2. (largest in the history). On the other side, natural gas production is also
not appreciable as it only increased from 2.3Bcm to 138.8Bcm (with the
increase of only 1.4% in 2016). Perhaps, because of the above-stated
situations, the country's consumer price index (CPI) reached highest in
last three years (i.e., 2.57% in 2017). Therefore, there is still need to
understand the relationships between commodity pricing, financial
development, energy markets and commodity markets (e.g., hard and
soft commodities) in a country.
The study has a novel contribution in the Chinese energy and re-
source markets through the intervention of financial development in-
dicators that supports hard and soft commodities by promoting mineral
rents, natural gas rents, oil rents, ores and metal exports, crop pro-
duction, livestock production, fisheries production, and food produc-
tion. The financial indicators, i.e., broad money supply, domestic credit
provided by financial sector, real interest rate, insurance and financial
services, and FDI inflows are further accessed to energy market in-
dicators, including, electric power consumption, fossil fuel energy
consumption, nitrous oxide emissions in energy sector, and energy ef-
ficiency in order to judge resource conservation agenda in a country.
The commodity prices served as intervening variables while the growth
–specific factors are used as control variables. The previous studies
disjointedly examined the effect of financialization in energy and re-
source markets, and largely limited the few indicators, for instance,
Irwin and Sanders (2011) analyzed future commodity prices and create
an index for fund investment, while Silvennoinen and Thorp (2013),
Cheng and Xiong (2014), Labban (2010), Olson et al. (2014) Main et al.
(2018), Xunpeng et al. (2019), and Wang et al. (2019) considered
stocks, bonds, and future commodity returns; price bubble and risk
uncertainty; oil scarcity markets and financialization; volatility in en-
ergy and equity indices; risk premium in commodity futures markets;
LNG commodity prices; and time varying volatility in natural gas prices
respectively. The study is first in its kind that used the number of stated
financial and resource markets in a single study with sophisticated
statistical technique that helpful to proposed sound policy inferences in
a given country context.
1.1. Review of past studies
1.1.1. Pricing mechanism in the markets
Allocations of resources are mechanized by pricing, because of this,
pricing is considered as the core variable between energy supply and
demand (He et al., 2016). Moreover, industrial energy consumption and
behaviors are the consequents of energy market prices. Whereas, the
non-market price may weaken the association between pricing and
resource allocation (Valadkhani and Babacan, 2014). Particular to
China, the energy pricing prior to 1978, have been government-re-
strained. Despite the fact that energy largely affects life and social
production, energy prices then were reformed and government was
relaxed regarding such intervention. In 1992, the government realized
the situation and introduced "social market economic system" as its
strategic goal to introduce market-price mechanism in energy in-
dustries. Because of the expansion of markets, the energy prices gained
more freedom. Thus, because of open-price, reforms and policies leads
to continuous rise in China's energy prices. Still, the energy prices in the
country were not fully relaxed comparing other developed countries in
the region.
During the same period, the prices of the energy in the country rose,
as there was an increase in the price of oil and coal. In 1993, the
government again decided to relax the price mechanism (particularly
for coal), in 2002, it was decided to rely on the market-oriented price
rather publishing guiding price of coal. Since then, due to the increased
demand for energy for mass level production, the coal and oil prices
continued to rose in the country. However, in 2009, the government
decided to make another reform regarding oil prices where both market
and government would determine the oil price. On the other side,
particular to the electricity pricing, China remained competitive.
Various studies have witnessed that energy prices due to its con-
sumption influence various aspects of the economy and one of the as-
pects is a gross domestic product (GDP). According to Aucott and Hall
(2014), GDP increases when energy costs are 5–6%, and it decreases
when energy costs are 10–12%. Another important focus of this study is
the association between energy consumption and general pricing level
(e.g., inflation and CPI). The literature is mixed regarding the associa-
tion between energy consumption and general pricing level, for ex-
ample, Jin et al. (2009) found no association and Irz et al. (2013) found
a significant association between the same. In particular, the associa-
tion between inflation and energy consumption varies across time-
period within the country (Hooker, 2002). These arguments generate
the need to understand the association between commodity prices and
energy markets in a single country.
Commodity prices may also affect natural resources, agricultural
products, and livestock. Previous researchers have clarified the asso-
ciation between energy and food, as both are dependent on each other
(Ghaith and Awad, 2011). The topic of commodity prices and food (e.g.,
gold to agriculture, oil prices to food and inflation to livestock etc.)
remained highlighted since long and had not yet been shed light in a
detailed manner. This urged the researchers to understand the asso-
ciation between commodity price and agriculture as well as natural
resources production. The world's population is growing rapidly (cur-
rent population is approximately seven billion) and according to world
population clock, this may reach to nine billion by 2042, which will
double the food demand by that time (United Nations, 2009). There-
fore, understanding the balance between commodity prices and soft and
hard commodities has become important than ever so that policies
could be established to cope the issues.
1.1.2. Financial development in the markets
Today's second-largest economy China started making reforms since
1978. These reforms were not only for a single sector but for all the
sectors of the economy. A major change was observed when the notion
"reform" itself was developed in 1992 to recognize the incompatibility
of market system with socialism. The country introduced the concept of
"socialist market economy" in which government owns and maintains
major products, whereas market mechanism governs economic in-
tegrations. This strategy worked for the country to continue its growth
to be the world's second-largest economy. During the financial crises
2011, the country's economy was slowed down, however, in 2017, the
economy seemed to be back. According to IMF (2017) report, China's
forecasted growth would be 6.8% because the country performed
stronger than expectations.
Economic development and financial development are dependent
upon each other, where financial development is a multidimensional
variable. Financial development, according to Hussain and
Chakrabortry (2012) comprised of bank's increased financial services
including domestic credit, interest rate, broad money, insurance and
foreign direct investment (FDI). Literature has largely focused Amer-
ican and European countries understanding the association between
financial development and growth, and suggested a developed financial
economy can lower information cost, help sound allocation of resources
and help in adoption of latest technology (Shahbaz et al., 2010).
However, such studies on Asian countries (like China) are scant.
The relationship between financial development and energy con-
sumption remained the topic of researchers' interest for a decade and
there is still need to develop consensus on the ongoing debate. Karanfil
(2009) suggested pricing might be the mechanism between interest and
exchange rates and energy consumption, following which, Dan and
Lijun (2009) noted causality between financial development and energy
consumption. These studies opened new ways for the future re-
searchers, where some studies found small (Shahbaz and Lean, 2012)
and other found large associations (Mehrara and Musai, 2012) between
the two variables. However, Shahbaz et al.'s (2016) thought strikes that
financial development may affect energy through consumption and
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
241
3. production channel and found a bidirectional association between the
same.
Financial development along with energy consumption can also be
related to the agricultural production. According to Ghosh et al. (2012),
financial speculation is the reason behind increased prices for agri-
culture and livestock. Since long, the agriculture commodities and li-
vestock prices remained stable, but mayhem exaggerated the associa-
tion between finance and agricultural commodities (Clapp et al., 2017).
Growing agricultural commodities made it difficult to differentiate
between financial and agricultural sector (Fairbairn, 2014). Clapp et al.
(2017) then contributed in the existing literature that, pension funds,
investments, and banks are contributing towards farmland investment.
However, a natural disaster can harm agricultural commodities and
livestock. However, the role of financial development in saving agri-
cultural commodities is essential. Countries with greater domestic
credit and high insurance rates can save their agricultural commodities
from natural disaster to maintain economic growth (Keerthiratne and
ToI, 2017). Otherwise, it may lead to the bank's default (Klomp, 2014).
