Climatic change (CC) and the political response to it has been the major factors affecting the Fossil Fuel (FF) prices since it was declared an issue of concern on the global arena. This has considerably affected the demand and supply chain of many FF companies in the world. As a mitigation factor to Greenhouse gas (GHG) emissions, the Kyoto Protocol (KP) was adopted by the United Nations Framework Convention on Climate Change (UNFCCC) in 1997. Various responses at the national and international levels on CC has effectively targeted the use of FF, effectively reducing its demand, in favor of the renewable energy like solar and wind, thereby causing a considerable reduction on the revenues and growth of the market for FF-producing countries. In the recent past, there has been economic turbulence on countries that strongly rely on FF revenues like the Kingdom of Saudi Arabia (KSA) due to the unpredictable pricing of their commodities. Therefore, the KSA future development as one of the oil-rich countries is at stake; hence, the need for Research in the area.

This thesis examines how the FF rich countries are affected by the CC, and the “greening of the world.” The study will closely monitor how FF prices are affected by the environmental policies and CC as driven by the GHG emissions from FF combustion. The research will also take a keen interest in the perception and policies of the management of the KSA on how the CC and how it affects their FF prices. Additionally, the research will consist of a quantitative analysis while interrogating how the dynamics of CC and the global prices of green energy and how they affect the global uptake of the FF and their effects on FF prices, which will be achieved through the analysis of secondary data. The variable set return will include how the FF prices in KSA are affected by that of natural gas (NG) and Coal. On the other hand, the variable set CC will cover the green energy index, global CO2, temperature and precipitation.

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The research will use time series conditional variance as the approach to determine how the CC and the greening our society affects the returns of FF industries. The study will analyze how the above variables are dependent on each other. The granger casualty tests are used to estimate the systematic relationships, the responses to the variables and variance decompositions. The research will also seek to determine the various weights and the linkage between variables influenced by CC and how they influence the prices of FF, which is achieved through the canonical correlation analysis.

This thesis suggests that stockholders, to a large extent, consider the dynamics of CC and how they influence the FF stocks in their index valuation processes. There is an excellent inference that the FF industry’s value independent on the green energy index and equally affects its demand.

Fossil fuel, climate change; qualitative and quantitative analyses; stationarity; stochastic

volatility models; vector autoregressive; impulse response; variance decomposition

analysis; Granger causality test; correlation; loading; redundancy.

Contents

Methodology and Data Analysis 10

3.3.1 Vector Autoregressive Analysis 11

3.3.2 Impulse Response Analysis 12

3.3.3Variance Decomposition Analysis 13

3.3.4 Granger Causality Analysis 13

3.4 Unit Root Tests and Model Checking 13

Empirical Analysis and Discussion 15

4.1 Presentation under Different Categories 15

4.1.1 Factors that influence oil prices in different countries globally 15

4.1.2 The Global Concern on CC 15

4.1.3 The Nature of Links among oil-producing countries regarding FF production and prices 15

4.3 Analysis Based on Correlation 16

4.4 Vector Autoregressive Analysis 17

4.4.1 Lag Order Based on the Simulations for vector autoregressive 17

4.4.2 Vector Autoregressive System Stability 17

4.4.3 Testing the Adequacy of Vector Autoregressive Model 17

4.4.4 Vector Autoregressive Estimate 18

4.4.5 Impulse Response Test Results 18

4.5 Variance Decomposition Test Results 18

4.6 Granger Causality Test Results 19

Chapter 1

Climatic conditions that are continuously recorded accelerate the concerns towards the persistent use of Fossil Fuels (FF) commodities. In this regard, the “Fossil fuel age” is expected to come to an end sooner, and so is the influence that comes about the production of oil, natural gas (NG) and coal. Other than the CC concerns, the FF demand is further threatened by the consistent awareness on the need to conserve energy which works in favor of the alternatives to FF. Combustion of FF remains the most significant source of Green House Gases (GHG) emissions, which is the main product of Oil, NG and coal industries, thereby becoming the most significant contributor to the CC (Anderson, 2007). Since the most significant market factors that determine pricing is demand and supply, the FF prices will only remain stable in a short time due to the limitation of the alternatives (Halsnæs, 1996). However, future changes and technological uptake to improve the existence and cost of alternatives will significantly dent the FF prices due to reduced demand.

According to the Scientists and the International Energy Agency (IEA), the world faces a catastrophe of global warming; hence there is a need to move to other energy sources with lower GHG emissions. There have been increasing awareness on the FF’s and their link to CC thereby causing public protests, pushing the global business and economic community to formulate solutions in terms of policies. Some of the policies include the European Union (EU) Emissions Trading Scheme (ETS) in 2005 and the New Zealand ETS which came as a response to carbon-emissions concerns. Such policies have been supported at a local, national, regional and global level over time. Reduction of GHG emissions involves huge financial burden through infrastructural investments in alternative energy sources, as nations switch to lower carbon sources of energy and renewable ones. In the words of Rifkin (2016), the third industrial revolution will adopt and promote a global green economy.

