Oil Prices, Stock Markets and Firm Performance: Evidence from Europe

Oil prices, stock markets and firm performance: Evidence from Europe

International Review of Economics & Finance ◽

10.1016/j.iref.2019.02.007 ◽

2019 ◽

Vol 61 ◽

pp. 270-288 ◽

Cited By ~ 11

Author(s):

Miramir Bagirov ◽

Cesario Mateus

Keyword(s):

Firm Performance ◽

Stock Markets ◽

Oil Prices

Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam

Journal of International Financial Markets Institutions and Money ◽

10.1016/j.intfin.2012.03.004 ◽

2012 ◽

Vol 22 (4) ◽

pp. 758-773 ◽

Cited By ~ 88

Author(s):

Cuong C. Nguyen ◽

M. Ishaq Bhatti

Keyword(s):

Stock Markets ◽

Oil Prices ◽

Copula Model ◽

Model Dependency

WTI OIL SHOCKS IN EASTERN EUROPEAN STOCK MARKETS: A VAR APPROACH

10.31410/eman.2021.71 ◽

2021 ◽

Author(s):

Rui Dias ◽

◽

Hortense Santos ◽

Paula Heliodoro ◽

Cristina Vasco ◽

...

Keyword(s):

Stock Markets ◽

Oil Prices ◽

Oil Price ◽

Portfolio Diversification ◽

Oil Shocks ◽

Eastern European ◽

Safe Haven ◽

Wald Tests ◽

Stock Indices ◽

European Stock Markets

The 2020 Russia-Saudi Oil Price War was an economic war triggered in March 2020 by Saudi Arabia in response to Russia’s refusal to reduce oil production to keep oil prices at a moderate level. In view of these events, this study aims to analyze oil shocks (WTI) in the eastern European stock markets, namely the stock indices of Hungary (BUX), Croatia (CROBE), Russia (MOEX), Czech Republic (PRAGUE), Slovakia (SAX 16), Slovenia (SBI TOP), Bulgaria (SOFIX), from September 2019 to January 2021. The results show mostly structural breakdowns in March 2020, while the VAR Granger Causality/Block Exogeneity Wald Tests model shows two-way shocks between oil (WTI) and the stock markets analyzed. These findings show that the hypothesis of portfolio diversification may be called into question. As a final discussion, we consider that investors should avoid investments in stock markets, at least as long as this pandemic last, and rebalance their portfolios into assets considered “safe haven” for the purpose of mitigating risk and improving the efficiency of their portfolios.

Measuring Contagion between Oil Prices and Stock Markets in the GCC Countries Using a Hybrid Artificial Neural Network Model

International Journal of Trade Economics and Finance ◽

10.18178/ijtef.2016.7.4.505 ◽

2016 ◽

Vol 7 (4) ◽

pp. 93-96

Author(s):

M. El Shazly ◽

◽

A. Lou

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Stock Markets ◽

Artificial Neural Network Model ◽

Oil Prices ◽

Gcc Countries ◽

Artificial Neural ◽

Hybrid Artificial Neural Network

A Note on ‘What Drives Share Prices in the Middle East?’

International Journal of Banking and Finance - Vol. 15, Number 2, 2020 ◽

10.32890/ijbf2008.5.2.8370 ◽

2008 ◽

Author(s):

Panos Priftakis ◽

M. Ishaq Bhatti

Keyword(s):

Middle East ◽

Stock Market ◽

Stock Returns ◽

Stock Markets ◽

Predictive Ability ◽

Oil Prices ◽

Unobserved Components ◽

Market Prices ◽

Share Prices ◽

Unobserved Components Model

There are several hypotheses suggesting that some properties of oil prices make it interesting to focus on the predictive ability of oil prices for stock returns. This paper reviews some models recently used in the literature and selects the most suitable one for measuring the relationships and/or linkages of oil prices to the stock markets of the selected five oil producing countries in the Middle East. In particular, the paper uses two methodologies to test for the presence of a cointegrating relationship between the two variables and an unobserved components model to find a relationship between the two variables. The results rejects convincingly that there is no linkage between the prices of oil and the stock market prices in these oil-based economies.

Comovements and Volatility Spillovers Between Oil Prices and Stock Markets: Further Evidence for Oil-Exporting and Oil-Importing Countries

Emerging Markets and the Global Economy ◽

10.1016/b978-0-12-411549-1.00016-8 ◽

2014 ◽

pp. 371-382 ◽

Cited By ~ 1

Author(s):

Khaled Guesmi

Keyword(s):

Stock Markets ◽

Oil Prices ◽

Volatility Spillovers

Oil prices, exchange rates and stock markets under uncertainty and regime-switching

Finance Research Letters ◽

10.1016/j.frl.2018.02.032 ◽

2018 ◽

Vol 27 ◽

pp. 28-33 ◽

Cited By ~ 31

Author(s):

David Roubaud ◽

Mohamed Arouri

Keyword(s):

Exchange Rates ◽

Stock Markets ◽

Regime Switching ◽

Oil Prices

Linking crude oil prices and Middle East stock markets

OPEC Energy Review ◽

10.1111/opec.12145 ◽

2019 ◽

Vol 43 (2) ◽

pp. 136-167 ◽

Cited By ~ 2

Author(s):

Mousa Tawfeeq ◽

Alan R. Collins ◽

Levan Elbakidze ◽

Gulnara Zaynutdinova

Keyword(s):

Middle East ◽

Crude Oil ◽

Stock Markets ◽

Oil Prices ◽

Crude Oil Prices

Dynamic Linkages between Crude Oil Prices, Exchange Rates, and Stock Markets of Other Emerging Economies

Dynamic Linkages and Volatility Spillover ◽

10.1108/978-1-78635-554-620161006 ◽

2016 ◽

pp. 111-141

Keyword(s):

Exchange Rates ◽

Crude Oil ◽

Emerging Economies ◽

Stock Markets ◽

Oil Prices ◽

Crude Oil Prices ◽

Dynamic Linkages

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

Kybernetes ◽

10.1108/k-04-2017-0139 ◽

2018 ◽

Vol 47 (6) ◽

pp. 1242-1261 ◽

Cited By ~ 2

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.