scholarly journals Moving Average Market Timing in European Energy Markets: Production Versus Emissions

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3281
Author(s):  
Chia-Lin Chang ◽  
Jukka Ilomäki ◽  
Hannu Laurila ◽  
Michael McAleer
Keyword(s):  
Renewable Energy ◽  
Carbon Emission ◽  
Oil And Gas ◽  
Moving Average ◽  
Market Timing ◽  
Asset Markets ◽  
Price Series ◽  
Stochastic Trends ◽  
Long Run ◽  
Returns Predictability

This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA futures and ICE Brent oil futures (reflecting the two largest energy sources in Europe), Stoxx600 Europe Oil and Gas Index (the main energy stock index in Europe), EEX Power Futures (representing electricity), and Stoxx600 Europe Renewable Energy index (representing the sunrise energy industry). This paper finds that the Moving Average (MA) technique beats random timing for carbon emission allowances, coal, and renewable energy. In these asset markets, there seems to be significant returns predictability of stochastic trends in prices. The results are mixed for Brent oil, and there are no predictable trends for the Oil and Gas index. Stochastic trends are also missing in the electricity market as there is an ARFIMA-FIGARCH process in the day-ahead power prices. The empirical results are interesting for several reasons. We identified the data generating process in EU electricity prices as fractionally integrated (0.5), with a fractionally integrated Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process in the residual. This is a novel finding. The order of integration of order 0.5 implies that the process is not stationary but less non-stationary than the non-stationary I(1) process, and that the process has long memory. This is probably because electricity cannot be stored. Returns predictability with MA rules requires stochastic trends in price series, indicating that the asset prices should obey the I(1) process, that is, to facilitate long run returns predictability. However, all the other price series tested in the paper are I(1)-processes, so that their returns series are stationary. The empirical results are important because they give a simple answer to the following question: When are MA rules useful? The answer is that, if significant stochastic trends develop in prices, long run returns are predictable, and market timing performs better than does random timing.

Does Technological Innovation Advance Environmental Sustainability in ASEAN Countries?

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Tabish Nawab ◽  
Muhammad Azhar Bhatti ◽  
Muhammad Atif Nawaz
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Environmental Sustainability ◽  
Energy Demand ◽  
Carbon Emission ◽  
Technology Innovation ◽  
Renewable Energy Consumption ◽  
Panel Ardl ◽  
Long Run ◽  
Environment Degradation

Environment degradation is a very important issue in developing nations and a lot of research had done to examine the factors of environmental degradation but these studies were missed some important factors which are covered by this study. By examining the effect of economic growth and energy in the presence of renewable energy consumption and technology innovation on environment degradation for ASEAN nations. Panel ARDL (which is PMG and MG) is used to estimate the model, and the advantage of this model is it gives both the long and short-run estimates of the model which helps to understand the situation in both short as well as long run. The results confirm that economic growth, Population, trade, and renewable energy increase the carbon emission level in ASEAN nations. While technology innovation decreased carbon emission levels which means technology innovation helps to keep the environment healthy and clean. Hence, economic growth helps the nations to improve their energy mode from non-renewable to renewable energy, which meets the energy demand by keeping the environment clean.

scholarly journals Long Run Returns Predictability and Volatility with Moving Averages

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 105 ◽  
Author(s):  
Chia-Lin Chang ◽  
Jukka Ilomäki ◽  
Hannu Laurila ◽  
Michael McAleer
Keyword(s):  
Financial Performance ◽  
Stock Returns ◽  
Moving Average ◽  
Long Run ◽  
Returns Predictability ◽  
Rolling Window ◽  
Risk Free Rate ◽  
Data Frequency ◽  
Predictability Of Returns ◽  
Free Rate

This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.

Agricultural land usage and tourism impact on renewable energy consumption among Coastline Mediterranean Countries

2018 ◽  
Vol 29 (8) ◽  
pp. 1438-1454 ◽  
Author(s):  
Andrew Adewale Alola ◽  
Uju Violet Alola
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Carbon Emission ◽  
Agricultural Land ◽  
Tourism Development ◽  
Renewable Energy Consumption ◽  
Land Usage ◽  
Mediterranean Countries ◽  
Long Run ◽  
Short Run

Abstact This empirical study aims to investigate the dynamic response of renewable energy consumption to long-run disequilibrium and short-run impact of tourism development and agricultural land usage for the period of 1995 to 2014 in 16 Coastline Mediterranean Countries. For this reason, a dynamic Autoregressive Distributed Lag approach is employed in a multivariate and two-model framework such that carbon emission and gross domestic product are being controlled for in the models. Significantly, there is evidence of a joint impact of tourism development and agricultural land usage on renewable energy consumption. With a speed of adjustment of 21.6% from short-run disequilibrium to long run, their respective panel elasticities are 0.33 and negative 1.60 in the long run. Significant evidence shows that nine of the Coastline Mediterranean Countries have tourism development as a short-run factor while Slovenia and Cyprus exhibit a short-run common factor. Also, Granger causality evidences from carbon emission, gross domestic product and tourism development to renewable energy are all with feedbacks. However, Granger causality from agricultural land usage to renewable energy is without feedback. In the region, effective policy implementations through the collaborative effort of stakeholders will ensure a sustainable renewable energy development amidst agricultural and tourism activities.

