scholarly journals Nexus between crude oil prices, clean energy investments, technology companies and energy democracy

Green Finance ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 337-350
Author(s):  
Caner Özdurak ◽  
Keyword(s):  
Crude Oil ◽  
Stock Returns ◽  
Technology Development ◽  
Oil Prices ◽  
Clean Energy ◽  
Oil Price ◽  
Oil Futures ◽  
Company Stock ◽  
Volatility Index ◽  
Crude Oil Prices

<abstract> <p>In this study, we examine the nexus between crude oil prices, clean energy investments, technology companies, and energy democracy. Our dataset incorporates four variables which are S &amp; P Global Clean Energy Index (SPClean), Brent crude oil futures (Brent), CBOE Volatility Index (VIX), and NASDAQ 100 Technology Sector (DXNT) daily prices between 2009 and 2021. The novelty of our study is that we included technology development and market fear as important factors and assess their impact on clean energy investments. DCC-GARCH models are utilized to analyze the spillover impact of market fear, oil prices, and technology company stock returns to clean energy investments. According to our findings when oil prices decrease, the volatility index usually responds by increasing which means that the market is afraid of oil price surges. Renewable investments also tend to decrease in that period following the oil price trend. Moreover, a positive relationship between technology stocks and renewable energy stock returns also exists.</p> </abstract>

scholarly journals The Relation between Implied Volatility Index and Crude Oil Prices

2019 ◽  
Vol 30 (5) ◽  
pp. 556-566
Author(s):  
Imlak Shaikh
Keyword(s):  
Neural Network ◽  
Quantile Regression ◽  
Crude Oil ◽  
Implied Volatility ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Futures ◽  
Volatility Index ◽  
Crude Oil Prices ◽  
Volatility Feedback

Crude oil is a global commodity traded across the world market. The prices of the commodity over an extended period for crude oil have been analyzed using daily prices of crude oil futures and the implied volatility index (OVX). This paper aims to find the predictability of various parameters on the basis of time using neural network and quantile regression methods. Several estimates have been shown based on Barone, Adesi, and Whaley’s (BAW) model of neural network. Estimation parameters include opening, closing, highest and lowest price of the commodity and volumes traded for a given commodity on each trading day. The neural network estimates explain that future prices of the WTI/USO can be predicted with minimal error, and similar can be used to predict future volatility. The quantile regression results suggest that crude oil prices and OVX are strongly associated. The asymmetric association between the WTI/USO and OVX explains that the volatility feedback effect holds good for the OVX market. Bai and Perron least squares estimate evidence of the presence of a break in the time series. The main results uncover several interesting facts that implied volatility tends to remain calm during the global financial crises and higher throughout the post crisis period. The empirical outcome on the OVX market provides some practical implications for the trader and investor, in which oil futures can serve better to hedge the crude price volatility. The crude oil producer can short hedge enough through volatility futures and options to maintain the future quantity of crude to be produced.

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

The optimal thermal causal path analysis on the relationship between international crude oil price and stock market

Kybernetes ◽  
2018 ◽  
Vol 47 (6) ◽  
pp. 1242-1261 ◽  
Author(s):  
Can Zhong Yao ◽  
Peng Cheng Kuang ◽  
Ji Nan Lin
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Markets ◽  
Oil Prices ◽  
Oil Price ◽  
Chinese Stock Market ◽  
Crude Oil Price ◽  
Content Type ◽  
Crude Oil Prices ◽  
Causal Path

Purpose The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets. Design/methodology/approach The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method. Findings The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices. Originality/value China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.

scholarly journals Dynamic Interrelationship and Volatility Spillover among Sustainability Stock Markets, Major European Conventional Indices, and International Crude Oil

2020 ◽  
Vol 12 (9) ◽  
pp. 3908 ◽  
Author(s):  
Basel Maraqa ◽  
Murad Bein
Keyword(s):  
Crude Oil ◽  
Stock Returns ◽  
Global Financial Crisis ◽  
Oil Prices ◽  
Volatility Spillover ◽  
Socially Responsible ◽  
Crude Oil Prices ◽  
Daily Data ◽  
Oil Markets ◽  
The Global Financial Crisis

This study examines the dynamic interrelationship and volatility spillover among stainability stock indices (SSIs), international crude oil prices and major stock returns of European oil-importing countries (UK, Germany, France, Italy, Switzerland and The Netherlands) and oil-exporting countries (Norway and Russia). We employ the DCC-MGARCH model and use daily data for the sample period from 28 September 2001 to 10 January 2020. We find that the dynamic interrelationship between SSIs, stock returns of European oil importing/exporting countries and oil markets is different. There is higher correlation between SSIs and oil-importing countries, while oil-exporting countries have higher correlation with the oil market. Notably, the correlation between oil and stock returns became higher during and after the global financial crisis. This study also reveals the existence of significant volatility spillover between sustainability stock returns, international oil prices and the major indices of oil importing/exporting countries. These results have important implications for investors who are seeking to hedge and diversify their assets and for socially responsible investors.

scholarly journals Crude oil prices and sectoral stock returns in Jordan around the Arab uprisings of 2010