This study argues that financially developed sector can positively in-
fluence on agricultural commodities. Table –A in appendix shows the
recent strikes of literature in a given context for a ready reference.
The above stated studied confirmed the important role of financia-
lization in energy and commodity markets, which is largely attributed
by government regulations to channelize market mechanism in a way to
reduce market externalities. Thus, the current study examined the role
of financialization in energy and commodity markets under different
explained factors, including trade and financial openness, commodity
prices, per capita income, insurance and financial services, and in-
dustrial value added in a context of China for conclusive findings.
2. Data source and methodological framework
The study used number of factors to display financialization in en-
ergy and commodity markets, i.e., commodity markets contains both
the hard and soft commodities, while hard commodities represented by
natural resources and soft commodities represented by agricultural
products and livestock. The commodity markets contain total 8 factors,
energy markets contain 4 factors, financial development represented by
5 factors, commodity prices shown by 2 factors, and 3 controlled
variables are used to analyze growth-specific factors in China. The data
missing values is filled by preceding and succeeding variable values,
where required. The detail of the variables is shown in Table 1 for ready
reference.
2.1. Theoretical framework
Financialization in the commodity markets largely discussed in
terms of commodity futures asset pricing, which is hardcore debate for
portfolio investors such like bonds and stocks to stabilize stock market
uncertainty, however, sustainability in the commodity markets rarely
discussed in economic and environmental agenda, which is the need to
devise strong policies of green financing for sustainable growth. Storm
(2018) discussed the viability of social influence of financialization in
economic development that transmitted from banks to financial mar-
kets and allows rent-seeking practices in the elite globalized world.
Thus, the financial markets should serve as a mediator to improve
country's welfare in order to progress in economic and social order via
modern financing techniques. Jerneck (2017) argued that green fi-
nancing is imperative for long-term sustainability that could achieved
with solar energy demand, which supports corporate environmental
social responsibility agenda that impedes climate change and its miti-
gation through innovation. Endogenous growth theory discussed the
salient features of country's economic growth that holds investment in
human social infrastructure via spending on education, health, in-
novation, and knowledge diffusion. The knowledge based economy
gives positive externality towards economic prosperity via social
subsidies and R&D expenditures. The Cobb-Douglas production func-
tion gives three strands of output, i.e.,
=
Q AK L1
(1)
Where, ‘Q’ shows country's economic output, ‘A’ shows technology’, ‘K’
represent capital stock in the form of investment, ‘L’ represents labor
force, and α shows economies of scale.
The economic output may either be capital augmented or labor
augmented or technology neutral, thus these three strands of economic
output amplify economic opportunities to proceed for long-term sus-
tained growth.
Equation (1) can be simplified by adding financial factors that
support country's economic growth, however, the process of knowledge
diffusion from state actors to financial actors compromised largely to
environmental factors, which affect country's sustainability agenda. The
modified equation can be presented as follows:
=
Q A FD CP
( ) ( )1
(2)
Where, ‘FD’ shows financial development and CP shows commodity
prices.
Equation (ii) clearly shows that financial factors and commodity
prices support country's economic growth on the cost of environmental
degradation, thus the energy and commodity markets influenced with
unregulated financial activities, which need green financing infra-
structure for long-term growth. The equation (ii) further modified for
resource management, i.e.,
=
RM A FD CP
( ) ( )1
(3)
Where, ‘RM shows resource management.
The economy of China is largely devoted financial resources in order
to optimize energy and commodity markets, while its further expand
trade liberalization policies that is one point agenda for country's vision
to trade all across the globe. The rapid pace of industrialization largely
influenced the country's sustainability agenda, which cumbersome the
country's destination of zero carbon emissions. This debate attracts to
explore the financialization in energy and commodity markets, which is
associated with resource balance, optimization of energy resources, and
marginal increase in agricultural products and livestock. The following
two equations is used to empirical analyze the impact of financial de-
velopment on energy and commodity markets in a given country con-
text, i.e.,
= + + + +
EM FD CP CV
( ) ( ) ( ) ( )
t t t t t
0 1 2 3 (4)
= + + + +
CM FD CP CV
( ) ( ) ( ) ( )
t t t t t
0 1 2 3 (5)
Where, EM shows energy market (4 energy factors), FD shows financial
development (5 financial variables), CM shows commodity markets (4
factors for hard commodities and 4 factors of soft commodities), CP
shows commodity prices (2 price indicators), CV shows control vari-
ables (3 growth specific factors), ‘t’ shows time period from 1967 to
2016 (50 annual observations), and shows error term.
Equation (iv) and Equation (v) is further decomposed in to a main
regression equation to analyze simultaneity among the regressors by
two step Generalized Method of Moments (2 step GMM), called si-
multaneous equations GMM modeling technique, i.e.,
Model: Impact of Financialization on Energy and Commodity
Markets
= + + + +
+ + +
+ + + + +
ENRG COMM MARKET M DCPFS RIR IFS
FDI GDPDEF CPI
GDPPC IND TOP z
_ _ 2
0 1 2 3 4
5 6 7
8 9 10
(6)
Where, ENRG_COMM_MARKET shows energy and commodity market
indicators, i.e., energy demand (EPC), fossil fuel energy consumption
(FFUEL), nitrous oxide emissions (N2O), energy efficiency (EF), mineral
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
242
4. rents (MRENTS), natural gas rents (NGRENT), oil rents (ORENTS), ores
and metal exports (OME), livestock production index (LVPI), crop index
(CROPI), food production index (FOODPI), and total fisheries produc-
tion (TFISHP). ‘z’ shows instrumental variables list, and is error term.
Fig. 1 shows the research framework of the study.
Fig. 1 shows the number of possible channels through which fi-
nancial indicators and commodity prices influenced energy and re-
source markets intervening through growth –specific factors in a given
country. It is hypothesized that financial indicators may have a differ-
ential impact on energy and resource markets to manage natural
Table 1
List of variables.
Source: World Bank (2017).
Variables Symbol Measurement Expected Sign Remarks
Commodity Markets (Dependent Variables) a) Hard Commodities (Natural Resources)
Mineral Rents MRENT % of GDP Natural resources influenced by financial development and growth-specific factors in the form of
high resource rents.
Natural Gas Rents NGRENT % of GDP
Oil Rents ORENT % of GDP
Ores and metals exports OME % of merchandise exports
b) Soft Commodities (Agricultural Products and Livestock)
Crop production index CROPI 2004–2006 = 100 Financialization support to increase agricultural and livestock products in the form of high yield of
crops production, livestock production, and total fisheries production, which ultimately increase
food production in a country.
Livestock production index LVPI 2004–2006 = 100
Total fisheries production TFISHP Metric tons
Food production index FOODPI 2004–2006 = 100
Energy Markets (Dependent Variables)
Electric power consumption EPEC kWh per capita Energy markets required more financial assistance in order to generate electric power consumption;
however, it is imperative to reduce the dependency of fossil fuel energy that may affect
environmental sustainability agenda in the form of high N2O emissions. The alternative way is to
improve energy in terms of high GDP per unit of energy that balanced the energy supply and demand
in a country.
Fossil fuel energy consumption FFUEL % of total energy demand
Nitrous oxide emissions in
energy sector
N2O % of total energy demand
GDP per unit of energy use EF Constant 2011 PPP $ per kg of
oil equivalent
Independent Variables
- Financial Development
Broad money M2 % of GDP Positive Financial sector acts like a catalyst to promote resource markets and energy markets
that is imperative for long-term sustained growth.