Other factors threatening the FF production and pricing includes economic wars and counter GHG emissions policies that govern the development of clean and green energy. Consumption of FF is expected to drop massively due to these factors. As such, oil producing countries like Kingdom of Saudi Arabia (KSA) faces a huge revenue drop from FF which will directly affect their economies which are highly dependent on the ‘Petro’ dollars. Therefore KSA needs the knowledge on the relationships between the FF prices, and CC will be instrumental to her survival (Wellington and Sauer, 2005). It is such knowledge that will help KSA to respond to the global dynamics in time.

This study seeks to examine the global CC and the dynamics of green energy use and how they impact on the production and pricing of FF and their eventual market value. Empirically, this study explores the context of FF industry with green energy and GHG emissions.

The market value of the FF industry is primarily threatened by climatic risks concerns and, therefore, faces a potential variance in revenues. CC influences FF markets, for example, unusually hot summers and freezing winters affect the demand for heating and cooling solutions, which will influence production, pricing and the values of securities (Knight and Pretty, 1996). Conversely, FF products produce dangerous hydrocarbons which are closely associated with environmental pollution which causes environmental degradation and CC (McGeachie et al., 2005). Besides, FF sector pose severe disasters from oil leaks, spills and explosions as recently experienced in California gas leak. According to a scientific paper in Science, the hole released the equivalent of one-year emissions from half a million cars, by releasing 97 metric tons of heat-trapping methane over 112 days (Conley et al., 2016). The public perception and behavior threaten the market value of FF industries due to their contribution to the GHG emissions which are responsible for CC. FF sectors have always contributed marginally to any investor’s portfolio. However, a legitimate concern lies on the extent to which the market will respond to the concerns.

Climate is often expressed as the weather records over some time, and the climatic elements include temperatures, precipitation, and other meteorological phenomena. Presently, there lacks an empirical, comprehensive analysis of the value relevance of CC on the FF producing countries and their industries, hence the significance of this research. Moreover, in the context of KSA, there has been little or no investigations on the relations. This research analyzes the effects of global CC and alternative less carbon energy use on the market value of KSA. Key players are the CC factors, pollution from oils, NG and the coal industry. The primary aims of this research include the following:

- To determine the roles and responsibilities of the petroleum industry and how its impacts on the CC.
- To determine the characteristics of the individual time series of the FF variable set and the CC variable sets.
- Determine the relationships among FF, CC and the sets of variables.

To achieve the targets, the researchers will examine the value market behaviors. The research further focuses on the impacts of CC and GHG today compared to the past before the UN introduced the CMP (KP). The researcher examines the KSA FF sector, since climate policies and the increase in renewable energy affects all the firms in the industry worldwide, to obtain a detailed outcome.

This research paper consists of 5 chapters. Theoretical and empirical literature will be reviewed in the second chapter while presenting the institutional background of the FF industry and CC respectively. Besides, the section will introduce new research questions as suggested by the findings and represent the gaps and contractions. They will also examine different issues that relate to FF prices and the relevance of CC policies, green energy, and carbon risks. Chapter 3 of the thesis will present the methods applied in the empirical analyses. It will describe the data and the statistical techniques and models used in the analysis.

Chapter 2

According to (BP, 2016) there is large dominance of the energy industry by the crude oils which remains to be the primary source of energy and has stood against all the innovations and advancements in alternative energy sources in the world. With the steady global population growth and increased motorized transportation, it is believed that there will be a steady growth in oil prices which will, in turn, sustain the market value of the FF industries (Rickwood, 2010). The need to make the environment conducive has led to countries formulating policies which affect OP, NG and coal prices in general. The moves impact on CO2, TEM, and RN in various ways as follows.

Oil prices were modest at the time of discovery, making it the primary source of fuel and energy due to its numerous physical characteristics. This made engineering inventions of equipment’s centered on oil as the source of energy. Examples include the combustion engines. Consumption of oils was further pushed by policies and investments that supported the practice. In Britain, Winston Churchill weighed in support of oil by directing the Navy to transition from the use of coal in favor of oil, with policymakers directing investments towards establishment and development of oil companies (Craig et al., 2013). As a result, there was a huge upset of oil prices in the 1970s which saw a huge disruption in consumption from the gouge. In the early 1980s, the world witnessed a huge decrease in the carbon intensity in various countries which accounted for approximately 24% (IEA, 2004). The disruption, however, pushed a huge policy steering on the diversification and efficiency towards the reconciliation of the energy market. There were greater incentives and regulations on automobile production that were geared towards NG and nuclear energy. The regulations were extended to general appliances and industrial constructions causing a drastic decline in oil prices between 1986 and 1999 (Jochem et al., 2000).

Over the years, there has been a different pattern of demand which has certain seasons of increased demand and prices and others characterized with a depression. These factors have been attributed to changes in structures of the oil markets like the overtaking of the pricing system by the Organization of Petroleum Exporting Companies (OPEC) and shock in supply stores (Fattouh, 2010). However, there was little influence from the production of power itself on the price movements (Fattouh, 2007). The pertinent question that has always risen is the increased demand for oil and other FF products with the oil alternatives dominating the discussion in its eventuality every time. Economists and engineers focus on the alternative for oil dominance in the transport industry to alleviate it from dependence on oil (Kahn Ribeiro et al., 2007).