Assessing symmetric price transmission by using threshold cointegration in Iranian egg market

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Aghabeygi ◽  
Federico Antonioli ◽  
Filippo Arfini
Keyword(s):  
Egg Production ◽  
Autoregressive Process ◽  
Commodity Price ◽  
Price Transmission ◽  
Content Type ◽  
Price Series ◽  
Stochastic Trends ◽  
Long Run ◽  
Cointegration Tests ◽  
Misspecification Error

Purpose Eggs bear an essential role in Iranian diet, primarily for their protein content. The egg production strictly depends on the price of inputs, that is corn used for poultry feeding. The upsurge in corn prices in recent years gave rise to both consumers’ and producers’ dissatisfaction, increasing production cost and the egg price in the final market. The purpose of this paper is to investigate the price-transmission dynamics between corn and retail egg prices in Iran. Design/methodology/approach Individual commodity price series generally contain stochastic trends and they are non-stationary. Standard unit root and cointegration tests will be conducted in order to determine whether price series are stationary and whether they are cointegrated, respectively. The existence of cointegration between the two-price series depends on the nature of autoregressive process. If there is an asymmetric convergence between two variables, then Engle and Granger’s (1987) test can have a misspecification error and the result cannot indicate nature of variables. Threshold or asymmetric convergence test should be used, which can detect the asymmetric behavior of variables and threshold effects on series. Findings Results showed that, in the long run, owing to price transmission, any price shocks on corn price will be transmitted to the egg price. Practical implications Policy makers should implement input and output price policies to support producer and consumer in the retail market to increase consumer and producer welfare, and they should also control intermediaries in this market. Originality/value This research dealing with price transmission has been concerned only with applying time-series modeling techniques to price data. The main focus of this approach has been to characterize vertical price relationships by the extent, speed and nature of the adjustments through the supply chain to market shocks generated at different levels in the marketing process. Thus, it complements the marketing margin models, which are mainly concerned with testing for market imperfections and calculating the price transmission. Besides these points, particular importance has been given in this research to the question of symmetry of price adjustments.

scholarly journals The Effects of Energy Consumption and National Output on CO2 Emissions: New Evidence from OIC Countries Using a Panel ARDL Analysis

2020 ◽  
Vol 12 (8) ◽  
pp. 3312 ◽  
Author(s):  
Mohd Shahidan Shaari ◽  
Zulkefly Abdul Karim ◽  
Noorazeela Zainol Abidin
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Co2 Emissions ◽  
Oil And Gas ◽  
Oil Consumption ◽  
Panel Ardl ◽  
Long Run ◽  
Short Run ◽  
National Output ◽  
Gas Consumption

The issue of energy has been debated among policymakers and economists. Energy plays an important role in generating economic activities. On the other hand, it can have deleterious impacts on the environment as more carbon dioxide (CO2) emissions will be released. Most previous studies focused on total energy rather than types of energy such as oil and gas in investigating the effects of energy consumption on CO2 emissions. Therefore, this study investigates the effects of oil and gas consumption rather than total energy consumption on CO2 emissions in 20 Organization of Islamic Cooperation (OIC) countries. The dynamic heterogeneous panel (panel autoregressive distributed lag model – panel ARDL) approach namely pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE) were employed. The main results reveal that in the long run, overall national output contributes to higher environmental degradation. However, in the short run, overall national output does not affect CO2 emissions. The results also suggest that the population can reduce CO2 emissions in the short run but leaves no effect in the long run. Besides, gas consumption and oil consumption can have deleterious effects on the environment. The effect of oil consumption is greater than the effect of gas consumption on the environment. Therefore, it is important to consume more renewable energy such as solar, biodiesel, and hydro to replace non-renewable energy, particularly oil, in a bid to conserve the environment.

scholarly journals Renewable Energy Consumption, Trade Openness, and Environmental Degradation: A Panel Data Analysis of Developing and Developed Countries

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hayat Khan ◽  
Liu Weili ◽  
Itbar Khan ◽  
Sikeo Khamphengxay
Keyword(s):  
Developing Countries ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Environmental Quality ◽  
Carbon Emission ◽  
Trade Openness ◽  
Developed Countries ◽  
Renewable Energy Consumption ◽  
Long Run

Studies regarding environmental degradation and its association with different factors have got considerable attention recently in the prevalent literature but with assorted outcomes which have been a guide to the ongoing debate on environmental studies. Energy from renewable sources has been considered beneficial for environmental quality while it is still below the anticipated level especially in developing economies. Openness to trade is important to enhance economic growth while it has been overawed to worsen the quality of environment due to deprived policies especially in developing countries. Subsequently, the present research investigates trade openness, renewable energy consumption, and foreign direct investment in carbon emission in the world developing and developed countries by employing static, dynamic and long run estimators. Trade openness has been found to have a decreasing effect on carbon emission in developed countries while degrading the quality of environment in developing countries while renewable energy consumption enhances environmental quality in both samples. The impact of tourism on carbon emission varies in different samples where FDI increases emission in developed countries while having a negative effect of carbon emission in developing countries. The long run estimators also evidence the existence of long run association among variables. The outcomes of this study have considerable policy implication regarding trade openness policy formulation to upsurge environmental quality especially in developing countries. The study has further suggestions regarding tourism and promoting the use of renewable energy sources by avoiding the use of former’s energy to enhance environmental quality.

Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 181
Author(s):  
Imriani Moroki ◽  
Alfrets Septy Wauran
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Moving Average ◽  
Arima Model ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Energy Potential ◽  
Good Design ◽  
Potential Sources ◽  
The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of  the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

scholarly journals New Glycerol Upgrading Processes

Catalysts ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 103
Author(s):  
Miguel Ladero
Keyword(s):  
Renewable Energy ◽  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Oil And Gas ◽  
The Other ◽  
Energy Policies ◽  
Gas Recovery ◽  
The Us ◽  
The Eu

Energy policies in the US and in the EU during the last decades have been focused on enhanced oil and gas recovery, including the so-called tertiary extraction or enhanced oil recovery (EOR), on one hand, and the development and implementation of renewable energy vectors, on the other, including biofuels as bioethanol (mainly in US and Brazil) and biodiesel (mainly in the EU) [...]

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