Domestic credit provided by
financial sector
DCPFS % of GDP Positive
Real Interest Rate RIR % Positive
Insurance and financial services IFS % of service exports, BoP Positive
Foreign direct investment, net
inflows
FDI % of GDP Positive
- Commodity Prices
Inflation, GDP deflator GDPDEF Annual % Negative Higher the commodity prices lead to decrease economic activities, in the form of
natural resource depletion, low agricultural and livestock products, and low energy
infrastructure.
Inflation, consumer prices CPI Annual % Negative
Controlled Variables
GDP per capita GDPPC Constant 2010 US$ Positive Economic activities supported both the energy markets and commodity markets via
the channel of adequate resource rents, high agricultural yields, and optimize
energy resources in a country.
Industry value added (% of
GDP)
IND % of GDP Positive
Trade Openness (% of GDP) TOP % of GDP Positive
Fig. 1. Research framework of the study.
Source: Author's self extraction based on previous literature.
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
243
5. resources and delimit anthropogenic activities through developing a
green financing infrastructure, while commodity prices support in-
dustrial value added and trade liberalization policies that negatively
influenced energy and resource markets. Thus, the commodity prices
should be flexible in terms of balancing the energy shortfalls and im-
proves energy efficiency through green production.
The study used simultaneous equations GMM estimator, which gives
more robust inferences as compared to the single estimated GMM es-
timator. The single GMM estimator is conventionally used in number of
studies, i.e., Cömert et al. (2010), Daskalaki (2012), Haile and Kalkhul
(2013), Ott (2014), Baum and Zerilli (2016), Brooks et al. (2016), etc.
The two step GMM is comparable a better instrument then the con-
ventional one and remove simultaneity from the set of regressors by
appropriate instrumental list. The equations listed above have a same
exogenous factors with multiple response variables, with partially
mediate to adopt the same instrumental lists for each equations among
the regressors The Hansen J-statistic provides the basis of appro-
priateness of instruments being used in this study that over identifying
the restrictions on the lagged regressors. Ullah et al. (2018) developed
some generic STATA codes for using GMM estimator to better control
three sources of endogeneity, i.e., i) unobserved heterogeneity, ii) si-
multaneity, and iii) dynamic heterogeneity. There are number of pro-
blems arises in social science research especially where the errors are
largely depicted in measurement, although it overcome with somehow
by adopting structural equations modeling technique, however, its still
need robust techniques for single indicators to reduce measurement
errors. The multiple dimension of constructs in modeling framework
largely used in empirical studies, however, there is still a chance of
omission bias of the major variables from the model that needs to check
the reliability of the constructs, validity, and exploratory factor ana-
lysis. In a similar fashion, two variables could be used simultaneously in
a single model, thus simultaneity is major issue that lead to biased in-
ferences. The emergence of GMM estimator resolved the above stated
issues of measurement, omission bias, and possible simultaneity in a
given model. Arellano and Bond (1991) and Blundell and Bond (1998)
included lagged dependent variable as a regressor in the model to re-
duce possible endogeneity, thus they developed GMM estimator for
dynamic panel modeling. There are two types of GMM estimator, i.e., i)
the ‘first difference transformation, usually called one-step GMM and ii)
second order transformation, usually called two-step GMM estimator.
The main limitation of one –step GMM estimator is that if the data is
unbalanced then the first difference transformation leads to loss too
many observations that could affect the variables’ coefficient estimates.
Thus, to avid this issue, two –step GMM estimator is the optimized
solution to provide efficient and consistent estimates in balanced panel
dataset. The following pre-requisite tests could be used to detect pos-
sible endogeneity issues that leads to biased estimates if the dynamic
panel estimation procedure is not fully adopted, i.e., first start with
simple multiple regression model and identify possible simultaneity
from Durbin-Wu-Hausman test followed by fixed effect estimator. The
failure of this estimator caused due to dynamic endogeneity, thus GMM
estimator can be used by adding the initial value of dependent variable
in regressors lists to address endogeneity issue. This study used two
–step GMM estimator to handle possible endogeneity issues from the
given models.
3. Results and discussions
Table 2 shows the descriptive statistics and correlation matrix and
found that inflation calculated by consumer price index is about to
6.082% on average with a maximum increase in 24.237%, while in-
flation calculated by GDP deflator is about 3.801% on average, which is
far lower than the CPI in a country. The agricultural and livestock
products is represented by crop production index with an average value
of 68.187, food production index value is about 63.053, livestock
production index is about 57.543, and total fisheries production is
about 29310163 metric tons on average. The crop production has
greater index values, which specify its importance in the soft com-
modities. The hard commodities represented by natural resource rents,
i.e., mineral rents is about 0.594% of GDP on average, natural gas rents
is about 0.054% of GDP, oil rents has a value of 2.564% of GDP, and
ores and metal exports is about 1.955% of merchandise exports on
average. Thus, it is quite visible that hard commodities have a large
dispersion in the value added, as oil rents have a larger value relative to
the GDP, followed by ores and mineral rents that is nearly about 2% of
the GDP. The financial development factors, including domestic credit
that have an average value of 92.512% of GDP, broad money supply by
94.367% of GDP, FDI inflows have a value of 2.081% of GDP, insurance
and financial services have a value of 4.929% of exports, and real in-
terest rate has an average value of 1.857%. The soundness of fi-
nancialization is depicted by the larger financial values relative to their
GDP share, thus it accompanied with the sound banking system, money
supply, financial liberalization policies, and insurance sector in a
country. Energy market is highly inflamed by country's economic ac-
tivities and environment sustainability agenda, which is imperative for
long-term sustained growth. The electricity production is about
1121.024 kwh per capita on average, while energy efficiency is about
3.124 PPP US$ per kg oil equivalent, fossil fuel dependency is about
75.327% of total energy consumption, and N2O emissions in energy
sector is about 7.601% of total energy consumption on average. Thus,
energy market profile is holistic in terms of compromised environ-
mental sustainability agenda, which is flared with high mass de-
pendency of fossil fuel demand and N2O emissions produced by energy
sector. The policies should be entrenched with environmental friendly
for sustainable development in a country. The country's per capita in-
come is US$6894.464 at maximum, while industry value added and
trade openness have a corresponding average value of 44.007% of GDP
and 29.522% of GDP respectively. The high industrial value added and
substantially favorable trade liberalization policies optimistically may
impact positively on energy and commodity markets in a given country.
These statistics give a rough estimate about variable's trend during the
study time period.
Table 2, panel –B shows the correlation estimates and found that
financialization have a differential impacts on energy and commodity
markets, as domestic credit have a positive correlation with the crop
production index (r = 0.961), food production index (r = 0.960), li-
vestock production index (r = 0.957), and total fisheries production
(r = 0.959), similarly, FDI inflows and broad money supply both have a
positive correlation with the agricultural commodities in a country.
Insurance sector largely influenced the agricultural commodity markets
due to non-durable items, which uncovered the insurance policy. The
commodity prices decreases agricultural and livestock products, as in-
flation have a negative correlation with the crop production
(r = −0.455), food production (r = −0.461), livestock production
(r = −0.455), and total fisheries production (r = −0.471), while GDP
deflator has a weak but positive correlation with crop production
(r = 0.117), food production (r = 0.119), livestock production
(r = 0.140), and total fisheries production (r = 0.070). The growth
specific factors, including per capita GDP and trade openness exert a
high and positive correlation with the agricultural and livestock pro-
ducts, while industry value added further confirm the positive and
moderate correlation with the soft agricultural commodities.