Furthermore, NG and coal prices have been dictated by global demand patterns and pricing of oil as they have largely been used as an alternative use of energy. Over time, Europe has grown a high dependence of Russia for NG making it both physically and politically restrained forcing Europe into alternative energy sources like nuclear and renewable energy (Dickel et al., 2014). As the world experiences a reducing future prediction on demand for oil, it has pushed to a reduction of NG prices with its connection to oil loosening. According to (Helm, 2011), there will be a greater impact from electricity transportation methods as well as a transition from oil over to NG and coal. However, the production of electricity would be through abundant NG, coal and Shale gas, thereby pushing aside the notion of Peak gas.

Natural gas prices have been predicted to follow the pattern reflecting the ration of NG prices to the underlying price of oil (IEA, 2014). Equally, there is moderately low coal consumption and trade in the global market is characterized by low global price impact on local prices. Most local pricing of coal and natural gas prices are closely associated with their respective countries local production levels. Factors affecting the future prices of coal include the competition between coal and NG within the power industry. There is little or no anticipation in the rise of coal prices as compared to the prices of NG and oils, with future demands anticipating a drop in the general coal demand by 2030 (IEA, 2014).

The carbon content characteristics and CT emissions have also been the center tool for climate policies for economists. The Green Index has been used by experts to measure the performance of a green economy across 10 countries globally. GI was launched in 2010 and experts have been used to assess the sector’s performance. GI informs various production strategies for the FF industry and their supply behavior. On the other hand, GHG have greatly contributed to the rise of average global temperatures from the traditional 18°C to 33°C with studies suggesting that the global average temperature today would be 15°C in the absence of GHG effect. However, this would be unfit for human habitation (Dawson and Spannagle (2008, p. 196). The FF concerns in the close linkage of FF emissions and how they affect the Global temperatures thereby informing policy change towards minimizing the FF consumption. As such, it affects the demand and pricing of the FF products. On the other hand, global rain is a phenomenon that is closely connected to the production and combustion of FF. In this regard, global rainfall is another factor that contributes to the push for renewable energies as an alternative to the FF. There is also a strong link between global warming effects caused by the GHG emissions and precipitation which creates critical amounts of evaporation of water and drying of the earth surfaces (Trenberth, 2011).

Chapter 3

In this chapter, the various techniques that will be used in the study to achieve the aims above and objectives will be introduced. The study aims to investigate the effect on the price of the global oil (op) brought about by the global temperature change(TEM), the global rain(RN), changes in global CO2(CO2) and the green index(GI). This aims at determining the dynamics of the effects of nature on the market prices of global oil. The next objective is to investigate the effects of RN, CO2, GI, and TEM have on the market prices of the global natural gas (NG). The purpose is to elaborate on the dynamism of nature concerning the price changes. As such, this chapter combines different approaches of ascertaining the existence or lack thereof the relations. The chapter provides on how best the objectives of the study were achieved, from the data collection methods to how the data was analyzed. The deductive and qualitative approaches taken by this study connects research concepts data and procedure into achieving the qualitative and the quantitative information. The chapter also brings to perspective the data collection and the analysis process in achieving its objectives.

The analysis will include autoregressive vector estimates (VAR), impulse response (IR), variance decomposition (VD) and Granger causality (GC).

This section used the time series methodology in its analysis of the price indices concerning the dynamism of nature. The important TEM is perceived to disrupt the environmental as well as the financial aspect of the production of the gas oil. The selection of the gas oil as the focus of the research was informed by the importance of the oil in the country’s economy. As a result of the close connection between the TEM and the gas oil industry, plays a role in reducing the emission of the CO2 and the green index. Besides, the TEM affects coal production hence the coal prices (CP). The study uses the time series multivariate to help in the decomposition of the relation between the TEM and the creation of oil gas and coal.

For more informative results, data examined at different levels, including a total of 2,008 weeks,

462 months and 38 years (January 1, 1978, to June 30, 2016), were used to examine the

The interaction between the market value of FF and CC-related variables. The study had to sets of variable Yi and Xi. The Xi consists of the variables that affect the environment for the production of oil, gas, and coal which include, global temperatures (TEM), global rain (RN), global CO2 (CO2) and green index(GI). The Yi consists of the variables on the prices which includes the gas oil prices (op), the coal prices (CP) and the natural gas prices (NP). The Xi being the dependent variable and Yi is the independent variable. The variables subject to the analysis can be obtained from different sources. The details of the variables and references are shown in table 4.1 in the (a1.pdf)

The FF market data examined in the study are end-of-financial-trading-day, weekly, monthly and

annual share prices traded on different FF markets (Figure 4.1). Y1 is the Chapter 4 Data and

Methodology 143 average spot price of a significant oil trading classification that serves as a substantial

benchmark price for global oil purchases, equally weighed (Brent 38 ° API, Fateh 32oAPI, WTI

40oAPI). The gas oil price, Y2, refers to the average spot price between Arabian Light 34oAPI and Arab Heavy 27oAPI. The global NG price, Y3, is the average spot price for Europe, JapanThe set Yi variable transformed into the form of returns (RYi) on price indexes.

