International Journal of Energy Economics and
Policy
ISSN: 2146-4553
available at http: www.econjournals.com
International Journal of Energy Economics and Policy, 2019, 9(3), 229-243.
Is there a “Reverse Causality” from Nominal Financial Variables
to Energy Prices?
Roberto J. Santillán-Salgado1, Alí Aali-Bujari2, Francisco Venegas-Martínez3*
1
Instituto Tecnológico y de Estudios Superiores de Monterrey, EGADE Business School, Mexico, 2 Universidad Autónoma de Estado
de Hidalgo, Escuela Superior de Apan, México, 3Instituto Politécnico Nacional, Escuela Superior de Economía, México.
*Email: fvenegas1111@yahoo.com.mx
Received: 02 January 2019
Accepted: 22 March 2019
DOI: https://doi.org/10.32479/ijeep.7526
ABSTRACT
This paper is aimed at examining the association between energy prices and financial variables, but, in contrast to previous works, it explores the
possibility of a reverse causality from financial variables towards energy prices from a global perspective considering the world’s four largest world
economic poles (the United States, China, the European Union, and Japan), as well as the prices of oil (Brent) and Natural Gas. In order to study the
interaction between energy prices and relevant nominal variables (stock market returns, interest rates, and exchange rates), a panel vector autoregression
analysis is carried out. The empirical finding is that Brent oil and natural gas price fluctuations are positively and highly significantly influenced by
lagged interest rates, that is, energy markets are sensitive to monetary policy signals and, most likely, to economic agents’ expectations about inflation.
Other empirical results also reveal that: (1) Lagged exchange rate fluctuations have a negative and significant effect over the stock market; (2) a
positive performance of the stock market has a negative effect on the exchange rate, and: (3) That interest rate markets follow their own dynamics
independently of the rest of the model variables.
Keywords: Energy Prices, Stock Market Returns, Interest Rates, Exchange Rates
JEL Classifications: G10, G15, E43, F31
1. INTRODUCTION
In recent years, a large number of empirical studies have addressed
the relationship among international oil prices, financial markets
and economic variables. Among those studies, one of the most
prolific strands of the literature centers on the subject of the
influence of oil prices on stock markets performance; for example:
Chen et al. (1986), Jones and Kaul (1996), Chiou and Lee (2009),
Arouri et al. (2011), Demirer et al. (2015), Abhyankar et al.
(2013), Aloui and Aïssa (2016), Aali-Bujari et al. (2018), and other
relevant and thoroughly explored research area has to do with the
relationship of energy prices and financial and economic variables.
For example: On the incidence of oil prices on interest rates and
exchange rates (e.g., Bal and Rath, 2015; Selmi et al., 2015; Kim
and Jung, 2018; Aali-Bujari, et al., 2017; and Salazar-Núñez and
Venegas-Martínez, 2018; 2018b); on macroeconomic variables,
both at a domestic and at a global level, (e.g., Hamilton, 1983;
Barsky and Killian, 2004; Hamilton, 2009; Ozturk, 2010;
Blanchard and Galí, 2010; Chatrath et al., 2012; and Ahmadi et al.
2016); on channels of interaction between oil prices and different
economic sectors (e.g., Davis and Haltiwanger, 2001; Brown and
Yudel, 2002; Lardic and Mignon, 2008; and Wattanatorn and
Kanchanapoom, 2012); on the relationship between oil prices and
monetary policy response (e.g., Bernanke et al., 1997; Kilian and
Lewis, 2011; and Bodenstein et al. 2013); and, still others, have
argued that the behavior of oil prices is not completely exogenous,
but that it responds to a variety of stimuli that simultaneously
affect the world economy and the energy markets (Barsky and
Killian, 2004).
This Journal is licensed under a Creative Commons Attribution 4.0 International License
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Santillán-Salgado, et al.: Is there a “Reverse Causality” from Nominal Financial Variables to Energy Prices?
In spite of the above abundant research on the subject, until now
very little attention has been paid to explore the possibility of a
reverse causality between nominal financial variables and energy
prices. This work empirically addresses such relationship by
considering a global perspective that includes the world’s four
largest economies or economic areas (the United States, China,
the European Union, and Japan), and the prices of oil (Brent) and
Natural Gas. In order to study the interaction between energy
prices, and relevant financial variables (stock market returns,
interest rates, and exchange rates), this research carries out a panel
vector autoregression analysis (PVAR) (Holtz-Eakin et al., 1988;
Love and Zicchino, 2006; and Abrigo and Love, 2016).
The main empirical finding is that Brent Oil and Natural Gas price
fluctuations are positively and highly significantly influenced
by lagged interest rates. That is, energy markets are sensitive to
monetary policy signals and, most likely, to economic agents’
expectations about inflation.
The obtained empirical results also reveal that: (1) Lagged
exchange rate fluctuations have a negative and significant effect
over the stock market returns; (2) a positive performance of the
stock market has a negative effect on the exchange rate, and;
(3) interest rate markets follow their own independent dynamics,
that is, they do not respond to the rest of the model variables’
influence. Additionally, Brent oil lagged fluctuations have a
negative effect on next day’s Brent fluctuations, and lagged Natural
Gas fluctuations have a positive influence on oil. Finally, Natural
Gas prices are negatively influenced by lagged Brent oil prices and
positively influenced by their own lagged price changes.
Based on several tests, it is possible to conclude that the influence
that energy prices have over the stock market, the exchange rate,
and the interest rates of the four largest world economic poles is
neither statistically nor economically significant, in agreement
with, for example, Chen et al. (1986), Huang et al. (1996), Apergis
and Miller (2009), Ghosh and Kanjilal (2016), and Zhang (2017),
among others.
Thos paper is organized as follows. The following section presents
a brief literature review, classifying several representative studies
on the influence of energy prices on financial nominal variables
according to their economic context. Section 3 discusses the world
economic environment and the evolution of the variables of under
study during the sample period (January 5, 2005 through December
30, 2016). Section 4 addresses the methodological issues, the
database, the estimation results and their interpretation and
discussion. Finally, section 5 concludes and highlights important
implications of the empirical findings.
2. A BRIEF LITERATURE REVIEW
An argument initially put forward in the work of Miller and Ratti
(2009) postulates that crude oil is an important input for a large
variety of industries and, for that reason, its price fluctuations
directly affect the cost composition of many productive processes.
Natural gas price fluctuations play a similar role, probably on a
narrower spectrum of economic activities, but with very similar
230
consequences. Since energy price changes have a direct effect on
corporations’ cash flows, they should have an incidence on their
stock performance in the market. Nonetheless, the interrelationship
between the effects of energy prices on economic activity and the
stock market performance of publicly traded companies’ shares can
only be separated conceptually for analytical purposes since they
effectively interact all the time. However, only a few studies have
captured their real-world complex and complementary interaction.
One possible explanation of that analytical bias has to do with
the fact that energy prices not only affect the cost structure of
productive activities, but also have consequences over inflation
and monetary policy, as well as a significant influence on other
commodity market prices, each deserving special attention.