Financialization exert a multiple impact on hard natural resource
commodities, as domestic credit exert a positive correlation with the
mineral rents (r = 0.439) while it has a negative correlation with the
other three natural resources, i.e., natural gas rents (r = −0.061), ores
and metals exports (r = -0.660), and oil rents (r = −0.361). FDI exerts
a positive correlation with the mineral rents while negative correlation
with the other three natural resources. There is a negative correlation
between insurance and mineral rents while positive correlation with the
natural gas rents, ores and metal exports, and oil rents. There is a po-
sitive correlation between broad money supply and mineral rents, while
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
244
7. negative correlation with the other three natural resources. Inflation
largely decreases mineral rents and natural gas rents, while it increases
ores and metal exports and oil rents. GDP deflator has a low but positive
relationship with natural resources in a country. GDP per capita exert a
positive correlation with mineral rents and natural gas rents while ne-
gative correlation with the remaining two natural resources.
Industrialization has a positive correlation with the three natural re-
sources except ores and metals exports where it tends to show a ne-
gative correlation with it. Trade openness substantially increases mi-
neral rents, while it decreases the other three natural resources in a
country.
The energy market is highly responsive against the financialization,
commodity prices and growth specific factors, as domestic credit shows
a positive and high correlation with the energy efficiency (r = 0.939),
electricity production (r = 0.891), fossil fuel (r = 0.927), and N2O
emissions (r = 0.725), while FDI inflows further tend to increase energy
efficiency (r = 0.633), electricity production (r = 0.462), fossil fuel
energy (r = 0.733), and N2O emissions (r = 0.378). Insurance and fi-
nancial services largely decrease energy factors, as the correlation
coefficient shows the negative correlation between them. One of the
justified reasoning is that energy market is highly unpredictable as its
required massive energy to regulate economic functioning of the
countries, hence the wide fluctuations in the energy markets uncovered
the high costs of insurance sector, which is highly unlikely to see the
positive impact of insurance sector on energy markets. Money supply
although exert a positive correlation with the energy products, as it
largely increases energy efficiency, electricity production, fossil fuel
energy and N2O emissions by energy consumption. The commodity
prices, i.e., consumer price index negatively influence the energy
market, while GDP deflator substantially decreases energy efficiency
and N2O emissions by energy demand, while it increases electricity
production and fossil fuel energy consumption. The growth specific
factors, including per capita income, industry value added, and trade
openness substantially increases energy products in a country. Figure
–A and Figure –B in appendix shows the level ad first differenced plots
respectively.
After analyzing the trend analysis of the selected variables, the
study further proceeds to estimate coefficient parameters by simulta-
neous equations GMM estimator. Table 3 presented the estimates for
energy markets. The results show that electricity production largely
affected by domestic credit provided to the financial sector and change
in commodity prices in terms of high consumer price index, as if there is
one unit increase in domestic credit and CPI, electricity production
decreases by −6.074(p < 0.0000) and −7.183(p < 0.000) unit,
while electricity production increases by high values of real interest
rate, GDP deflator, per capita income, and trade openness. Trade
openness is a larger share in terms of increase per unit change in
electricity production among the growth specific factors, while real
interest rate has a greater share among the financial factors and GDP
deflator among the commodity price market. Thus, it is worth noted
that sound economic growth and trade liberalization policies sub-
stantially improves electricity production under the sound banking
system where change in real interest rate matters for expedite the
process of growth specific factors on energy market. Although, com-
modity prices in terms of CPI largely influenced the electricity pro-
duction, however, it may reduced by expansionary monetary policy by
adopting flexible interest rate by the Central bank to give loans to the
commercial banks. Carmona and Coulon (2014) advocated for flexible
energy markets where energy prices support to the commodity markets
in terms of gaining adequate payoffs to the government, which helpful
to meet the energy demands and increased fuel capacity in a country.
Zhang et al. (2017) argued that financialization in stock market, oil,
and natural gas gives strong connection between them due to the di-
vergent behaviors of stock market on crude oil and natural gas, which
need to construct an integrated financial modeling to reduce the dy-
namic volatility of stock market in given energy profiles. Olson et al.
Table
2
(continued)
Panel-A
IFS
IND
LVPI
M2
MRENT
N2O
NGRENT
OME
ORENT
RIR
TFISHP
TOP
MRENT
−0.336
0.259
0.600
0.524
1.000
N2O
−0.519
0.136
0.809
0.768
0.745
1.000
NGRENT
0.131
0.421
−0.035
−0.065
0.234
0.045
1.000
OME
0.455
−0.088
−0.661
−0.670
−0.318
−0.630
−0.067
1.000
ORENT
0.340
0.350
−0.395
−0.399
−0.109
−0.423
0.822
0.309
1.000
RIR
−0.326
−0.040
0.114
0.149
−0.202
0.000
−0.051
−0.115
−0.037
1.000
TFISHP
−0.755
0.278
0.991
0.986
0.578
0.840
−0.046
−0.680
−0.426
0.140
1.000
TOP
−0.802
0.455
0.909
0.896
0.568
0.666
−0.031
−0.463
−0.274
−0.037
0.861
1.000
Source:
World
Bank
(2017)
and
Author's
estimation.
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
246
8. (2014) found that financialization in the form of equity returns to the
energy market have a differential low and high returns in the developed
country, as dynamic volatility is reported in energy market due to low
shocks in equity return, while energy shocks substantially affect equity
markets in vice versa. The study concludes with the empirical fact that
energy is poorly hedged in the developed equity market. Manera et al.
(2016) concluded that speculation in the energy markets largely ham-
pered the commodity markets, as it destabilize the energy prices, in-
cluding oil, natural gas, and gasoline.
The results further elaborate the impact of financialization on fossil
fuel energy demand, which reveals that broad money supply, real in-
terest rate, GDP deflator, and growth specific factors largely induce
fossil fuel consumption, while FDI inflows helpful to determine the
reduction of fossil fuel dependency in a country. The results show that if
there is one unit increase in the money supply, real interest rate, GDP
deflator, per capita income, industrialization, and trade openness, it
substantially increases fossil fuel energy about 0.100 units, 0.520,
0.646, 0.0008, 0.421, and 0.133 units respectively. The size of the per
capita income coefficient is comparatively smaller in terms of in-
creasing fossil fuel energy, while high in commodity prices and real
interest rate, which confined that contractionary monetary policy may
increases the demand of fossil fuel energy, while industrialization and
trade liberalization policies further increases fossil fuel dependency in a
country. Wen et al. (2014) discussed the viability of new energy and
fossil fuel stocks in a country profile and argued that both the energy
mix are considered as competing assets, however, new energy is com-
paratively in higher risk to the fossil fuel energy demand, thus financial
risk management is imperative for energy policy making. Altvater
(2009) concluded that dependency of fossil fuel energy largely re-
sponsible for climatic changes due to high mass carbon emissions
during the combustion process of fossil energy, hence strict environ-
mental regulations are desirable for sustainable development. Tumen
et al. (2016) criticized the fossil fuel usage taxation under the macro-
economic variables and argued that this taxation is subject to some
policy induced challenges, which derives interest rate and inflation in
an economy. This gain is limited in terms of welfare loss that brought
up by fossil taxation; hence it need fair environmental mechanism to
cater this uncertainty for welfare gains.