A total of 70 firms were selected for this study under strict restrictions and after careful review of

the stock market of the green firms. The selected firms are listed in Appendix I. The stock

variable (X1) allows us to track the renewable energy industry, thus creating an energy index that

reflects the most significant opportunities for clean energy. This index will provide a benchmark for

future clean energy sectors and a performance measure for environmental sustainability projects

in Chapter 4 Data and Methodology 146.

The methodology is that theoretical and systematic strategy that brings out how the study was undertaken among other things. It gives light to the tools and techniques used in doing the research. These methods determine, among other things, the mode and how the data was collected and calculated. As much as the methodology focuses on the kind and nature of the process followed in a specific procedure in attaining an objective, it does not describe the specific methods. The research aims in determining the relationships of the variables which are the FF production, pricing, CC regarding policies and management structures and how the greening of the economy has influenced the output and pricing of FF, in the past, currently and the future predictions. As such, this chapter combines different approaches in ascertaining the existence or lack thereof, of these relations. The deductive and qualitative approach taken by this study connects the research concepts, data, and procedures into achieving qualitative and quantitative information. The chapter also brings to perspective the data collection and analysis process in achieving its objectives. The choice of the approaches was guided by the goals of the study as discussed

in the previous chapters.

The time series modeling and prediction have a high significance in a wide range of practical spheres. The various models used overtime for the improvement in precision and time efficacy and structuring series and foretelling the values are as discussed below.

This study uses a VAR approach to capture multiple time series evolution and interdependence.

VAR imposes a theoretical minimum demand on the model’s structure and requires only the

variables and the largest number of lags to capture the inter-effects of utmost variables. The model

was introduced by Sims (1980) in the form of a non-structural econometric movement. The

research methodology estimates a multivariate form of VAR time series using the number of

selected variables in the VAR system. In a multivariate time series analysis, variable (yt),

consisting of n-variables, is characterized by a VAR model of an autoregressive order that is ≤ P.

уt= ξ0 + ∑ ξjуt−k+ τt

Where yt is an (n × 1) matrix, a column vector of daily, weekly or monthly time-series data, and

ξ0 and ξj are (n × 1) and ( n× n) matrix coefficients, respectively. The variable P represents the lag length while rt serves the column vector (n × 1) which is serially uncorrelated error value

terms. The jth term of half-j is used to estimate the immediate influence in k-periods on the ith

value of a variation in jth-value changes. The ith component of t is the ith value innovation

which cannot be predicted from past values of other system variables. VAR (p) denotes the

multivariate statistic–a VAR order process p as written in the equation below (Brüggemann et

al., 2008)

уt = φ0 + φ1xt−1 + ⋯ + φpуt−p + τt

Where φ0 is a column vector (n × 1) of intercept parameters, φi is a coefficient matrix (n × n) for

(i = 1, 2… )andτt is an unobservable vector error term (n × 1) with zero mean and time-

Invariant with ∑τ covariance matrix. The aspects involved in the VAR methodology are

discussed next.

The output obtained in the signal evaluation can be described as an IRA. Changing in an external

dynamic concerning time is the observable general reaction. IRA is capable of identifying the

dynamic associations shown over time and defines the period and Chapter 4 Data and

Methodology 155 effects/ consequence of variables in one variable to another by tracking the

shock to one endogenous variable. The impulse response function identifies the dynamic

relationships between the selected time series. In the case of stationary VAR (p) the model has a

mobile average (MA) that can be written as;

уt= ω+ ∑ Φirt−i

Where ω is the mean of the process, which is equal to Θ0(Iʘm– Θ1 − ⋯ − Θp) −1. The MA

matrix ubiquitous consists of responses to predict πt errors that occurred I periods ago. If the

contemporary residual relationship is high, it becomes difficult to interpret the answers.

Cholesky decomposition was used in the analysis of orthogonalized shocks as obtained by ∑t

Since the outcome of the IRA could be based on the ordering of variables, with some of

the responses used being generalized impulse (Pesaran and Shin, 1998). In the latter method,

shocks are orthogonalized which looks at a shock in variable k and integrates the influences from

the shocks by using the error distribution; thus, the correlation between components is considered. If

πt has a multivariate normal distribution, it is illustrated as

E [rkt| ] = (𝜎1k, 𝜎2k, …,𝜎nk )T𝜎kk −1δk = ∑ek𝜎kk −1tδ k.

Where 𝜎nk represents the elements of ∑t and er is a selection vector (k × 1) With 1 in k position

and 0 elsewhere. Therefore, the response vector to a shock variable k that occurred I times ago is

shown in the equation below:

Where 𝛿𝑘 is then scaled to achieve a standard deviation of size 1 and setting 𝛿𝑘 = √𝜎𝑘𝑘 yields the following equation

This provides a generalized impulse response of variables in yi to the shock variable k that

occurred a few times ago. The IRA can be estimated uniquely and the patterns of historical

correlation observed among the different shocks can be entirely taken into account. These are

invariant to the variable order in the VAR, unlike the orthogonalized impulse responses.

