonal rotation. In Fig. 2,
when compared with other variables, the coal consumption,
oil consumption and carbon dioxide emissions are more
independent and irrelevant with the other variables. The US
GDP, industrial production, exchange rate and average pay
level are more closely related. These are the indexes to
measure US economy
(Lin and Wesseh 2013)
that are related to interest like the risk-free rate and term
premiums are also the same in their loading.
The path diagrams are shown in Fig. 5 when we focus
on the first three factors.
Fig. 5 Path diagram analysis (factor 1–factor 3)
This paper uses factor analysis to test 20 variables that may
influence the gas price from January 1997 to December
2016. Among them, five major factors are selected out
from the 20 variables—economic factor, interest rate
factor, total energy demand factor, dollar factor and gas
consumption factor. Linear regression analysis finds that
the gas price is mainly affected by natural gas demand,
alternative energy consumption, dollar exchange rate and
the economic condition. The interest rate factor only
influences the gas price little. Variables that represent the
economic conditions are similar in their relationship with
each factor, like US GDP, industrial production and
average pay level. The loading of coal consumption, oil
consumption and carbon dioxide emissions is similar
et al. 2015)
. The relationship between interest rate and
exchange rate is quite independent when compared with
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