The energy sector produced nitrous oxide emissions, which is fairly
influenced by financial development, commodity prices, and growth
specific factors. The results derive that growth specific factors, in-
cluding per capita income and trade openness significantly increases
N2O emissions, while commodity prices, domestic credit, and real in-
terest rate decreases N2O emissions in a country. The results conclude
that tight monetary policy is effective to reduce energy associated N2O
emissions, which is deem desirable for sustainable development, how-
ever, growth specific factors compromised the sustainability agenda in
the form of high emissions of N2O that is cumbersome for long-term
sustained growth. Bond (2012) described the role of Kyoto protocol in
emissions trading, which is highly induced by market mechanism
system that includes financial liberalization policies of a country.
Cooper (2015) argued that there is a dire need of environmental fra-
mework to reduced emissions trading, which could be better to include
market based instruments for policy redefining in metrological systems.
Stuart and Schewe (2016) identified some structural barriers that hin-
ders against the agricultural development in a developed economy and
argued that local commodity markets enterprises should need to re-
concile their decisions that directly linked with the farmers, while
agriculture food sector required more concentrated efforts to break-
down concentrated power for mutual gains, thus the efforts to reduce
climate mitigation is highly aligned with the development strategies for
ethical gains.
Finally, energy efficiency in terms of GDP per unit of energy use is
analyzed under certain financial and growth specific factors and found
that broad money supply, FDI inflows, and per capita income positively
impact on energy efficiency, while domestic credit to the foreign sector,
real interest rate, and GDP deflator largely decreases energy efficiency
in a country. The result implies that contractionary monetary policy
and commodity prices highly put a strain on energy market, while fi-
nancial liberalization in the form of foreign attractiveness and sound
country's economic growth speedup the process of energy financiali-
zation to sustained commodity market growth. Buyuksahin and Robe
(2011) concluded the dynamic correlations between stock market in-
dices and energy returns under the premises of general speculators and
hedge funds. The results generally specified the need of future energy
demand, which is vital component of energy financialization in the
commodity markets. Lueg et al. (2015) found that corporate sustain-
ability is the promising solution to build a low cost business model,
which supports the stakeholder values. The policies to re-design energy
and commodity markets in terms of ‘go-to-green’ policies is desirable,
where environmental conservation is mandatory for sustainable de-
velopment. MarcJoëts (2015) argued that energy market is associated
with high price uncertainty in the extreme shocks time period where
heterogeneous traders affected with the rapid change in energy market
prices that destruct the energy dynamics model. The policy to device
energy modeling is desirable in conjunction with the financialization in
energy markets for sustained growth. Table 4 shows the estimates of
simultaneous equations modeling by GMM estimator for Chinese com-
modity markets.
The financialization in hard commodity markets fairly aligned with
the commodity prices, financial development, and growth specific
factors. The results show that mineral resource rents is decreases by
domestic credit, insurance and financial services, and FDI inflows,
among which FDI inflows largely decreases by mineral resource rents
(r = −0.207, p < 0.001), followed by insurance and financial services
(r = −0.092, p < 0.011), and domestic credit (r = −0.039,
p < 0.000). The positive impact of GDP deflator, per capita income,
and trade openness is visible for mineral resource rents, among which
GDP deflator shows greater magnitude followed by per capita income
and trade openness in a country. Industrialization and GDP deflator
both positively influenced to natural gas rents, while commodity price
in terms of inflation decreases natural gas rents. Oil rents decreases by
broad money supply, FDI inflows, and price level, while it increases by
domestic credit, real interest rate, insurance and financial services, GDP
deflator, and industrial value added. Ores and mineral exports increases
by higher price level and trade openness, while it decreases by in-
surance and financial services, FDI inflows, and GDP deflator in a
country. Ruta and Venables (2012) argued that natural resources are
one of the fundamental components of production that is largely in-
fluenced by commodity prices. Additionally, it is one of the dominant
sources of exports in many developing countries, which may protect
from taxation, price monitoring and control and production quotas. It is
evident that international policies for natural resource conservation are
remains in disequilibrium that tend to shows market failure in the hard
commodities. To get a better payoff for both the resource exporting and
importing countries, it is desirable for coordinated policy efforts re-
quired between the countries. Labban (2010) discussed four main fac-
tors of financial markets that give it shaped to worked under oil market,
including, oil scarcity, markets production, financial investment, and
commodity prices. These factors are helpful to draw the mechanism
through which oil markets affected with commodities' financialization.
Cheng and Xiong (2014) discussed the importance of financialization in
commodity future markets in terms of price distortions and price bub-
bling. The study concludes with the fact that financialization changed
commodity markets through risk sharing and information discovery
mechanism. Van der Ploeg and Venables (2011) concluded that natural
resource wealth is vital for countries gain, however, many countries
does not get and able to create it from development and growth. The
wide fluctuations in the revenues due to unpredictable commodity
market prices may less secure to extend its benefits in promoting
country's growth and development. Table 5 shows the estimates of si-
multaneous equations by GMM estimator for soft commodities.
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
247
9. The soft commodities comprises livestock production, crop pro-
duction, food production, and total fisheries production that influenced
by financial development, commodity prices, and growth specific fac-
tors. The results show that broad money supply and growth specific
factors contributed largely to increase livestock production index,
among which broad money supply exert a larger share that contributed
around 0.808 units per unit increase in the livestock production, fol-
lowed by trade openness that have a magnitude value about 0.260
units, industrialization share is about 0.259 unit, and per capita income
is 0.003 units. Domestic credit provided to foreign sector, however,
decreases livestock production index with an estimated value of
−0.378 units in a country. In a similar way, crop production index
largely influenced by domestic credit, insurance and financial services,
and FDI inflows while it substantially increases by broad money supply,
real interest rate, GDP deflator, and growth specific factors. The results
imply that monetary policy is helpful to determine a significant increase
in crop production, which further aligned with high per capita income,
industrial value added, and trade liberalization policies. Food produc-
tion index is supported by broad money supply while it decreases by
insurance and financial services and FDI inflows. The growth specific
factors further expanded the food production base by sound economic
growth, high industry value added, and trade liberalization policies,
thus it reduces food security concerns in a country. Fisheries production
is affected by tight monetary policy in the form of high charging real
interest rate that imposed to the commercial banks, which further re-
stricted by less domestic credit given into the commodity market. The
broad money supply, FDI inflows, per capita income, and higher price
level supports to increase total fisheries production in a country.
Nazlioglu et al. (2013) argued that the correlation is stable in the pre-
crisis period between oil and agricultural commodity prices, while in
the post scenario, there is a wider divergent been observed between the
two factors (except in sugar commodity). The inter-temporal causation
is reported between these two factors in the post-crisis period, while it
remains stable in the normal times. Thus, it is concluded that agri-
cultural commodity prices are highly inductive in oil regime, which
should be balanced by integrated policy framework. Du et al. (2011)
discussed the volatility between the crude oil and agricultural com-
modity market, which is largely visible after the fall of 2006. The three
Table 3
Simultaneous equations GMM estimator for energy markets.