The interest of the present study is on the association between
energy prices and financial variables but, in contrast to previous
studies, it explores the possibility of a reverse causality from
financial variables towards energy prices. For that reason, the
aims of this literature review are purposely circumscribed to some
representative studies that analyze how do energy prices interact
with the stock market, the exchange rates and interest rates, and
briefly refers to the few studies that suggest the possibility of an
influence from nominal variables over energy prices.
2.1. Oil Price and the Stock Market Relationship in
the United States
To the present, most published empirical studies on the relationship
between oil prices and the stock market have been centered on
the United States markets, which is not surprising given the large
number of public firms that exist and the abundant availability
of detailed statistical and financial information. However, more
recently, the interest on how energy prices interact with financial
markets has widened to include the most developed and an
increasing number of emerging countries.
Among the first studies that incorporate the impact of oil prices on
financial assets prices is that of Chen et al. (1986) that finds that
a number of macroeconomic and financial factors representing
sources of risk for the stock market are significantly priced by the
stock market, including spreads between long and short interest
rates, inflation (expected and unexpected), industrial production,
and the risk premium spread between high- and low-grade bonds.
However, they also find that oil price volatility is not independently
rewarded in the market.
According to Huang et al. (1996) the prominent role that oil plays
in the economy and the politics of industrialized countries justifies
the large number of studies that have been devoted to the study of
energy and its effects on the macroeconomics. Among the most
relevant studies are those on the negative relation between oil
price increases and real GNP (as documented by Hamilton, 1983;
Mork et al.1, 1994; and Gilbert and Mork2, 1986). However, the
consequences of energy shocks on financial markets had been
scarcely researched.
1
2
Mork et al. (1994) extend their conclusions to other countries different
from the United States.
Gilbert and Mork (1986) analyze the macroeconomic effects of oil supply
disruptions.
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Huang et al. (1996) study the impact of oil price shocks on the
United States stock market from the perspective of the dynamic
interactions between oil futures prices traded on the New York
Mercantile Exchange and stock prices, using a multivariate VAR
model to explore the possibility of lead, lag and feedback effects.
The possible linkages between oil prices and stock market are
examined at three levels: First, for a wide stock price index,
the S&P 500 index; second, for twelve equally weighted stock
price indices based on stocks classified according to the first
two digits of their SIC codes; and, third, for three individual oil
company stock price series (Chevron, Exxon, and Mobil). Their
conclusions are that, contrary to the often-cited relevance of oil
for economic activity, there is limited evidence that a link exists
with the stock market, except for oil companies, for which they
report the presence of significant Granger causality from oil
futures (something to be expected). Huang et al. (1996) conclude
that given the lack of correlation between oil futures and stock
markets, the former represent an attractive vehicle for diversifying
stock portfolios.
Sadorsky (1999), on the basis of the results of a VAR model with
monthly data for the United States, reports that oil prices and oil
price volatility combine to affect real stock returns, and that the
structural nature of the relationship changed after 1986. As of
that moment, the reported evidence shows that oil price changes
explain a larger fraction of the error variance in real stock returns
than interest rates. This study also reports evidence that oil price
volatility shocks have asymmetric effects on the economy. The
findings of Ciner (2001) are consistent with those from Sadorsky
(1999) regarding the influence of oil shocks on stock returns,
and challenge the findings of Huang et al. (1996). In effect, he
argues that Huang et al. (1996) conclusions are due to the fact
that they only focus on linear dependencies, and tests for linear
and nonlinear causality between oil futures and the S&P500 for
the 1980s (same data as Huang et al., 1996) and for the 1990s to
find that oil price shocks do affect the returns of the S&P500 in a
nonlinear fashion. Notably, this study is among the few that finds
that there is a feedback relation between the S&P500 index and
oil futures prices during the 1990s.
On the other hand, Chiou and Lee (2009) examine the asymmetric
effects of oil price changes on stock returns and explore the
importance of structural changes in that relationship. Using daily
data on the S&P500 and the price of West Texas Intermediate oil
from 1992 to 2006, they incorporate expected and unexpected oil
price fluctuations into a model of stock returns. These authors also
explore the way that oil price volatility can influence the stock
market. Based on the results of an Autoregressive Conditional
Jump Intensity model with structural changes, they report that
high oil price fluctuations have unexpected asymmetric impacts
on the S&P500 returns.
Based on the results of a time-varying transition-probability
Markov-switching model to characterize bull versus bear markets
as a function of oil prices, Chen (2010) explores if higher oil price
increases the probability that the stock falls into bear territory.
The results of their analysis, which are tested for robustness,
suggests that increases in oil prices raise the probability of a bear
market emergence. Their work also finds that further increases in
oil price also augment the probability that the market remains in
a bear mood.
The work of Narayan and Sharma (2011) is exceptional in the
sense that instead of studying the relationship between oil prices
and the stock market, it explores the relationship between oil
price and 560 individual firms listed in the NYSE with the aim to
determine whether oil prices affect different sectors of economic
activity differently. They use three versions of a GARCH (1,1)
model and report that, indeed, oil price fluctuations affect the price
of different economic sector stocks differently. These authors
also report strong statistical evidence of oil price effects on stock
returns with lags for all the firms in the sample which, in a sense,
corroborates that there is underreaction to information arrival in
the short-run, i.e., “the effect of information is felt after some
time” (as in Hong and Stein, 1999), and that the lagged effect
is maximized at two lags for six productive sectors including
chemicals, electricity, general services, manufacturing, supply, and
transport. Another objective proposed in their paper is to examine
if the relationship between oil price changes and individual firm
stock returns is related with firm size, which they attempt by
subdividing their sample in size-quartiles and then calculating
the relative frequency with which oil price is found to have a
statistically significant positive and negative effects on a firm’s
stock returns in each quartile. Along with the main findings, the
paper reports that, in most cases, small firms stock returns are
positively related with oil price, contradicting the initial findings
that for the whole sample, in which the relation is negative. Also,
as the size of firms increases, for firms in which oil price has a
statistically significantly negative relationship, the magnitude of
such relationship grows by a factor of three times.
Kang et al. (2015) estimate the impact of oil price shocks on
the return and volatility of the United States stock market by
constructing, from daily observations, the covariance of return
and volatility on a monthly basis. They measure daily volatility as
realized volatility, conditional volatility from a stochastic volatility
model, and implied-volatility deduced from options prices, and
find that positive shocks to aggregate demand are associated with
negative effects on the covariance of returns and volatility, while
supply disruptions have positive effects on the covariance of
returns and volatility. Their study also reports a large and highly
statistically significant spillover index between oil price shocks
and the covariance of stock returns and volatility.
