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.

Trade and tech uncertainty will boost ‘safer’ assets

2019 ◽  
Keyword(s):  
Stock Market ◽  
Emerging Markets ◽  
Crude Oil ◽  
Stock Markets ◽  
Oil Price ◽  
Crude Oil Price ◽  
Government Bonds ◽  
Content Type ◽  
The Us ◽  
New Phase

Significance US President Donald Trump’s decision last month to intensify the US-China conflict by raising the tariff rate and targeting Chinese tech firms is straining stock markets and making government bonds more attractive. Marking a dangerous new phase, sentiment towards the tech sector is deteriorating, after powering the stock market 'bull run' for a decade. Impacts Uncertainty over both US policy and geopolitics globally will continue to make the dollar more attractive, outweighing Fed dovishness. Emerging markets enjoyed a surge in inflows from January-April 2019, but suffered sharp outflows in May, and investors will remain cautious. The VIX Index, Wall Street’s so-called ‘fear gauge’, has surged by around 50% since May 3, and is likely to remain elevated. Rising US output means that the Brent crude oil price is likely to stabilise rather than rebound, having fallen by about 20% since April.

Volatility spillovers between crude oil price and stock markets: evidence from BRIC countries

2017 ◽  
Vol 12 (2) ◽  
pp. 352-365 ◽  
Author(s):  
Bhaskar Bagchi
Keyword(s):  
Crude Oil ◽  
Long Memory ◽  
Stock Markets ◽  
Stock Price ◽  
Price Volatility ◽  
Oil Price ◽  
Crude Oil Price ◽  
Content Type ◽  
Bric Countries ◽  
Crude Oil Prices

Purpose The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times. Design/methodology/approach The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects. Findings For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries. Originality/value The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

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.

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 THE IMPACT OF OIL PRICE ON STOCK MARKET:EMPIRICAL EVIDENCE FROM US

2021 ◽  
Vol 9 (1) ◽  
pp. 330-337
Author(s):  
Shanaz hakim , Tugut Tursoy,
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Negative Impact ◽  
Positive Impact ◽  
Oil Price ◽  
P Value ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Interactive Relationship ◽  
The Impact

The analysis of this research focuses on the interactive relationship among the fluctuation of crude oil prices, the real GDP and the stock market of United State. This empirical investigation uses data is in between 1990 and 2018 with the Vector Auto-regression (VAR) analysis, and multiple regressions with its assumption were used in order to analyses data.  Findings, oil price and economic growth are very important determinates of stock market in US because the p-value of this were less than the common alpha α =0.05. For instance, the crude oil price had positive impact on stock market because for each unit increasing of crude oil price, the stock market will increase by (0.276901) after holding all other variable constant. However, we find that GDP has negative impact on the participations of increasing the stock market.

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.

scholarly journals An ARDL Approach on Crude Oil Price and Macroeconomic Variables

2019 ◽  
Vol 4 (1) ◽  
pp. 68-73
Author(s):  
Seuk Yen Phoong ◽  
Seuk Wai Phoong
Keyword(s):  
Economic Growth ◽  
Crude Oil ◽  
Unemployment Rate ◽  
Positive Relationship ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Macroeconomic Variables ◽  
Crude Oil Prices ◽  
Ardl Approach

Objective - The removal of fuel subsidies by the Malaysian government in 2014 has been implement with the managed float system for fuel prices. Methodology/Technique - This study investigates the impact of the managed floating system of crude oil prices on the Malaysian economy using ARDL approach by looking at macroeconomic variables such as inflation, economic growth and unemployment rates. Findings - The results show that all of the variables have short lived relationship with oil prices whereby inflation and economic growth are positively related to oil prices. However, unemployment rate has a negative relationship with the changes of WTI crude oil prices. Novelty - The major input in the economy of Malaysia contributes to a positive relationship between inflation and oil prices, whilst the contribution of Malaysia being an oil-producing country results in the positive relationship of economic growth and oil price. Likewise, as oil prices are high, the increase in demand results in increase in job opportunities. Lastly, the correlation test shows that inflation and economic growth have a high positive correlation while unemployment rate has a low negative correlation with oil price. Type of Paper: Empirical. Keywords: ARDL; Crude Oil Price; GDP; Inflation; Unemployment. JEL Classification: E10, E30, E39. DOI: https://doi.org/10.35609/jber.2019.4.1(8)

scholarly journals MODEL KOMBINASI ARIMA DALAM PERAMALAN HARGA MINYAK MENTAH DUNIA

2018 ◽  
Vol 7 (1) ◽  
pp. 54-63
Author(s):  
Eka Setiyowati ◽  
Agus Rusgiyono ◽  
Tarno Tarno
Keyword(s):  
Crude Oil ◽  
Everyday Life ◽  
Oil Prices ◽  
Oil Price ◽  
Ensemble Method ◽  
Economic Conditions ◽  
Arima Models ◽  
Crude Oil Price ◽  
Crude Oil Prices

Oil is the most important commodity in everyday life, because oil is one of the main sources of energy that is needed for other people. Changes in crude oil prices greatly affect the economic conditions of a country.  Therefore, the aim of this study is develop an appropriate model for forecasting crude oil price based on the ARIMA and its ensembles. In this study, ensemble method uses some ARIMA models to create ensemble members which are then combined with averaging and stacking techniques. The data used are the price of world crude oil period 2003-2017. The results showed that ARIMA (1,1,0) model produces the smallest RMSE values for forecasting the next thirty six months. Keywords: Ensemble, ARIMA, Averaging, Stacking, Crude Oil Price

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