Variables/Models Coefficient Standard Error z-statistics P > z [95% Confidence Interval]
SEM-1: EPC = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant 237.762 122.7045 1.94 0.053 −2.73443 478.2584
M2 0.901295 1.487341 0.61 0.545 −2.01384 3.81643
DCPFS −6.0745 1.015655 −5.98 0 −8.06514 −4.08385
RIR 10.31854 4.363781 2.36 0.018 1.765691 18.8714
IFS 9.544351 6.494716 1.47 0.142 −3.18506 22.27376
FDI −12.4309 11.05859 −1.12 0.261 −34.1053 9.243555
GDPDEF 12.57842 3.82388 3.29 0.001 5.08375 20.07309
CPI −7.18371 3.590862 −2 0.045 −14.2217 −0.14575
GDPPC 0.685416 0.018627 36.8 0 0.648908 0.721924
IND −2.36475 2.468809 −0.96 0.338 −7.20353 2.474023
TOP 7.271943 1.310331 5.55 0 4.703741 9.840144
SEM-11: FFUEL = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant 43.94647 2.833111 15.51 0 38.39368 49.49927
M2 0.100294 0.03434 2.92 0.003 0.032989 0.1676
DCPFS −0.02523 0.023451 −1.08 0.282 −0.0712 0.020729
RIR 0.521301 0.100756 5.17 0 0.323823 0.718779
IFS −0.18481 0.149956 −1.23 0.218 −0.47872 0.109099
FDI −0.88159 0.25533 −3.45 0.001 −1.38202 −0.38115
GDPDEF 0.646609 0.088289 7.32 0 0.473566 0.819653
CPI −0.07292 0.082912 −0.88 0.379 −0.23543 0.089583
GDPPC 0.000868 0.00043 2.02 0.044 2.52E-05 0.001711
IND 0.421539 0.057004 7.39 0 0.309812 0.533265
TOP 0.133261 0.030256 4.4 0 0.073961 0.192561
SEM-111: N2O = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant 8.162215 0.799697 10.21 0 6.594838 9.729592
M2 −0.01173 0.009693 −1.21 0.226 −0.03073 0.007265
DCPFS −0.01887 0.006619 −2.85 0.004 −0.03184 −0.0059
RIR −0.20341 0.02844 −7.15 0 −0.25915 −0.14767
IFS 0.03263 0.042328 0.77 0.441 −0.05033 0.115591
FDI 0.103227 0.072071 1.43 0.152 −0.03803 0.244484
GDPDEF −0.16099 0.024921 −6.46 0 −0.20984 −0.11215
CPI −0.04206 0.023403 −1.8 0.072 −0.08793 0.003807
GDPPC 0.001105 0.000121 9.11 0 0.000867 0.001343
IND −0.01367 0.016091 −0.85 0.396 −0.04521 0.017868
TOP 0.061645 0.00854 7.22 0 0.044906 0.078383
SEM-1V: EF = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant 1.198419 0.375264 3.19 0.001 0.462915 1.933923
M2 0.02301 0.004549 5.06 0 0.014095 0.031925
DCPFS −0.00744 0.003106 −2.4 0.017 −0.01353 −0.00136
RIR −0.05807 0.013346 −4.35 0 −0.08422 −0.03191
IFS 0.002496 0.019863 0.13 0.9 −0.03643 0.041426
FDI 0.062991 0.03382 1.86 0.063 −0.00329 0.129277
GDPDEF −0.07572 0.011695 −6.48 0 −0.09864 −0.0528
CPI 0.007237 0.010982 0.66 0.51 −0.01429 0.028761
GDPPC 0.000177 0.000057 3.1 0.002 6.49E-05 0.000288
IND 0.010915 0.007551 1.45 0.148 −0.00388 0.025714
TOP −0.00475 0.004008 −1.19 0.236 −0.01261 0.003101
Note: Test of over identifying restriction by Hansen's J chi-square test, i.e., χ2
(0) = 2.5e-27. SEM shows simultaneous equations modeling.
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
248
10. main causation of crude oil volatility is identified that includes spec-
ulation prices, scalping, and petroleum inventories, which largely in-
fluenced the agricultural future commodity markets. Silvennoinen and
Thorp (2013) estimated the dynamic correlation between stocks, bonds,
and commodity futures return, and found the visible heterogeneity
among the variables, that largely accompanied with state financial
variables. Clapp and Helleiner (2012) concluded that global financial
crisis influenced food market derivatives, which is stabilized by gov-
ernment regulations and financial soundness in a developed country.
The study argued that although financialization in agricultural com-
modity market is blamed for high commodity food prices, while it's
optimize by strategic policies to get better payoffs.
The results of simultaneous equations modeling by GMM estimator
give a facility to impose restrictions on over-identification of the in-
strumental lists, hence in this regard; we check the Hansen's J-statistics,
which is merely based upon chi-square statistics. The Chi-square sta-
tistics shows insignificant at 5% level of confidence in all three pre-
scribed models, including energy market model, hard commodities
model, and soft commodities model, thus its clearly exhibit that the
prescribed instrumental lists is valid and gives conclusive findings.
Table 6 shows the values of variance inflation factor (VIF) and auto-
regressive (AR) serial correlation test at first and second lagged values
in order to detect the multicollinearity issues and simultaneity issues
from the given models respectively.
The results show that VIF values are less than the threshold value of
10, thus we confirm that the given model has no serious multi-
collinearity issue and the parameter estimates are consistent and effi-
cient.
4. Conclusions and policy implications
The energy and commodity markets highly reactive against the
price changes that affect energy efficiency, fossil fuel energy demand,
electricity production, natural resource rents, and agricultural and li-
vestock products. China is no exception that faces similar issues in the
energy and commodity markets through price volatility; however it
strives hard to balance through higher economic growth, strict mone-
tary actions, and trade and financial liberalization process. This study
Table 4
Results of simultaneous equations modeling for hard commodities.
Variables/Models Coefficient Standard Error z-statistics P > z [95% Confidence Interval]
SEM-V: MRENT = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant 1.828439 0.688471 2.66 0.008 0.479061 3.177817
M2 0.009188 0.008345 1.1 0.271 −0.00717 0.025544
DCPFS −0.03976 0.005699 −6.98 0 −0.05093 −0.02859
RIR 0.003126 0.024485 0.13 0.898 −0.04486 0.051114
IFS −0.0927 0.03644 −2.54 0.011 −0.16413 −0.02128
FDI −0.20767 0.062047 −3.35 0.001 −0.32928 −0.08606
GDPDEF 0.058865 0.021455 2.74 0.006 0.016814 0.100916
CPI 0.00092 0.020148 0.05 0.964 −0.03857 0.04041
GDPPC 0.000645 0.000105 6.17 0 0.00044 0.00085
IND 0.003555 0.013853 0.26 0.797 −0.0236 0.030706
TOP 0.031849 0.007352 4.33 0 0.017439 0.046259
SEM-VI: NGRENT = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant −0.37015 0.103026 −3.59 0 −0.57208 −0.16822
M2 −0.0011 0.001249 −0.88 0.38 −0.00354 0.001352
DCPFS 0.000229 0.000853 0.27 0.788 −0.00144 0.001901
RIR 0.001267 0.003664 0.35 0.73 −0.00591 0.008448
IFS 0.006111 0.005453 1.12 0.262 −0.00458 0.016799
FDI −0.01121 0.009285 −1.21 0.227 −0.02941 0.006991
GDPDEF 0.008576 0.003211 2.67 0.008 0.002283 0.014868
CPI −0.00878 0.003015 −2.91 0.004 −0.01469 −0.00287
GDPPC 0.000024 1.56E-05 1.54 0.124 −6.62E-06 5.47E-05
IND 0.010712 0.002073 5.17 0 0.006649 0.014775
TOP 0.000159 0.0011 0.14 0.885 −0.002 0.002315
SEM-VII: ORENT = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant −14.9465 3.604497 −4.15 0 −22.0112 −7.88184
M2 −0.0925 0.04369 −2.12 0.034 −0.17814 −0.00687
DCPFS 0.055433 0.029836 1.86 0.063 −0.00304 0.113911
RIR 0.326305 0.128189 2.55 0.011 0.075059 0.577551
IFS 0.176346 0.190784 0.92 0.355 −0.19758 0.550277
FDI −0.5518 0.32485 −1.7 0.089 −1.18849 0.084898
GDPDEF 0.507504 0.112328 4.52 0 0.287345 0.727663
CPI −0.30573 0.105487 −2.9 0.004 −0.51248 −0.09898
GDPPC 0.000594 0.000547 1.09 0.277 −0.00048 0.001667
IND 0.424202 0.072525 5.85 0 0.282056 0.566348
TOP 0.034049 0.038493 0.88 0.376 −0.0414 0.109495
SEM-VIII: OME = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant 2.008641 0.650019 3.09 0.002 0.734628 3.282654
M2 −0.00384 0.007879 −0.49 0.626 −0.01929 0.011598
DCPFS 0.00104 0.005381 0.19 0.847 −0.00951 0.011586
RIR 0.011469 0.023117 0.5 0.62 −0.03384 0.056777
IFS −0.05919 0.034405 −1.72 0.085 −0.12662 0.008245
FDI −0.10043 0.058582 −1.71 0.086 −0.21525 0.014388
GDPDEF −0.04825 0.020257 −2.38 0.017 −0.08796 −0.00855
CPI 0.080536 0.019023 4.23 0 0.043251 0.11782
GDPPC −0.00013 9.87E-05 −1.29 0.198 −0.00032 6.65E-05
IND 0.001066 0.013079 0.08 0.935 −0.02457 0.0267
TOP 0.01905 0.006942 2.74 0.006 0.005445 0.032656
Note: Test of over identifying restriction by Hansen's J chi-square test, i.e., χ2
(0) = 1.8e-27.