Narayan and Gupta (2015) study monthly time series of oil prices
and the United States stock market for a period of 150 years
(from October 1859 to December of 2013). This exceptional
characteristic in terms of the length of the observation period
makes it the first analysis of its kind. These authors are interested
on the relationship between oil price and stock returns, and engage
in an analysis based on a time-series predictive regression model
originally developed by Westerlund and Narayan (2012) and
Westerlund and Narayan (2015). This modeling is capable of
accommodating the persistency and endogeneity of oil prices,
and any heteroscedasticity present in the regression model. The
analysis is conducted both in-sample and out-of-sample and tests
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Santillán-Salgado, et al.: Is there a “Reverse Causality” from Nominal Financial Variables to Energy Prices?
whether oil price nonlinearly is capable of predicting stock returns.
The main findings of their work are that oil price “is a persistent
and endogenous predictor variable,” and the hypothesis of no
stock return predictability can be rejected. Also, find that while
oil prices predict the returns of the United States stock market,
that predictability is “non-linear” in the sense that negative oil
price changes predict stock returns better than positive changes.
As is clear from the sample of studies reviewed in this section,
most of them find that oil shocks have a negative impact on the
United States stock markets.
2.2. Regional Studies on the Relationship between Oil
Prices and the Stock Market in Developed Countries
Several studies that address the relationship between the dynamics
of energy prices dynamics and the performance of stock market
have followed either a multinational perspective or a more
regional/country focus, widening the available evidence on the
interaction between these variables. In what is already considered
a classical study on the subject, Jones and Kaul (1996) revise
whether the response of international stock markets returns to
shocks in oil prices is justified by changes to current and future
real cash flows and expected returns. They find that, during the
years after the Second World War, the response of the United States
and Canadian stock markets to oil shocks may be completely
accounted for by cash flow and expected return changes induced
by oil shocks. However, that is not the case in the United Kingdom
and Japan, where oil price shocks induce a greater response than
that attributed to expected changes in cash flows and returns.
In a country level study, Papapetrou (2001) attempts to clarify
the dynamic relationship among oil prices, real stock prices,
interest rates, real economic activity and employment in Greece
by running a multivariate VAR model. The results indicate that
oil price changes have an influence on real economic activity and,
as a consequence, on employment levels. The empirical evidence
suggests that oil price changes affect real economic activity and
employment and they are also capable to explain stock price
movements.
Park and Ratti (2008) argue that if oil price shocks affect consumer
and firm’s behavior (as documented in, for example, Hamilton,
1983; Barsky and Killian, 2004; Blanchard and Galí, 2010; and
Chatrath et al., 2012), it follows that the effects of oil price shocks
should be reflected on the world stock markets. To contrast their
hypothesis, they use a multivariate VAR model with linear and
non-linear specifications of oil shocks, and study the impact of oil
price shocks on the stock returns of the United States and thirteen
European stock markets, from 1986 to 2005, to find that there
is a significant contemporaneous association (and next-month
association) between the two variables. They also insist on the
convenience to consider the effect of oil price fluctuations in
different national markets in order to identify cross-country effects
that may be systematic in nature. Interestingly, they report that in
the case of Norway, an oil exporting country, there is a positive
statistically significant real stock market return response to oil price
advances, consistent with the logic that the country receives larger
rents when oil prices increase. By contrast, for many European
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countries’ stock markets, the response to increases in oil price
volatility is negative.
Lescaroux and Mignon (2008) investigated the relationship
between oil prices and different macroeconomic and financial
variables for a large sample of countries that includes oil-importers
and oil-exporters. These authors study both short- and long-run
relationships by means of Granger-causality tests, and evaluate
cross correlations between the cyclical components of the series
to detect leads and lags, and to carry out a cointegration analysis.
The main findings reported include evidence on the short-run link
between oil prices and the stock market, as well as the presence of
Granger-causality from oil prices towards other macroeconomic
variables in the long-run.
According to Driesprong et al. (2008), oil price fluctuations
represent a reliable predictor of stock market returns, and
the evidence reported in their work indicates that significant
predictability is found in both developed and emerging markets.
Using stock market data from 48 countries, a world stock market
index, and prices of different types of oil, they find that stock
returns are lower after oil price increases and higher when oil prices
fall in the previous month. They report that this predictability
is not only statistically significant but also economically
significant. Instead of accepting the argument that such results
are a consequence of time-varying risk premia, they argue that
their evidence is consistent with an underreaction hypothesis “as
it appears to take time before information about oil price changes
becomes fully reflected in stock market prices,” and suggest that
their findings can be explained under the “gradual information
diffusion hypothesis” of Hong and Stein (1999).
Apergis and Miller (2009) study specific structural shocks that,
characterize oil prices fluctuations as endogenous impact stockmarket returns in 8 industrialized countries. First, they decompose
unexpected real oil-price changes into mutually orthogonal
components and classify them as produced by oil-supply shocks,
global aggregate-demand shock, and global oil-demand shocks,
and then they run VAR and VECM models that includes global
oil production, global real economic activity, and real oil prices,
to estimate what are the effects of structural shocks on the stock
market returns. The reported results in their study suggest that the
sample stock market returns are not sensitive enough to oil market
shocks. i.e., they are significant but small in magnitude.
Ghorbel et al. (2014) examine behavioral contagion between oil
prices, the United States stock market, and stock markets of oilimporting and oil-exporting countries during the oil shock and the
Global Financial Crisis of 2008-2009, with a tri-variate BEKKGARCH model that includes the VIX, oil prices and returns of
stock market indices of 22 oil-importing and -exporting countries,
adding up the United States. They examine the spillover of
volatility between oil market prices and stock market, and find that
there exists a volatility spillover of American investor sentiment
towards the stock market returns and the oil market returns. The
pure contagion effects between the oil market and stock markets
are captured by using the Kalman filter, independent of the
macroeconomic fundamental factors. By analyzing the dynamic
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correlation between the forecasting errors of oil price returns and
the stock indices returns, these authors find there was a sharp
increase in the time-varying correlation coefficients during the oil
crisis and the global financial crisis, which represents supporting
evidence of the idea that there was a herding-behavior contagion
between the oil market and the several stock markets in the sample
during the period of analysis. Specifically, the authors consider
investor-sentiment and herding-bias to explain the volatility
transmission between oil and stock market returns.
An alternative approach that explains oil price shocks effects,
considering both world oil production and world oil prices, with
the aim to disentangle oil supply from oil demand shocks is
suggested by Cunado and Perez de Gracia (2014). They analyze
the influence of oil price shocks on the stock returns of twelve
European oil-importer countries with VAR and VECM models
for the period between 1973 and 2011. Their main findings are
that the response observed varies greatly depending on the nature
of the oil price change, which confirms that there are statistically
significant negative sensitivities among most European stock
market returns. Furthermore, they find evidence that European
stock market returns are mostly affected by oil supply shocks.
“Finally, Zhang (2017) implements a methodology that was
developed in previous work by Diebold and Yilmaz (2009, 2012
and 2014) to analyze the relationship between oil shocks and
returns for six major stock markets, and combines it with a rolling
windows analysis to find that that the contribution of oil shocks
to the world financial system is limited, but can occasionally
contribute significantly to stock markets. He also proves that only
large shocks matter.”