2016 ◽  
Vol 56 ◽  
pp. 205-214 ◽  
Author(s):  
Elie Bouri ◽  
Basel Awartani ◽  
Aktham Maghyereh
Keyword(s):  
Crude Oil ◽  
Stock Returns ◽  
Oil Prices ◽  
Arab Uprisings ◽  
Crude Oil Prices

Common Trends and Common Cycles in Oil Price and Real Exchange Rate

2014 ◽  
Vol 14 (2) ◽  
pp. 249-263 ◽  
Author(s):  
Hem C. Basnet ◽  
Puneet Vatsa ◽  
Subhash Sharma
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Crude Oil ◽  
Real Exchange Rates ◽  
Oil Prices ◽  
Oil Price ◽  
Cycle Decomposition ◽  
The Real ◽  
Crude Oil Prices ◽  
Common Cycles

This study explores the long- and short-run movement between oil prices and the real exchange rates of two large oil-exporting countries – Canada and Norway. Cointegration and serial correlation common features tests are jointly used to identify the long-term common trend and short-term common cycles. Our test results find that oil prices and the real exchange rates of the Canadian Dollar and the Norwegian Krone have two shared trends and one shared cycle. The trend–cycle decomposition shows a great deal of positive comovement among the trend and cyclical components. The two currencies show economic dynamics very similar to crude oil prices. They do not exhibit any qualitative differences in the trajectory of the trend and cycles when controlling for different crude oil prices. Our results indicate that oil price fluctuations play significant role in explaining the exchange rate movements of oil-exporting countries.

scholarly journals Impact of FDI inflow, crude oil prices, and economic growth on CO2 emission in Tunisia: Symmetric and asymmetric analysis through ARDL and NARDL approach

2021 ◽  
Vol 12 (1) ◽  
pp. 1-13
Author(s):  
Tarek Ghazouani
Keyword(s):  
Economic Growth ◽  
Crude Oil ◽  
Carbon Emissions ◽  
Co2 Emission ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Fdi Inflows ◽  
Crude Oil Prices ◽  
Long Run

This study explores the symmetric and asymmetric impact of real GDP per capita, FDI inflow, and crude oil price on CO2 emission in Tunisia for the 1972–2016 period. Using the cointegration tests, namely ARDL and NARDL bound test, the results show that the variables are associated in a long run relationship. Long run estimates from both approach confirms the validity of ECK hypothesis for Tunisia. Symmetric analysis reveals that economic growth and the price of crude oil adversely affect the environment, in contrast to FDI inflows that reduce CO2 emissions in the long run. Whereas the asymmetric analysis show that increase in crude oil price harm the environment and decrease in crude oil price have positive repercussions on the environment. The causality analysis suggests that a bilateral link exists between economic growth and carbon emissions and a one-way causality ranges from FDI inflows and crude oil prices to carbon emissions. Thus, some policy recommendations have been formulated to help Tunisia reduce carbon emissions and support economic development.

scholarly journals Relationship between International Crude Oil Price and Indian Stock Market (NSE)

2011 ◽  
pp. 63-73
Author(s):  
Rajendra Mahunta
Keyword(s):  
Crude Oil ◽  
Global Economy ◽  
Stock Exchange ◽  
Oil Prices ◽  
Oil Price ◽  
Raw Material ◽  
Crude Oil Price ◽  
New Era ◽  
Crude Oil Prices

In this new era of economic growth, the exceptional increase in the crude oil prices is one of the significant developments that affect the global economy. Crude oil is an important raw material used for manufacturing sectors, so that increase in the price of oil is bound to warn the economy with inflationary inclination. The study examine the long-term relationships between CNX NIFTY FIFTY index of National Stock Exchange and crude price by using various econometric test. The surge in crude oil prices during recent years has generated a lot of interest in the relationship between oil price and equity markets. The study covers the period between 01.01.2010 and 31.12.2014 and was performed with data consisting of 1245 days. The empirical results show there was a cointegrated long-term relationship between CNX index and crude price. Granger causality results reveal that there is unidirectional causality exists and crude oil price causes NSE (CNX) but NSE (CNX) does not cause oil price.

Oil prices complicate Latin America outlook

2015 ◽  
Keyword(s):  
Latin America ◽  
Crude Oil ◽  
Investment Decisions ◽  
Oil Prices ◽  
Price Impact ◽  
Oil Price ◽  
Content Type ◽  
Crude Oil Prices ◽  
The World ◽  
Alternative Energies

Subject 'Winners' and 'losers' from the recent collapse in oil prices. Significance The recent precipitate fall in crude oil prices, with the Brent crude price falling below 50 dollars/barrel in January (less than half its September 2014 level), is clearly having a major impact around the world. In Latin America, which includes both oil importing and exporting countries, there will be winners and losers from this development, although in some cases the oil price impact is likely to prove more nuanced. Impacts Plunging oil prices are compounding doubts surrounding the regional hydrocarbons sector. The effect on investment decisions will have a longer-term impact on the region. The development of alternative energies in Latin America will be hit by the lower prices.

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