VDA tool is used to appraise multivariate time series models, which shows the contribution of

each variable to the other variables within the context of the method. The methods used an endogenous method to decompose the variables into the component shocks to other variables in the VAR

which aids the derivation of error VDA. The uncertainty associated with error terms includes the

ambiguity in the realization of yt. This method is useful in decreasing the in one equation as to the error in all other equations. VDA represents the h-step in the prediction of error variance in the variable i through

Variable j of VAR (Pesaran and Shin, 1998). The variance of the forecast error is given by the ijth part of

the coefficient of the orthogonalized impulse response matrices.

𝜎𝑡 2 (ℎ) = ∑ (Γ1, 𝑘 2 + Γ2, 𝑘 2 + ⋯ + Γ𝐾, 𝑘2 ) = ℎ−1 𝑘=0 ∑ (Γ𝑡𝑗, 0 2 + Γ𝑡𝑗, 1 2 + ⋯ + Γ𝑡𝑗, ℎ−1 2 )

This test is used to determine the causal attribute between variables. GCA allows examining

which variable influences which and thus to identify which variables have more or less effect. In

simple mathematical terms, the GCA can be described as follows:' variable A' Does not Granger- cause variable B ' if and only if B's predictions based on the Universe (U) of forecasters are no better than B's predictions based on U-[A ']; that is, the Universe with A omitted. The bivariate Granger (1969, 1980) causality test was applied to determine interdependencies. A

The methodological framework supports the use of GCA to determine whether there is one or two-way causality of Granger between variables. Probability conditions frame the definition of causality in terms of Granger. VAR models were frequently used to examine the causality relationships of Granger between two subsets of variables. As noted by Emirmahmutoglu and Kose (2011), in the context of the VAR, the causality of Granger is based on the null hypothesis framed as there are no restrictions on the coefficients of sub-sets of variables. In a bivariate VAR of Ψ and η variables, η does not Granger-cause Ψ if the coefficient metrics are lower triangular for all j.

Ψ1𝑡 = 𝑐1 + 𝑎1Ψ1𝑡−1 + ⋯ + 𝑎𝑝 Ψ1𝑡−𝑝 + 𝑏1 Ψ2𝑡−1 + ⋯ + 𝑏𝑝 Ψ2𝑡−𝑝 + 𝜏1t

If the null hypothesis is accepted, (H0: b1 = ⋯ = bt = 0), Ψ2t will not Granger

cause Ψ1t. Therefore, if Ψ1tdoes Granger-cause Ψ2t, the in a similar manner. The following equation can be written;

Ψ2𝑡 = 𝑐1 + 𝑎1Ψ1𝑡−1 + ⋯ + 𝑎𝑝 Ψ1𝑡−𝑝 + 𝑏1 Ψ2𝑡−1 + ⋯ + 𝑏𝑝 Ψ2𝑡−𝑝 + 𝜏2�

Since many of the macro-economic are non-stationary, the econometric approaches dictate the need to test the stationarity of every discrete time series. The statistics vary on how they showcase a deterministic as well as stochastic drift. Most of the macro-economic variables are different stationary variables, hence the need to use first variances in econometric research. The stationarity of a variable is determined by the time-invariance and the autocovariance of the series between two intervals. Non-stationary variables are those that do not satisfy this requirement. The desire to retain valid typical norms for any asymptotic analysis advises the need to keep valuables stationary. Some of the unit root tests used in the time series include but not limited to Augmented Dickey-Fuller (1979), GLS altered Dickey-Fuller (Elliott et al., 1996) (DF-GLS), Philips-Perron (1988) (PP), Ng and Perron (2001) (NP) and Kwiatkowski et al.

(1992) (KPSS). The optimum unit root evaluation, therefore, determines the stationarity of a sequence.

An ADF test consents to residual sequential associations and also investigates unit roots. The chosen ADF test consisted of a time drift and slope in the level form and only intercept in the initial variation of each variable. By using AIC and SIC and a supreme lag length of 9, one can adopt automatic lag length selection. The Augmented Dickey-Fuller test was based on this regression model.

∆𝑦𝑡 = 𝜑0 + 𝜑1 𝑡 + 𝜑2𝑦𝑡−1 + ∑ 𝜃𝑖∆𝑦𝑡−𝑖𝐿𝑖=1 + 𝜓t

Where 𝜓t= error term for pure white noise adjusting errors in the correlation. It is usually identically distributed and independent. The above equation, ∆𝑦𝑡−𝑖 = 𝑦𝑡 − 𝑦𝑡−(𝑖+1), where ∆y𝑡−I = first degree of differentiation with l lags. The terms 𝜑0, 𝜑1, 𝜑2 and 𝜃𝑖 are an underestimation in the equation

The regression test conducted under ADF is done to identify if there is a unit root of yt. The variable denotes all variables at time t in the expected logarithmic arrangement. The test is performed on the yt−1 coefficient found in the regression. When it is observed that the coefficient is less than zero (i.e., significantly different from 0), the theory that y has a unit root may be rejected. The H0: F2 = 0 and Ha: F2 < 0 of the existence of a unit root in yt is the null and alternative hypothesis. Rejection of the null hypothesis implies stationary statistics within the series.