2.3. Oil, Exchange Rates and Interest Rates
A few studies have specifically tackled the relationship between
oil prices and exchange rates. That relationship should be more
explicit in the case of those countries where oil exports represent
a significant component of their foreign trade, or where oil
imports can affect the cost of production and distribution of
their main industries. It should be noted, however, that the size
of the economy and the relative importance of the oil industry,
as well as other characteristics of the economy are factors in
the determination of how much will exchange rates respond to
changes in oil prices. For example, the paper of Huang and Guo
(2007) investigates to what extent the oil price shock and other
types of macroeconomic innovations affect China’s real exchange
rate. They estimate a structural VAR model whose results indicate
that real oil price shocks have a lesser appreciation effect on the
long-term real exchange rate, relative to other trading partners
who are also members of the reminbi basket peg regime due to
China’s lower dependence on imported oil. In contrast, the work
of Lizardo and Mollick (2010) argues that since oil represents a
strategic input “into making virtually everything, including steel,
aluminum, plastics, rubber, fabrics, and fertilizers”, and given
the dependence of the United States economy with respect to oil
imports increased towards the end of the first decade of the century,
the US dollar may be losing value against other major currencies
due to the supply and demand relation for dollars, i.e., as more US
dollars are paid out to import a large volume of oil every day, an
increase in its price will also enlarge the supply of dollars to the
market, pressuring down the exchange rate. In order to test their
hypothesis, these authors expand the monetary model of exchange
rates, and find that oil prices significantly explain movements in
the value of the dollar against major currencies in between the
1970s and 2008, and also confirm that their long-run forecasts
are remarkably consistent with an oil-exchange rate relationship.
They reveal that an increase in the real price of oil results in
a significant depreciation of the US dollar with respect to oil
exporting countries’ currencies, such as in the cases of Canada,
Mexico, and Russia.
On the other hand, Ghosh (2011) studies the relationship
between the oil price and the exchange rate in India, by using
GARCH and EGARCH models, for the period from July 2007 to
November 2008, and reports as the main findings that increasing oil
prices produce a depreciation of the Indian currency with respect
to the USD because India is an oil-importer, and as oil prices rise,
Indian refiners pay more dollars for oil-imports, putting pressure
downwards on the Indian currency. The study also concludes that,
in contrast to previously published works, oil price shocks have
symmetric effect on the exchange rate, and that oil price shocks
influence exchange rates volatility in a permanent way.
It is important to point out that Basher et al. (2012) have studied
the relation among oil prices, emerging market stock prices and
exchange rates. These authors explored the way in which oil prices
and emerging market stock prices are related, or how oil prices
affect exchange rates. However, the combined dynamic between
all three variables had not been extensively dealt with. This paper
estimates a structural VAR model that serves as a platform to study
the interactions between the three variables, and which serves to
perform an impulse-response analysis. The results are in line with
previously published stylized facts, like the negative response of
emerging market stock prices and dollar exchange rates to oil price
increases in the short run.
Aloui and Aïssa (2016) utilize different copulas of the elliptical,
Archimedean and quadratic families to model the underlying
dependence structure between crude oil prices and the USD
exchange rate of five major currencies, Euro, Canadian Dollar,
British Pound Sterling, Swiss Franc, and Japanese Yen, during
both bearish and bullish market phases, over the 2000-2011
period. They find reliable evidence of a significant and symmetric
dependence for almost all the oil price-exchange rate pairs
considered. Oil price increases are related with a depreciation of
the dollar according to the Student-t copulas results, and the main
results are unchanged when considering alternative GARCH-type
model specifications.
A fundamental relationship that surprisingly has seldom been
mentioned in the literature is the fact that the USD is the currency
used to invoice international oil trading most of the time and, for
that reason, the dollar appreciation or depreciation caused by
changing macroeconomic conditions should influence the dollar
price of oil. According to Zhang (2017), since 2002 the dollar
price of oil increased when the USD depreciated, suggesting the
existence of a co-movement in the long-run. This author uses
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cointegration analysis to confirm that this is the case, but finds
that cointegration does not show unless two structural breaks are
controlled for (November 1986 and February 2005).
that direct effects of oil price changes are present on interest rates
in net oil-consuming countries, those effects are nonlinear and
asymmetric among oil-producing countries.
At a the macroeconomic level, the relationship between exchange
rates and interest rates is subject to supply and demand conditions
that ensure a simultaneous equilibrium in both markets. While
interest rates respond to inflationary pressures, mainly through
the monetary policy channel, and exchange rates respond, among
other variables, to interest rates. In that sense, the work of Kim and
Jung (2018) examine the dependence structure between crude oil
prices, exchange rates, and interest rates in the United States by
using a MV-GARCH-BEKK model and data for the 1998-2017
period. These authors find that there exists an inverse relationship
between interest rates and crude oil prices and that the link between
oil prices and exchange rates becomes stronger for oil dependent
countries after the global financial crisis of 2006-2008.
Previous studies on the relationship assume that the direction of the
impact runs from oil price to interest rates. However, only a few
studies have addressed the inverse relationship. “One first example
is the work of Akram (2009) that revises the fluctuations of
different commodity prices (crude oil, food, metals and industrial
raw materials) and investigates whether declines in real interest
rates favor higher commodity prices, since price increases may be
associated to reductions in interest rates and, as a consequence, to
the dollar parity vis à vis other currencies.” With several structural
VAR models estimated on quarterly data over the period 1990-Q12007Q4, Akram’s findings are that commodity prices, including oil
prices, rise when real interest rates decrease in the United States.
Also, this author reports that real interest rate fluctuations explain
in a good measure the forecast error variance in commodity prices,
and commodity prices tend to overshoot in response to interest rate
changes. The conclusions of this work suggest that lower interest
rates in the United States lead to a weaker dollar, which explains
why commodity prices tend to rise in response.
The relationship between oil prices and interest rates is probably
more obvious when considering that oil prices are an element of
cost-push inflation in many productive sectors to which monetary
policy will immediately react by raising interest rates. With the
aid of a structural cointegrated VAR model for the G-7 countries,
Cologni and Manera (2008) study the effects of oil price shocks
on economic activity and the general price level, as well as the
reaction of monetary authorities. In the case of all G-7 countries
with the exception of Japan and the U.K., the hypothesis of oil
prices influence on domestic inflation is not rejected and, in
most countries, there is a temporary effect of oil price shocks
on domestic prices. Besides, impulse-response analysis reveals
monetary policy reaction to inflationary pressures are different
from one country to another. These authors also report the results
of a simulation exercise to estimate the impact of the 1990 oil price
shock and suggest that “a significant part of the effects of the oil
price shock is due to the monetary policy reaction…”
Ioannidis and Ka (2018) study the way oil price shocks impact
the entire yield curve of the United States, Canada, Norway and
South Korea by using a structural VAR model. These authors report
that the term structure incorporates oil shocks differs depending
on what drives them, as well as the degree of dependence of
the country on oil. According to the reported results, shocks of
oil prices originated by demand raise interest rates among oil
importers, but not among oil exporters. In all studied countries,
demand originated shocks result in an increase of the yield
curve slope. By contrast, oil supply originated shocks result in
brief negative responses of the United States and Canada yield
curve slopes, which are also associated with an accommodating
monetary policy.
