The Effect of the Relationship Between Oil Price and Stock Markets in Energy Sustainable Countries

2016 ◽  
pp. 123-144
Author(s):  
Şahnaz Koçoğlu ◽  
Mehmet Baha Karan ◽  
Ayhan Kapusuzoğlu
Keyword(s):  
Stock Markets ◽  
Oil Price ◽  
The Relationship

scholarly journals The Regime-Switching Behaviour of Exchange Rates and Frontier Stock Market Prices in Sub-Saharan Africa

2021 ◽  
Vol 14 (3) ◽  
pp. 122
Author(s):  
Maud Korley ◽  
Evangelos Giouvris
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Regime Switching ◽  
Markov Switching ◽  
Sub Saharan Africa ◽  
Var Model ◽  
High Volatility ◽  
Sub Saharan ◽  
The Relationship ◽  
Fx Market

Frontier markets have become increasingly investible, providing diversification opportunities; however, there is very little research (with conflicting results) on the relationship between Foreign Exchange (FX) and frontier stock markets. Understanding this relationship is important for both international investor and policymakers. The Markov-switching Vector Auto Regressive (VAR) model is used to examine the relationship between FX and frontier stock markets. There are two distinct regimes in both the frontier stock market and the FX market: a low-volatility and a high-volatility regime. In contrast with emerging markets characterised by “high volatility/low return”, frontier stock markets provide high (positive) returns in the high-volatility regime. The high-volatility regime is less persistent than the low-volatility regime, contrary to conventional wisdom. The Markov Switching VAR model indicates that the relationship between the FX market and the stock market is regime-dependent. Changes in the stock market have a significant impact on the FX market during both normal (calm) and crisis (turbulent) periods. However, the reverse effect is weak or nonexistent. The stock-oriented model is the prevalent model for Sub-Saharan African (SSA) countries. Irrespective of the regime, there is no relationship between the stock market and the FX market in Cote d’Ivoire. Our results are robust in model selection and degree of comovement.

Exploring the inflationary effect of oil price in the US

2019 ◽  
Vol 13 (1) ◽  
pp. 60-76 ◽  
Author(s):  
Amine Lahiani
Keyword(s):  
Growth Rate ◽  
Structural Breaks ◽  
Oil Prices ◽  
Oil Price ◽  
Inflation Expectations ◽  
Oil Price Shocks ◽  
Price Shocks ◽  
Content Type ◽  
The Us ◽  
The Relationship

PurposeThe purpose of this paper is to explore the effect of oil price shocks on the US Consumer Price Index over the monthly period from 1876:01 to 2014:04.Design/methodology/approachThe author uses the Bai and Perron (2003) structural break test to split the data sample into sub-periods delimited by the computed break dates. Afterwards, the author uses the quantile treatment effects over the full sample and then, by including sub-periods dummies to accommodate the selected structural breaks that drive the relationship between inflation and oil price growth.FindingsThe findings include a decreased transmission effect of oil price changes on inflation in recent years; a varied elasticity of inflation to the growth rate of oil prices across the distribution; and, finally, evidence of asymmetry in the relationship between the growth rate of oil prices and inflation, with a higher transmission mechanism for decreasing rather than increasing oil prices.Practical implicationsPolicymakers should remain alert to monitoring potential inflation increases and should take precautionary measures to anchor inflation expectations, because inflation reacts differently to positive and negative oil price shocks. Moreover, authorities should consider the asymmetric reaction of inflation to oil price shocks to adopt an appropriate monetary policy strategy to achieve the price stability target.Originality/valueThe paper used a quantile regression model with structural breaks, which has not yet been used in the literature.

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

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.

scholarly journals Elucidating the Relationship among EUA Spot Price, Brent Oil Price and Three European Stock Indices

2016 ◽  
Vol 4 (2) ◽  
pp. 53-72 ◽  
Author(s):  
John Wei-Shan Hu ◽  
Yi-Chung Hu ◽  
Jenny Chien
Keyword(s):  
Oil Price ◽  
Spot Price ◽  
Stock Indices ◽  
The Relationship

scholarly journals Oil price effect on sectoral stock returns: A conditional covariance and correlation approach for Mexico

2020 ◽  
Vol 16 (1) ◽  
pp. 1-15
Author(s):  
Rodrigo A. Morales Fernández Rafaelly ◽  
Roberto J. Santillán-Salgado
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Spillover Effect ◽  
Oil Prices ◽  
Oil Price ◽  
Price Effect ◽  
Stock Market Performance ◽  
Original Contribution ◽  
Bivariate Garch Model ◽  
The Relationship

This paper analyzes the relationship between the volatility of oil price and selected sectoral stock returns in Mexico (industrials, materials, financials and consumer discretionary) by implementing a Diagonal VECH-type bivariate GARCH model in order to estimate conditional covariances and correlations. The econometric results suggest that there exists a statistically significant relationship between sector indices, as well as between Mexico’s aggregate stock exchange returns, and variations in oil prices. Conditional correlations suggest that during most of the analyzed period, the relationship between oil price fluctuations and sectoral stock returns is positive. The recommendation, supported by these results, is that investors should take into consideration the interaction between the analyzed variables in order to generate more robust risk-hedge strategies. An important limitation for this work is information availability at sector level in the country. The original contribution of this paper lies mainly in the analysis of the influence of oil prices over sectoral indices of the Mexican Stock Exchange. These results provide more support to the current that suggests that a price increase in oil has a direct spillover effect on stock market performance.

Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient

2014 ◽  
Vol 29 (01) ◽  
pp. 1450236 ◽  
Author(s):  
Guangxi Cao ◽  
Yan Han
Keyword(s):  
Correlation Coefficient ◽  
Stock Markets ◽  
Cross Correlation ◽  
Sunshine Duration ◽  
Statistical Test ◽  
Nonlinear Method ◽  
Weather Variables ◽  
Chinese Stock Markets ◽  
The Cross ◽  
The Relationship

Recent studies confirm that weather affects the Chinese stock markets, based on a linear model. This paper revisits this topic using DCCA cross-correlation coefficient (ρ DCCA (n)), which is a nonlinear method, to determine if weather variables (i.e., temperature, humidity, wind and sunshine duration) affect the returns/volatilities of the Shanghai and Shenzhen stock markets. We propose an asymmetric ρ DCCA (n) by improving the traditional ρ DCCA (n) to determine if different cross-correlated properties exist when one time series trending is either positive or negative. Further, we improve a statistical test for the asymmetric ρ DCCA (n). We find that cross-correlation exists between weather variables and the stock markets on certain time scales and that the cross-correlation is asymmetric. We also analyze the cross-correlation at different intervals; that is, the relationship between weather variables and the stock markets at different intervals is not always the same as the relationship on the whole.

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