Alternatively, PP unit root tests can be used because they offer a more comprehensive theory of

non-stationarity in variables. The difference between ADF and PP tests is that the later uses non-parametric statistical techniques to fit in a reflex adjustment to the Dickey-Fuller process; it does

not account for the variable's functional nature of error development. The auto-correction of the

procedure allows for autocorrelated residuals without necessitating the addition of lagged

variance values to the dependent variable through the adoption of the covariance matrix of

Newey and West (1987). Although the conclusions obtained are similar to those of ADF,

calculations within the PP test are complex. The automatic bandwidth selection is engaged in a

PP test and the spectral estimation of the Newey-West bandwidth is taken into account.

The kernel sum-of covariance estimator using Bartlett weights is the default estimator for the distinct

unit root. The null hypothesis for this unit root analysis is that the variable has a unit root.

The ADF-GLS tests recommend a simplified alteration of ADF unit root tests whereby statistics

are subjected to de-trending using generalized least squares, removing the explanatory variables

before running the regression test. The general assumption is that when an unidentified

deterministic drift exists, ADF-GLS has the same supremacy as the Dickey-Fuller in terms of

analyzing the non-existence of a deterministic tendency and enhanced supremacy. The NP test

consists of four separate test statistics based on de-trended data from the GLS. The values are

improved types of values Philips and Perron Zα and Zt, Bhargava (1986) R1 and Elliott et al.

(1996).

The KPSS differs from others since it assumes that the series trends stationarily with the null hypothesis. KPSS statistics are fixed on results from the ordinary least squares regression of the time series variables on the exogenous variables. The critical value statistics for the Lagrange Multiplier (LM) are calculated from the asymptotic outcomes recorded at KPSS. In a KPSS test, one begins by reversing the null hypothesis favoring the unit root and the other hypothesis that evidences stationarity to confirm whether the series will accept or reject the theory of stationarity.

Chapter 4

This section contains the findings from the interviews conducted. There are two ways of reporting the results. The first way involves presenting the key outcomes under the various categories handled while the second consists of incorporating the discussion into the results. This chapter focuses on the second approach which involves discussing the results.

The political conditions of a country have a significant effect on global prices. Political conflicts bring the worst impact even though they are not the only factors that affect the oil industry worldwide. Since the oil industry is a very crucial sector, it is essential to establish proper governance in the producing countries. A participant mentioned the conflicts in 1973 in the Arab countries which led to oil crises globally. As a result, the prices of oil and NG rose up steadily for six consecutive months thus enhancing the rate of inflation in the region and the whole world overall. Some of the interviewees raised the issue of other oil-producing nations that establish strategies which would ensure supply of oil at relatively lower prices especially during crises to control the market. The continued war in the Gulf has also pushed the developed countries to consider establishing environmental protection laws. Global financial crises like the case in 2007-2008 also affect how countries price their petroleum commodities. Financial crises lead to a drop in the demand of oil; thus countries like the OPEC reacts by cutting production to make the global prices stable.

CC has become a common topic as many countries view the importance of stopping the adverse CC experienced recently across the globe. Scientists warn of the threats of global warming to the survival of any living things including humans on the planet. Following the impending dangers, countries led by powerful ones work together to find a way of controlling CC. The union has formulated policies which demand that all states take measures to reduce and if possible eliminate the issue of global warming. Some of the climate policies may affect production, prices, sales and distribution of FF globally. The study addressed how different countries handled the CC policies as follows:

- Countries pledged to enhance and finance environmental protection strategies and climate change justification.
- Some participants emphasized the need to differentiate the roles of developed and developing countries. Other countries also recommended the need to finance and invest in more technological advancements that could help mitigate the issue of CC and foster the achievement of objectives of the CC policies.

Currently, the prices are steady or even low despite economic and political conflicts. Many oil-producing countries have refrained from reducing their oil production rates despite surplus supplies and low oil prices. The act is an indication of an alteration in the individual country’s oil policies. As compared to past trends in the fluctuations in oil productions to create stability in the global oil markets, some states did not cut petroleum production 2014- 2015, even though oil prices reduced remarkably while global supply of the products rose. Experts anticipate a continued increase in the prices of petroleum products concerning the increasing population growth, prosperity, lifestyle and demand in the industrial sector.

Table 1 illustrates the first analysis section. The descriptive statistics begins with the time series data examination. This introduces variables used in the study, evaluating mean, median, skewness kurtosis and the standard deviation at1%,5% and 10% level of significance for the three periods. It serves the purpose of giving a manageable description of the data gathered. The median ratio averages are within unit variables which show a low degree of deviation as also witnessed in the case of the small standard deviation across all level of significance.