Another example of a research that aims to contribute to the
understanding of the relationship between oil price changes and
interest rates is that of Sotoudeh and Worthington (2015). In it,
the authors test for the presence of nonlinear causality between
the two variables in the context of net oil-consuming and net
oil-producing countries by using the Hiemstra and Jones’ (1994)
nonlinear parametric model and the Mackey and Glass’ (1977)
parametric model. The findings are that while there is no evidence
234
Another perspective on the nature of the relationship between
interest rates and oil prices is presented in the work of Arora and
Tanner (2013). In it, the authors show that oil prices fall when there
are unexpected increases in either United States’ interest rates or
other major industrial countries’ interest rates. The underlying
reasoning is that the opportunity cost of oil extraction and
storage is represented by the real interest rate. In that sense, when
interest rates are low, production is low and storage increases; the
opposite is expected when real interest rates increase, creating the
conditions of an inverse relation between oil price and interest
rates. According to the paper results, oil price falls when the
ex-post short-term real interest rates increase, and the response
of oil price to ex-ante real interest rate changes depends on the
inclusion of certain periods of observation. However, oil price is
at all times responsive to short-term real interest rates (at least,
during the observation period, from January 1975 through May
2012), and oil prices become more sensitive to long-term interest
rates through time.
2.4. Natural Gas and the Stock Market in Different
Economic Regions
The relative abundance of studies focused on the relationship
between the price of energy commodities and financial markets
is much greater in the case of oil than in the case of any other
combination of alternatives (e.g., oil and interest rates or exchange
rates; natural gas and the stock market, interest rates or exchange
rates, etc.). However, some works that study the relationship
between natural gas prices and financial variables deserve a brief
mention in this section.
For example, Acaravci et al. (2012) investigate what is the nature
of the long-run relationship between natural gas prices and the
stock market using Johansen and Juselius’ (1990) diagnostic test
to identify the presence of cointegration among the variables, and
then proceed to develop a vector error correction model with which
International Journal of Energy Economics and Policy | Vol 9 • Issue 3 • 2019
Santillán-Salgado, et al.: Is there a “Reverse Causality” from Nominal Financial Variables to Energy Prices?0
they measure Granger causality for the EU-15 member countries
between 1990 and 2008. The reported empirical findings include
the existence of a long-term equilibrium between natural gas
prices, industrial production, and stock prices in Austria, Denmark,
Finland, Germany and Luxembourg, but not in the other ten EU-15
countries. Granger causality analysis results also suggest that the
increases in natural gas prices affect industrial production growth,
which, in turn, affects stock market returns.
into tail-spin fall and, by the 1st months of 2008, financial markets
around the world were in absolute chaos. Associated with the
financial crisis, the United States and other major economic
powers fell in a deep recession and, when they were experiencing
the first recovery signs, a new financial crisis was detonated by
the sovereign debt crisis in Greece, Ireland, Portugal and Spain
(from 2009 to 2012). Only then did a very gradual recovery of the
world economy take place from the depths of the 2008-2009 crisis.
Another study regarding the interaction between energy and
the stock markets in the USA is that of Gatfaoui (2015). After
controlling for structural breaks, characterizing dependencies,
and using a multivariate copula to assess the joint dependence
structure among natural gas, crude oil, and stock markets, the
author documents the changing nature of the relationship over
time. In particular, the author focuses on the identification of
changes of sign in correlations and the possibility of dependence
among extreme price changes.
The evolution of the stock markets of the main economic regions
reacted to the aforementioned events, as observed in Figure 1. The
four major stock market indices are converted to a common base with
value equal to 100 on January 4, 2005, to allow a visual comparison of
their relative evolution during the period of analysis. Figure 1 shows
that the United States, the Eurozone and Japan’s stock markets follow
similar paths, responding to the world’s economic and geopolitical
conditions, and that China’s main index, the Shanghai Stock
Exchange (SSE) index, recorded two dramatic roller-coaster like ups
and downs, the first one reflecting the enthusiasm for the impressive
economic growth and the rapid modernization of the country during
the 1st years of the century, followed by the drastic deacceleration
due to the financial crisis, and the second one probably associated
to the liberalization of the exchange rate and other important market
oriented institutional changes in that country. It must be emphasized
that the SSE transformed index is represented on the right axis of
Figure 1 due to the disproportionate magnitude of its range (reaching
554 points during June 2015).
One last reference on the relationship between natural gas prices
and the stock markets is the work of Ahmed (2017). The author
proposes the analysis of the dynamic mean and variance of natural
gas and the stock market in Qatar. By using a modified crosscorrelation test, controlling for structural breaks in conditional
variances, and removing regional as well as international factors,
this author finds mean and volatility spillover effects from natural
gas prices to Quatar’s stock market.
2.5. Studies on the Influence of Stock Markets on
Energy Prices
The influence of nominal financial variables (exchange rates,
interest rates, stock exchange returns) on energy (oil, natural gas)
prices has been scantly explored. Just a few works pay attention
to the influence of financial variables on energy prices; however,
they have recognized that there is evidence that financial variables
can influence energy prices. For example, Akram (2009) and Arora
and Tanner (2013) report the influence of interest rates on oil
prices and other commodities. Also, Ghorbel et al. (2014) find that
there exists a volatility spillover of American investor sentiment
towards the stock market returns and the oil market returns. Other
works that report an influence from financial variables onto energy
prices include that of Kilian and Park (2009) that questions the
validity of the studies that attribute the impact of oil prices on
the macroeconomics as if they were exogenous, omitting the
possibility of reverse causality from macro aggregates to oil prices.
3. ANALYSIS OF THE FINANCIAL
VARIABLES AND GAS AND OIL PRICES
The 1st years of the XXIst century were plagued by unexpected
global economic and geopolitical events that generated an intense
turbulence in the world’s markets. After the dot.com burst in 2000,
the United States economy was ready to slow-down. The terrorist
attack to the World Trade Center catalyzed what came to be a
relatively mild recession, and 2 years later, in 2003, the world
was precipitated into a new war, in Iraq. By 2007 the subprime
mortgages segment of the United States financial markets went
Figure 2 represents the evolution of trade-weighted exchange rates
of the world’s four largest economic areas, converted to a common
index, with base on January 2005=100. The index for the USD
was relatively stable throughout the period, while the renminbi
appreciated consistently, at times experiencing strong technical
adjustments. Notice that after reaching a 50% appreciation level, it
ended with a 30% appreciation in the observation period. After an
initial appreciation between 2005 and 2008, the euro depreciated
consistently, and by the end of the period it had lost almost 24%
of its initial value. In the meantime, the yen appreciated almost
25% between 2005 and 2012, but during the latter part of the
observation period depreciated consistently and ended the period
around 10% below its initial level.