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
249
11. aims to review the financialization in energy and commodity markets
for the given country context by using last 50 years annual data for
robust inferences. The results show that financial development largely
supported the energy and commodity markets through contractionary
monetary policy, reduced domestic credit, and large financial inflows in
the form of foreign direct investment in a country. The higher com-
modity prices distorted energy and commodity markets by decreasing
electricity production, natural gas rents, oil rents, and ores and mineral
exports, while it increases total fisheries production. The per capita
income, industrial share to GDP and trade openness significantly im-
proves energy and commodity market symmetric behavior. The study
concludes with some short-term, medium-term, and long-term policy
implications in the given country context, i.e.,
- Short-term Policy Implications:
It is overwhelming debate that price volatility is subject to the fi-
nancialization in energy and commodity markets, while it is necessary
to find a mechanism through which financialization may affect/change
the price level, hence in order to absorb this phenomena, the policy
makers have advice to reconsolidate the existing market mechanism
policies to intervene with government regulations through strict
monetary actions in a country. The financial intermediaries may sig-
nificantly influence the commodity market prices, thus it is imperative
to stabilize it by substantial insurance policies and banking instruments,
which supports the business-as-usual criteria. The financial and trade
liberalization policies may further entrenched per unit cost of energy
use that mutually adjust by country's given terms of trade. The resource
markets further be improved by domestic credit provided to the fi-
nancial sector and insurance policy that gives incentives to the stake-
holders to get economic gains from resource rents in a country. The
agricultural and livestock products supported by increase broad money
supply that needs expansionary monetary policy to sustained their ef-
forts in receiving economic gains.
- Medium-term Policy Implications:
The soundness of growth specific factors largely supported the
Table 5
Results of simultaneous equations for soft agricultural commodities.
Variables/Models Coefficient Standard Error z-statistics P > z [95% Confidence Interval]
SEM-IX: LVPI = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant −6.1365 4.594205 −1.34 0.182 −15.141 2.867976
M2 0.808494 0.055689 14.52 0 0.699346 0.917642
DCPFS −0.38727 0.038029 −10.18 0 −0.46181 −0.31273
RIR −0.17297 0.163387 −1.06 0.29 −0.4932 0.147264
IFS −0.38798 0.24317 −1.6 0.111 −0.86459 0.088622
FDI −0.46709 0.414054 −1.13 0.259 −1.27862 0.344443
GDPDEF −0.20444 0.143171 −1.43 0.153 −0.48505 0.076169
CPI 0.21642 0.134451 1.61 0.107 −0.0471 0.479939
GDPPC 0.003843 0.000697 5.51 0 0.002476 0.00521
IND 0.259526 0.092439 2.81 0.005 0.078349 0.440702
TOP 0.26001 0.049063 5.3 0 0.163849 0.356172
SEM-X: CROPI = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant 10.38099 4.412497 2.35 0.019 1.732655 19.02933
M2 0.445167 0.053484 8.32 0 0.34034 0.549994
DCPFS −0.22755 0.036524 −6.23 0 −0.29914 −0.15597
RIR 0.723238 0.156925 4.61 0 0.415671 1.030805
IFS −0.44145 0.233552 −1.89 0.059 −0.8992 0.016303
FDI −1.19203 0.397671 −3 0.003 −1.97145 −0.4126
GDPDEF 0.637995 0.137508 4.64 0 0.368484 0.907506
CPI −0.03572 0.129133 −0.28 0.782 −0.28882 0.217374
GDPPC 0.00882 0.00067 13.17 0 0.007507 0.010132
IND 0.363325 0.088783 4.09 0 0.189314 0.537336
TOP 0.218341 0.047122 4.63 0 0.125983 0.310699
SEM-XI: FOODPI = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant 5.076772 3.82152 1.33 0.184 −2.41327 12.56681
M2 0.590197 0.046319 12.74 0 0.499414 0.68098
DCPFS −0.30299 0.031632 −9.58 0 −0.36498 −0.24099
RIR 0.189012 0.135906 1.39 0.164 −0.07736 0.455384
IFS −0.4523 0.202271 −2.24 0.025 −0.84874 −0.05586
FDI −0.93723 0.344401 −2.72 0.007 −1.61224 −0.26222
GDPDEF 0.146291 0.119091 1.23 0.219 −0.08712 0.379705
CPI 0.064326 0.111838 0.58 0.565 −0.15487 0.283523
GDPPC 0.006981 0.00058 12.04 0 0.005844 0.008118
IND 0.294686 0.076892 3.83 0 0.143981 0.44539
TOP 0.268743 0.040811 6.59 0 0.188755 0.348731
SEM-VIII: TFISHP = f(M2, DCPFS, RIR, IFS, FDI, GDPDEF, CPI, GDPPC, IND, TOP)
Constant −7600691 4052123 −1.88 0.061 −1.55E+07 341324
M2 297681.8 5119.804 58.14 0 287647.1 307716.4
DCPFS −83949.1 13691.23 −6.13 0 −110783 −57114.8
RIR −314770 133218.9 −2.36 0.018 −575874 −53665.5
IFS −202222 209173.6 −0.97 0.334 −612195 207750.9
FDI 1338065 259459.6 5.16 0 829533.3 1846596
GDPDEF −677893 119772.3 −5.66 0 −912643 −443144
CPI 246474.9 113797.5 2.17 0.03 23436.01 469513.8
GDPPC 5150.803 320.6669 16.06 0 4522.307 5779.299
IND 142376.5 85548.21 1.66 0.096 −25294.9 310047.9
TOP 38229.36 38947.75 0.98 0.326 −38106.8 114565.5
Note: Test of over identifying restriction by Hansen's J chi-square test, i.e., χ2
(0) = 7.2e-19.
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
250
12. country's vision to surplus exports of energy and commodity markets,
while it's required strategic thinking to reduce market externalities on
one hand and stabilize market actors on the other hand to improve
energy efficiency, corporate sustainability, resource rents, and agri-
cultural production. The country should required to optimize risk
sharing behavior that support commodity markets to reduce market
failure, while symmetric information will gives new discovery channel
through which price bubble view should be cater with price less view
that supports to increase economies of scale. Thus, the energy and
commodity markets required sound economic development that im-
proves market efficiency and market regulatory affairs in a country.