The mean is highest when the maximum is lower in oil prices. The testing is completed using kurtosis and skewness in determining the normality. Thus the expectations are the skewness value will have a range of -1 and 1for normality. From the table of the results, the kurtosis does not fall on any of the normal range. The skewness values are negative except for two variables which are close to zero which gives the interpretation of the variables being skewed left with only two skewed right in the first period. The kurtosis provides the interpretation of whether the data peaks or flat-line relative to the normal distribution. This gives the result that the variables do not develop a more Gaussian peaked appearance. Only two variables satisfied this condition.

The tables are used to determine the null hypothesis ( H0) if the data is normally distributed. The result shows that the null hypothesis is accepted. That is the data is normally distributed.

Correlation analysis quantifies the relationship between two continuous variables. The correlation values range between -1 and 1. In the aspect of the correlation being positive shows that the higher number of one variable is associated with higher number of another variable while in the negative aspect it shows that the higher number of one variable is associated with a lower number of the other variable

From the table, table 2, on the correlation matrix in the first period shows quite a weak relation between the variables the values are at 0.04 and -0.03. This indicates that there is no association or rather weak association between the variables.

This result is also shown in the next table in the second period the correlation s are between 0.27** and – 0.07. This gives the impression of a weak association between these variables. So is the case in the third period of the study that has a range between 0.09 and –0.09.

From the whole table, the correlation seems to be close to zero meaning very little almost close to no association at all for these continuous variables in the study.

The correlation analysis is done on the null hypothesis (H0:) is there a relationship between the variable of the study. From the results, the null hypothesis can be rejected outrightly that there is no relationship or association between the variable. What is also seen is a weak association as these values fall close to zero whether positive or negative.

Generally, for VAR(p) model, what is used as the regression predictors are the first p lags of each variable. The standard AIC yields too many lags in a small sample. Several criteria can be used in choosing the optimal lag length in time series. These include; AIC( Akaike information criterion), HQ( Hannan-Quinn criterion), BIC(Schwartcz information criterion ), RMSE( Root Mean Square Error), MAE( Mean Absolute Error), BP(Bias proportion), LIK (Log-Likelihood) The function of discrimination differs from one criterion to another. In this case, the study will use the AIC and HQ in its analysis of the data.

AIC criterion is the relative quality estimator of statistical models for a given set of data. It estimates the relative information amount lost by a model. The model becomes of a higher quality if it loses less information.it deals with both the risk of overfitting or underfitting. It is used to test the hypothesis to compare the means of two normally distributed populations.

HQ like AIC is a selection model criterion.

Suppose we have n models denoted by AIC1, AIC2,….AICn, we take the AICmin to be the minimum value of AIC amongst those values. Thus, the probability of the ith model minimizing the estimated information loss is proportional to the value of the expression exp((AICmin-AICi)/2). The results in table3 give the AIC value of the lag length one at 21.03, 20.60 and 24.03 for the period 1 2 and 3 respectively. In this case, the AIC min is 20.60; thus the probability of the second model minimizing the information is exp((20.60- 21.03)/2)=0.81. And the probability of the third model minimizing the information is exp((20.60-24.03)/2)=0.18. In this case, we omit the third model. From the results, the study concludes that it is quite insufficient supporting the selection of one model from among the first two.

In VAR, it provides a useful framework in investigating both long term and short-term relations of the variables in a system. Stability test should be performed before conducting stability test. This test on the dependent variable is essential since if the VAR model is unstable or stationary, some tests become invalid. The calculations from the roots of the characteristic polynomial give the stability of a VAR model. In this case, the most sufficient and necessary condition of stability is all characteristics root are within unit circle making all characteristic polynomial full rank and variables stationary.

The roots of the characteristic VAR were brought out as defined by FF, CC and all the variables combined and specified for two lags. Figure 1 clearly shows that the inverse roots of AR polynomials lie within the complex unit circle. This indicates that the models will converge. That is, compounding effects will not be in the lagged variable values, and contemporary values stabilize over time.

Besides testing VAR model stability, white noise relative for residual terms was also examined. These tests concluded that the null hypothesis; no rejection to any serial autocorrelation in residuals for lag order addition to testing VAR model stability, we also checked for white noise relative to the

residual terms. The tests were for the three periods all put under consideration and dealing with FF, CC and combined trait models separately are considered representative and stable.

In the beginning, VAR analysis was to determine the variables that are linked significantly. The equations were later constructed for the system of dynamic after model specification completion. Only Sx3 has some significant coefficient that could the considered as either influenced or influential variable. Consequently, there is no interdependence among the variables. The correlation analysis findings support this as shown in the equations each equation expressing a single dependent variable in period t for the variables in the set in earlier two periods. The equation shows that a weak positive coefficient;0.161 with even weaker negative coefficient; -0.002 on the Ry1 and NG return had weak negative lag 1 and weak positive lag 2 effects on Ry1. For coal return, lags of one and two had negative impacts on the global oil price of return. The impact of other FF variables on global oil price returns are given by Equation (2), here the global oil return had a small positive (lag one) and negative (lag two) influence on Ry2. This so happens to Ry3 which also has a negative lag one and a negative lags two.