Energy prices often have sustained increases during relatively
long periods and the demand elasticity for energy is highly rigid.
Those two characteristics differentiate energy prices from other
commodities (Kilian, 2008). But the idea that exogenous energy
price shocks are the main cause of economic recessions is probably
an oversimplified description of the problem. While a number of
works present significant evidence about the relation between
oil price and economic activity is not a statistical coincidence
(Hamilton, 1983; and Blanchard and Galí, 2010), others theorize
there may be an indirect link via monetary policy. For example,
Bernanke et al. (1997) examine the role of monetary policy
when responding to oil shocks, which they authors consider
to be exogenous, and test whether the response of the Fed by
raising interest rates to control potential inflationary pressures
with anticipation becomes the main cause of downturns, but find
no concluding evidence. These authors suggest the possibility
International Journal of Energy Economics and Policy | Vol 9 • Issue 3 • 2019
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Santillán-Salgado, et al.: Is there a “Reverse Causality” from Nominal Financial Variables to Energy Prices?
Figure 1: Indices of the four largest stock markets in the world (January 2005=100)
Source: Bloomberg
Figure 2: Trade-weighted exchange rates for the USD, the Euro, the Yen and the Renminbi (January 2005=100)
Source: Bloomberg
236
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of “obtaining credible measurements of monetary policy’s
contribution to business cycles” is rather challenging. What cannot
be denied is that the Fed’s (and other Central Banks’) policy
decisions are mainly justified by macroeconomic conditions, as its
main task is to maintain price stability and full employment, and
that in order to execute its policy decisions, it makes use of the
discount rate (the Fed Funds rate in the case of the United States,
i.e., the reference interest rate for commercial banks, which is
also a benchmark for new debt issues and many other contractual
arrangements). In that sense, one would expect interest rates to
respond to oil price shocks, although there are also many other
macroeconomic variables that guide the decisions of central banks.
The dynamics of the interest rates in the four economic poles
studied in this work is shown in Figure 3 after converting the
original series to an index with base January 4, 2005 =100. This
conversion, again, is made with the intention to make different
interest rate levels relative changes strictly comparable in time.
The atypical increase of the Tokyo Interbank Offer Rate (Tibor)
index, reaching a value of 908 in December 2008 (and rapidly
descending afterwards in response to the world’s economy
recession) is explained by the very low level (close to 0%) it had at
the beginning of the period, and to avoid the distortion effect such
a large value would have on the rest of the indices, it is represented
with reference to the right axis of Figure 1.
The evolution of Japan and China’s interbank interest rates follows
very different patterns during the observation period. However, the
United States’ Fed Funds rate and the Euribor for the Eurozone,
maintain certain parallelism. The Chibor rates, represented along
with the Fed Funds rate and the Euribor rate on the left axis of
Figure 3 recorded two clearly identifiable periods of high volatility,
the first between January 2006 and December 2008, and the second
from January 2011 through December 2013.
Recent studies on the long-term relationship between natural
gas and crude oil prices have recognized they are cointegrated
(e.g., Brigida, 2014; Ramberg and Parsons, 2012). However,
the prices of oil and gas have followed very different paths
and some authors even argue that there is a permanent rupture
caused by fundamental transformation in each market. Ramberg
and Parsons (2012) find that cointegration needs to be tempered
with the additional consideration of the fact that there is a large
amount of “unexplained volatility” in natural gas prices at short
horizons, and that the cointegrating relationship does not appear
to be stable through time. Finally, Brigida (2014) models the
cointegrating relationship incorporating structural breaks in the
relative pricing relationship by means of a first order Markov
switching process.
When looking at the prices of oil and natural gas during the period
of study, it is clear that both prices follow a very similar path along
most of the 12 years of observation, but they show a significant
decoupling towards the last quarter of 2008 to converge again
towards the end of 2014. While the price of each commodity is
expressed in different units of measurement (oil price is expressed
Figure 3: Interbank interest rates for the four largest economic areas in the world (January 2005=100)
Source: Bloomberg
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Santillán-Salgado, et al.: Is there a “Reverse Causality” from Nominal Financial Variables to Energy Prices?
Figure 4: International price of natural gas and brent oil (January 2005=100)
Source: Bloomberg
in per barrel terms, and natural gas is expressed in per mmBtu
terms), the close parallelism observed in Figure 4 is emphasized
by the representation of an index whose base is January 4, 2005,
for both energy commodities.
4. ECONOMETRIC ANALYSIS, EMPIRICAL
RESULTS, AND DISCUSSION
The estimation of a PVAR model identifies the direction and
intensity of the reciprocal influence between energy markets and
nominal variables of the four largest and most developed economic
areas in the world. There is evidence of an influence from the
energy markets towards the sample stock market indices, but not
the other way around. However, the influence of interest rates on
the determination of both oil prices and natural gas prices is highly
statistically significant.
The database used in the PVAR analysis consists of four financial
variables and two energy prices, with daily observations for the
period that goes from January 5, 2005 to December 30, 2016.
The financial variables include the stock market indices of the
United States (S&P500), China (SSE Composite), the European
Union (Stoxx Europe 600), and Japan (Nikkei 225); the interbank
interest rates for each country/region, and the corresponding
trade-weighted exchange rates of the four main currencies (the US
Dollar, the Euro, the Yuan and the Yen). All the data was obtained
from a Bloomberg terminal.
238
The stock market indices correspond to the largest and more liquid
markets in each major country/region. The interbank interest rates
represent each country/region’ cost of borrowing incurred in shortterm loans among local banks, and the exchange rates are proxied
with trade weighted indices. A detailed description of the selected
variables is presented in Table 1.
As mentioned before, the effect that energy prices have on stock
markets performance (and other economic and financial nominal
variables) has been extensively documented and frequently verified
(e.g., Abhyankar et al., 2013; and Aloui and Aïssa, 2016), and the
influence of oil price fluctuations on other financial variables, as
exchange rates and interest rates response to oil prices changes,
has also been frequently studied (e.g., Ferraro et al., 2015; and Kim
and Jung, 2018). However, after an extensive review of related
works, it was confirmed that the study of the potential influence
of financial variables (stock market returns, exchange rate and
interest rate fluctuations) on energy prices has been only scantly
mentioned in the literature, and that is the main contribution of
the present research.