- Long-term Policy Implications:
The results conclude with the fact that volatility in the commodity
prices largely be the transformation phase of financialization in the
commodity market, which should be stabilized through government
price regulations in the form of monetary actions that adopted central
bank for monitoring and balancing the money supply in a country.
Commodity prices should be stabilizing by expansionary and contrac-
tionary monetary policies. Government regulations in the commodity
markets will helpful to stabilize market prices in a country. Soundness
of financial indicators could improves energy and commodity markets
through the channel of risk sharing and information discovery. Bank
based instruments may helpful to reduce market uncertainty and pro-
vides substantial loans to expand commodity markets. Insurance and
financial services may provide a full or partial coverage of the goods
traded, even for commodity markets, where the nature of goods may
not be durable as much as the other products, hence the short-term
insurance policies for less time may cover the risk associated hurdles of
the market to perform well in a desired capacity. Price Speculation for
future market commodities may further increase price hikes of small
and medium term market size firms, while large firm size, although
affected with the price bubbling, however, it may sustained through
fiscal and monetary measures. The energy and commodity markets
should improve product portfolio in which the investors may attract
and get maximum payoffs by investing in their desired products. It will
get two benefits, first, the market size will be enlarged and secondly it
attracts other investors to invest in this market for competitive gains.
The trade and financial openness in the commodity market is the op-
timized solution to reduce high price hikes in the commodity markets.
GDP deflator helpful to assess the real economic growth of the energy
and sub-markets in order to attract foreign private capital in a country,
and Developed financial market supports energy and commodity
market for economic gains. These policies may helpful to sustained
country's economic activities to support energy and commodity markets
by enlarge product portfolios and attract foreign investors to stabilize
government regulated prices. Thus, the financialization process is
regulated by government actions that helpful to determine a market
basket of energy and agricultural goods that backed up by the sup-
portive prices of a country.
Acknowledgements
The authors extend their appreciation to the Deanship of Scientific
Research, King Saud University, Saudi Arabia for funding this work
through research group no. RG-1437-027.
Appendix
Table A
Recent Literatures on Financialization in Energy and Resource Markets
Author's name Country Time Period Methodology Key Findings
Ouyang and Li (2018) China 1996Q1-2015Q GMM panel VAR approach FD↑EG↓
EC↑EG↑
FD↑EC↓
EC→FD
Destek (2018) 17 emerging economies 1991–2015 Common correlated effect estimator FD↑EC↓
Al Mamun et al. (2018) 25 OECD countries 1980–2015 Pooled mean group estimator FD↑CENRG↑
GFC↑CENRG↓
Al-Mulali and Sab (2018) UAE 1980–2008 VECM Granger causality EC→EG
EC↔CO2
CO2→FD
Liu et al. (2018) China 1980–2014 VECM Granger causality FD↔EC
FD→EG
Shahbaz et al. (2018) France 1955–2016 Bootstrapping ARDL Model
Table 6
Autoregressive (AR) -serial correlation test and VIF estimates.
Models SEM-1 SEM-11 SEM-111 SEM-1V SEM-V SEM-V1 SEM-VI1 SEM-VII1 SEM-1X SEM-X SEM-X1 SEM-XI1
AR(1) 0.000 0.269 0.691 0.002 0.000 0.269 0.337 0.101 0.003 0.015 0.008 0.884
AR(2) 0.000 0.922 0.602 0.039 0.026 0.000 0.095 0.000 0.000 0.006 0.030 0.718
VIF Values for Financialization in Energy and Commodity Markets
M2 2.237 Note: The probability values reported in SEM-1 to SEM-XII for AR(1) and AR(2).
DCPFS 1.167
RIR 5.429
IFS 9.969
GDPDEF 9.806
CPI 7.798
GDPPC 0.013
IND 1.681
TOP 1.833
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
251
13. FDI↑CO2↑
ERI↑CO2↓
CO2ΩEG
Paramati et al. (2018) 23 developed and 20 emerging market economies 1992–2011 Common correlated effect estimator CO2ΩFD
Adams and Klobodu (2018) 26 African countries 1985–2011 GMM estimator EG↑CO2↑
URB↑CO2↑
FD↑CO2↑
Sharif et al. (2019) 74 countries 1990–2015 FMOLS NREC↑CO2↑
REC↑CO2↓
FD↑CO2↓
CO2ΩEG
Mahmood et al. (2018) Saudi Arabia 1971–2014 Asymmetric analysis CO2ΩEG
FD↑CO2↓
Ali et al. (2018) Nigeria 1971–2010 ARDL estimator EG↑CO2↑
FD↑CO2↑
EC↑CO2↑
TOP↑CO2↓
Zakaria and Bibi (2019) 5 South Asian countries 1984–2015 2SLS CO2ΩEG
EG↑CO2↑
EC↑CO2↑
FD↑CO2↑
Shahbaz et al. (2019a) Vietnam 1974–2016 VECM Granger causality CO2ЙEG
Naz et al. (2019) Pakistan 1975–2016 Robust least square regression EG↑CO2↑
FDI↑CO2↑
REC↑CO2↓
Khan et al. (2019) 7 Asian countries 2005–2017 Panel random effect model NRD↑MIG↑
NDISASTER↑MIG↑
Shouket et al. (2019) Pakistan 1975–2016 ARDL model RPC↑CO2↑
ART↑NRD↑
TOP↑CO2↑
Hanif et al. (2019) 15 Asian countries 1990–2013 ARDL model EG↑CO2↑
FDI↑CO2↑
CO2ΩEG
Zafar et al. (2019) 16 APEC countries 1990–2015 FMOLS R&D↑EG↑
REC↑EG↑
NREC↑EG↑
Baloch et al. (2019) 59 countries 1990–2016 Driscoll-Kraay panel regression FD↑EFP↑
EG↑EFP↑
EC↑EFP↑
FDI↑EFP↑
URB↑EFP↑
Shahbaz et al. (2019b) 87 countries 1970–2012 Cross correlation approach GLOBALΩCO2 (in 16 countries)
Shahbaz et al. (2019c) 1990–2015 GMM estimator CO2ЙFDI
CO2ЙEG
BIOENRG↑CO2↓
FDI→CO2
EG↔CO2
BIOENRG↔CO2
Note: FD shows financial development indicators, EG shows economic growth, EC shows energy consumption, GFC shows global financial crisis, CENRG shows
cleaner energy production, CO2 shows carbon dioxide emissions, ERI shows energy research innovations, URB shows urbanization, NREC shows non-renewable
energy consumption REC shows renewable energy consumption, TOP shows trade openness, NRD shows natural resource depletion, MIG shows external migration,
NDISASTER shows natural disaster, RPC shows railways passengers carried, ART show air-railways transportation, R&D shows research and development ex-
penditures, EFP shows ecological footprints, GLOBAL shows globalization, BIOENRG shows biomass energy, ↔ shows bidirectional causality, → shows unidirectional
causality, ↓ shows decrease, ↑ shows increase, Ω shows inverted U-shaped EKC relationship, and Й shows N-shaped EKC relationship between the variables.
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
252
14. Fig. A. Level Data Plots. Source: World Bank (2017).
H.U. Rashid Khan, et al. Resources Policy 62 (2019) 240–255
253
15. Fig. B. Differenced Data Plots. Source: World Bank (2017). ‘D’ shows first difference.
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