In general, VAR equations overtime shows, for the time series selected, there is a similarity in the variables of different sets. However, it gives a weak link and probably no link between FF and CC for both lag one and lag two. The mean and median were weak in for FF set variables. All through the equations, there is a weak link up between the variables. It is also evident that it is only global variable co2 that can seem to have a little significance in the temperatures and green index. The green index though had a slight relation to the market prices (positive or negative).

The impulse response is the response by the most dynamics environment to the external change. In the case of the study, it will be to determine interaction speed and duration among variables of FF, CC, and the sets combined. The IRA was based on the general method of Pesaran and Shin(1998) it avoids results variation as a result of ordering of variables a problem witnessed with the Cholesky decomposition method, Eun and shim ( 1989). The IRA results are shown as figures 7.5 showing responses in periods 1, 2 and 3. The response is plotted between 1<-t >-10.

RA was performed on the FF variables, and the effects are shown in figure 7.5.shows that during period1, the shock experienced by the global oil return, the other FF variables seem not to be affected by it. The Saudi oil returns increased before coming to be more or less positive. The natural gas returns dropped to a negative before becoming to be more or less positive In the next period, period 2 the shock on the FF variables are not affected except for the global precipitate that increases a bit before being responsive. This shock sees Saudi oil returns increasing before being more or less positive, and so does the returns of the natural gas. This result is seen being consistent in the third period, period 3.

Variance decomposition analysis (VDA) was done for the FF and CC variables for the three periods. The resulting influence matrix is in table 7.11. From the table the openness of the variables can be inferred, that is, which variable is more influential compared to others. Results are shown concerning the variance forecast percentage of a variable in response to random shocks by other variables in the independent variables. Evident from the table, all FF variable set were affected to a somewhat greater or lesser extent by other variables across all periods, with RY2 being the highly influenced open to other variables being at 84.19% for the three periods. RY3 was the most affected variable the variance of the others. During Period 2, it can be seen that RY3 still becomes the variable that is more open followed by the others who tend to fall to almost zero influence. During Period 3, RY3 and RY both become the variables that are most influenced followed by others who fall close to zero influence.

This procedure is used in determining whether one-time series can be used in forecasting the other. This is achieved by testing the causality in economics. It is used in finding the null hypothesis (H0 🙂 if X granger cause Y. The conclusion of X having granger cause Y is brought out if X can statistically provide significant information about future Y values. This s performed in separate for the three periods. The results for the FF variable as are shown in table7.14 RY3 influenced others at the various though not all pairs had any statistically significant interactions. From the table, period1 there was no much of an influence of the multiple variables on, but it can be seen that the temperature affected the Saudi oil returns. On the second period 2, the global oil affected the returns of Saudi oil while other variables seem not to influence the other variables. From the table, it seems there is little interaction of the variables as it can be seen that the variables do not affect others at any level or if at all at a minimal extent

From the results, it leads to the rejection of the null hypothesis of CC affecting the FF returns in Saudi. It can be noted that there is less information that can be received on the climate change on fossil fuels. The external changes on the green index, the rain precipitate or the change in temperatures have nothing to do with the returns of the fossil fuels. Thus we conclude that X has not Granger cause Y.

This study took a novel approach to address some of the information gaps in the FF sector with keen interests in econometrical and environmental areas in a multivariate analysis. The thesis investigated the interdependence among the FF set indices, cc time series and the set variable’s using the VAR method. Additionally, this study examined interactive influences over three specific periods for the short and long term. In particular, under the assumption that each variable is influenced by its impulse and those of other variables, we evaluated the effects of different shocks on the variables of the VAR system. The variables examined presented features that permit a VAR approach. We tried to find results for IRA, VDA and GCA for 1 January 1978–30 June 2016 (Period 3). For a detailed analysis, the sample period was divided into two sub-periods (before the KP, 1 January 1978–28 February 1997 (period 1) and after the KP, 1 March 1997–30 June 2016 (period 2)).

In summary, this chapter’s methods provide a better understanding of the overall picture of the relationship between and among the selected variables (FF and CC variable sets), especially during the analyzed periods. The analyzers responded to the goals and hypotheses through empirical results which include correlation matrix tests for variables which show that some variables in the FF, CC and combined CC and FF sets were highly correlated in several periods. The VAR results show that there is a strong (positive/negative) relationship between the FF, CC and the short and long term combined CC and FF sets. Additionally, the IRA results indicate an increasing integration between variables over time. The results reveal that for Periods 1, 2 and 3, each variable in the FF or CC variable set responds efficiently to shocks other variables. Shocks in CC variables are transmitted to some of the FF variables. The VDC results show that many variables are significantly influenced by movements in other variables given that the different variables become less open to the effects of others. GCA was performed separately for two sets of variables and in combination to determine the direct causality between the pairs of variables. Granger causality indicated that one-way and two-way causality existed between any pair of FF, CC and combined CC and FF variable sets. Finally, the indices of the FF, CC and combined CC and FF sets are closely linked and respond to each other quite quickly and within both short and long periods.

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