The PVAR estimated with generalized method of moments
(PVAR-GMM) used in this work to examine the interactions of
the main financial variables of the world’s four largest economic
areas, China, the European Union, Japan and the United States,
and the price of the two most important energy commodities, oil
and natural gas, is estimated following the programming solution
developed of Abrigo and Love (2015). The PVAR estimates
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Table 1: Description of the variables used in the analysis
Assigned ticker
Wti
Brent
Natgas
Snp
Stoxx
Nikkei
Sse
Usd
Eur
Jpy
Bloomberg ticker
USCRWTIC Index
EUCRBRDT Index
NG1 COMB Comdty
SPX Index
SXXP Index
NKY Index
SHCOMP Index
USTWBROA Index
CEEREU Index
ATWIJPY Index
Concept
Barrel of oil
Barrel of oil
Natural Gas
Stock Mkt
Stock Mkt
Stock Mkt
Stock Mkt
Exch. Rate
Exch. Rate
Exch. Rate
Chy
ATWICNY Index
Exch. Rate
Fed
Euribor
Tibor
Chibor
GFED03M Index
EUR003M Index
TI0003M Index
IBO03M Index
Int.Rate
Int.Rate
Int.Rate
Int.Rate
Detailed description
Bloomberg West Texas Intermediate Cushing Crude Oil Spot Price (WTI)
Bloomberg European Dated Brent Forties Oseberg Ekofisk Price (BFOE)
Bloomberg Average Price of Natural Gas
S&P 500 Index
STOXX Europe 600 Price Index EUR
Nikkei 225
Shanghai Stock Exchange Composite Index
FED’s US Trade Weighted Broad Dollar (Jan 1997=100)
Calculated Effective Exchange Rate EURO (1990=100)
Westpac nominal effective exchange rate trade Weighted Japanese Yen
(Dec 1994=100)
Westpac Nominal Effective Exchange Rate Trade Weighted Chinese Yuan
(Dec 1994=100)
ICAP Capital Markets Domestic Fed Funds 3 months
Euribor 3 months ACT/360
Japan Bankers Association TIBOR fixing rate 3 months
China CHIBOR 3 months
Source: Bloomberg
the direction and intensity of the reciprocal influence between
prices and the main financial variables of. Its main focus is the
detection and measurement of the short-term influence of changes
in financial variables over energy prices. The original variables
(Table 1) are transformed into their first differences because of
the dynamic nature of the analysis, aimed to identify and quantify
the intensity of the response of a given variable when the other
variables in the system fluctuate; first order differentiation is also
useful to solve the problem of non-stationarity that is present in
several of the variables in the original series in levels.
According to Andrews and Lu (2001), a consistent model and
moment selection criteria for GMM can be based on the J test
statistic for testing over-identifying restrictions, and “include
bonus terms that reward the use of more moment conditions for
a given number of parameters and the use of less parameters for
a given number of moment conditions.” In effect, in the context
of PVAR, the identification of the optimal number of lags can be
based on the Bayesian (BIC), Hanan-Quinn (QIC) and Akaike
(AIC) information criteria adapted to the multivariate modeling
requirements, and reported here as MBIC, MAIC and MQIC, in
Table 2, where the minimum values of the three criteria suggest
that a first-order PVAR model must be selected.
The PVAR system estimation considering the nominal financial
variables (stock market returns, interest rate fluctuations and
exchange rate fluctuations) and energy prices (Brent oil price and
the Natural Gas price changes) are reported in Table 3.
The first equation corresponding to the stock market returns as
dependent variable, presents a negative and highly significant
coefficient corresponding to the exchange rate and suggests that
currency depreciation exercises a strong negative influence on
the stock market. The second equation, with the exchange rate
as dependent variable, it shows that the stock market’s returns
coefficient is highly significantly and negatively related to the
lagged stock market returns coefficient, which suggests that a
positive performance of the stock market has a negative effect on
Table 2: Panel vector auto regression lag order selection
criteria
Lag
1
2
3
Coefficient detection
0.6723
0.6972
0.1683
MBIC
−441.7492
−290.2568
−148.5636
MAIC
−54.2792
−31.9435
−19.4070
MQIC
−199.6869
−128.8820
−67.8762
Source: Stata output, elaborated by the authors.
the exchange rate, i.e., results in an appreciation of the currency.
The third equation, for interest rates fluctuations, shows no
evidence of significant influence from the rest of the variables
in the system. This may be interpreted as interest rate markets
following their own dynamics or, in any case, not responding to
the rest of the model’s variables influence (it may well be the case
that interest rates only respond to central banks’ monetary policy
decisions and market expectations regarding inflation).
The fourth and the fifth equations of the model correspond to Brent
oil price and natural gas price changes as dependent variables,
and show evidence of a lagged interest rate’s fluctuations positive
association with both. That is, lagged increases in interest rates are
positively related to Brent oil and natural gas price increases. This
is an interesting finding because it suggests that energy markets
are sensitive to monetary policy signals (and, probably, market
agents’ expectations about inflation). A possible interpretation of
this is that as central banks raise their reference rates, and all other
interest rates in the money and capital markets follow suit with the
intention to reduce the incentives for new investment projects and
inhibiting consumption (mainly of large price-tag goods), then the
market interprets that signal as a gauge of economic activity and,
indirectly, of the intensity of prevailing demand.
Brent oil lagged fluctuations also have a negative relation with
their own next day’s fluctuations, with a moderate statistical
significance, just below 10%; and, lagged Natural Gas fluctuations
have a positive influence, with a moderate statistical significance at
8%. In the first case, own lagged fluctuations negative relationship
suggests that Brent oil prices tend to react to 1 day’s upwards
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Santillán-Salgado, et al.: Is there a “Reverse Causality” from Nominal Financial Variables to Energy Prices?
Table 3: Panel vector auto regression estimation
Variables
Stockmkt
Stockmkt L1
Xchgrate L1
Intrate L1
Brent L1
Natgas L1
Xchgrate
Stockmkt L1
Xchgrate L1
Intrate L1
Brent L1
Natgas L1
Intrate
Stockmkt L1
Xchgrate L1
Intrate L1
Brent L1
Natgas L1
Brent
Stockmkt L1
Xchgrate L1
Intrate L1
Brent L1
Natgas L1
NatGas
Stockmkt L1
Xchgrate L1
Intrate L1
Brent L1
Natgas L1
Coefficient
Standard error
z
P>|z|
(95% confidence interval)
−0.04216
−0.20742
0.00902
0.03217
0.00650
0.03612
0.07713
0.00724
0.02058
0.00897
−1.17
−2.69
1.25
1.56
0.72
0.243
0.007
0.213
0.118
0.469
−0.11296
−0.35860
−0.00517
−0.00817
−0.01108
0.02864
−0.05625
0.02321
0.07251
0.02407
−0.02463
−0.02678
0.00209
−0.00415
0.00444
0.01236
0.03811
0.00168
0.00861
0.00453
−1.99
−0.7
1.24
−0.48
0.98
0.046
0.482
0.214
0.630
0.327
−0.04886
−0.10148
−0.00121
−0.02102
−0.00444
−0.00041
0.04791
0.00538
0.01273
0.01333
−0.14330
−0.03523
−0.12131
0.02289
0.00824
0.11937
0.22935
0.16909
0.06197
0.02669
−1.2
−0.15
−0.72
0.37
0.31
0.230
0.878
0.473
0.712
0.757
−0.37727
−0.48474
−0.45273
−0.09857
−0.04407
0.09067
0.41429
0.21010
0.14435
0.06056
0.03751
−0.10954
0.01804
−0.05263
0.02447
0.03966
0.09501
0.00567
0.03153
0.01396
0.95
−1.15
3.18
−1.67
1.75
0.344
0.249
0.001
0.095
0.080
−0.04022
−0.29576
0.00693
−0.11442
−0.00289
0.11524
0.07668
0.02915
0.00916
0.05183
0.03622
−0.02143
0.03840
−0.09621
0.03655
0.05518
0.12533
0.01159
0.03426
0.02239
0.66
−0.17
3.31
−2.81
1.63
0.512
0.864
0.001
0.005
0.103
−0.07193
−0.26708
0.01568
−0.16335
−0.00734
0.14437
0.22421
0.06111
−0.02906
0.08043
Source: Stata output, elaborated by the authors
Table 4: Granger causality tests
Equation/excluded
Stockmkt
Xchgrate
Intrate
Brent
NatGas
ALL
Xchgrate
Stockmkt
Intrate
Brent
NatGas
ALL
Intrate
Stockmkt
Xchgrate
Brent
Natgas
ALL
Brent
Stockmkt
Xchgrate
Intrate
NatGas
ALL
NatGas
Stockmkt
Xchgrate
Intrate
Brent
ALL
Chi square
df
Prob >Chi square
7.232
1.552
2.443
0.525
13.163
1
1
1
1
4
0.007
0.213
0.118
0.469
0.011
3.974
1.544
0.232
0.96
8.197
1
1
1
1
4
0.046
0.214
0.630
0.327
0.085
1.441
0.024
0.136
0.095
1.879
1
1
1
1
4
0.230
0.878
0.712
0.757
0.758
0.895
1.329
10.126
3.072
14.686
1
1
1
1
4
0.344
0.249
0.001
0.080
0.005
0.431
0.029
10.977
7.887
19.259
1
1
1
1
4
0.512
0.864
0.001
0.005
0.001
Source: Stata output, elaborated by the authors
240
movement, with a following day’s downwards movement, in some
sort of short-term mean reversion. In the second case, rising natural
gas prices impact the Brent oil market as the initial movement
reflects an increasing demand for energy, and expectations of the
market are bullish regarding economic activity.
In the last equation, with natural gas price fluctuations as a dependent
variable, besides interest rates changes positive significant relation,
Table 3 also reports a significantly negative influence of lagged Brent
oil prices, probably meaning that oil prices can affect economic
activity and reduce energy demand; and lagged natural gas prices
positively influence that same variable, but with a significance level
that is marginally >10%. A Granger causality analysis corroborates
the results of the PVAR analysis, as shown in Table 4.
Table 3 results suggest that the exchange rate fluctuations Grangercause stock market fluctuations, and the stock market fluctuations
Granger-cause exchange rate fluctuations. Interest rates seem
to have rather independent evolution, as they are not Granger
caused by any other variable. However, in the case of the Brent
oil price changes, the evidence suggests they are Granger-caused
by interest rates and natural gas changes; and natural gas changes
are Granger-caused by Brent oil price and Interest Rate changes.
All relationships in the causality analysis are consistent with the
results reported in Table 2.
Before proceeding to an Impulse-Response analysis, the stability
conditions of the PVAR model are verified by looking at the
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Santillán-Salgado, et al.: Is there a “Reverse Causality” from Nominal Financial Variables to Energy Prices?0
Figure 5: Roots of the companion matrix
That evidence reinforces the conclusion that while daily changes
in interest rates seem to have significant influence on the evolution
of the next-day price in both energy commodities, the effect is
short-lived (Figure 6).
5. CONCLUSIONS
Source: Stata output, elaborated by the authors
Figure 6: Impulse-response results for interest rate changes on energy
prices. (a) Interest rate: Natural gas, (b) Interest rate: Brent oil price
a
b
Source: Stata output, elaborated by the authors
calculated modulus of eigenvalues, which are, in all cases, less
than one, as can be observed on the complex unit circle (Figure 1)
confirming that the model is adequate to describe the stochastic
process (Lutkepohl, 2005; Abrigo and Love, 2015) (Figure 5).
While the PVAR-GMM and Granger causality results suggest
that there is an important role of interest rate changes on Brent
oil and natural gas price changes, a forecast error variance
decomposition (FEVD) based on Cholesky’s decomposition of
the residual covariance matrix of the PVAR model suggests that
the relationship only holds in the very short-run. In both cases,
after 10 days, a shock to interest rates has less than a one percent
explanatory capacity on those two variable variances. By contrast,
their own shocks explain as much as 94.7% in the case of Brent
oil price changes, and 96.1% in the case of natural gas changes.
The orthogonalized impulse-response of the PVAR summarizes
the model findings and makes the Granger Causality findings
compatible with the FEVD evidence. What one can observe from
the representation of interest rates’ impact on energy price changes
is that.3 In effect, interest rates have a clear, though small, impact
on natural gas price changes, but that impact is rapidly assimilated.
3
Figure 2 only includes interest rates changes impact on energy prices due
to space limitations.
The PVAR model used to analyze the interrelationships among
nominal financial variables and energy prices worldwide reveals
several different findings: (1) Lagged exchange rate fluctuations
have a negatively significant effect over the stock market; (2) a
positive performance of the stock market has a negative effect on
the exchange rate, i.e., results in an appreciation of the currency;
and (3) interest rate markets follow their own dynamics or, in any
case, do not respond to the rest of the model’s variables influence.
Probably the most interesting finding is that Brent oil and natural
gas price changes are positively and highly significantly influenced
by lagged interest rates’ fluctuations, that is, energy markets
are sensitive to monetary policy signals (and, probably, market
agents’ expectations about inflation). Additionally, Brent oil lagged
fluctuations have a negative effect on next day’s Brent fluctuations,
and lagged natural gas fluctuations have a positive influence on
oil. Finally, natural gas prices are negatively influenced by lagged
Brent oil prices and positively influenced by their own lagged
price changes.
It is true that, while the economic importance of each of the four
stock market indices in our sample is not comparable by far with
other stock markets in the developed and emerging world, there
are significant differences in capitalization value and liquidity
between the United States market and the rest, while in our analysis
its influence is included in the same terms as China, the Eurozone,
and Japan’s indices. Also, the importance of the interbank interest
rates in the United States is more economically significant than is
the case of the other three economic areas. Finally, the volatility of
the Shanghai composite index is, evidently, much greater than that
corresponding to the other three sampled indices, something that
may probably be due to its relatively recent creation as well as the
fast pace of changes that are taking place in China’s economy and
financial markets. While these considerations may have weighted
in the precision of our reported results, the size of the sample and
the highly significant empirical outcomes that are found validate
all of the above results.
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