scholarly journals Oil Prices and Stock Markets in Europe: Detection of Extreme Risk Spillover

2019 ◽  
Vol 10 (1) ◽  
pp. 40-53 ◽  
Author(s):  
Blanka Łęt
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
Stock Markets ◽  
Oil Prices ◽  
Testing Procedure ◽  
Test Results ◽  
Oil Market ◽  
Extreme Risk ◽  
Negative News ◽  
European Stock Markets

The goal of this paper is to check existence of Granger causality in risk between eleven European stock markets and crude oil market. We analyze bidirectional instantaneous and delayed Granger causality in tails test results, i.e. whether occurrence of the extreme returns on the crude oil market precede similar events on the main European stock markets and vice versa. Using Brent futures prices and main stock indices in Europe (Belgium, France, Germany, Greece, Italy, Netherlands, Norway, Poland, Spain, Sweden and United Kingdom), we apply testing procedure developed by Candelon and Tokpavi (2016). The main conclusion is that in the vast majority of cases instantaneous causality in tails was symmetrical. We also found that more long-lived reaction appeared as a result to the negative news from the oil market and from the stock markets.

scholarly journals Modeling Nonlinear Granger Causality and Co-Integration Between Gold Price Returns and Crude Oil Price Returns

2018 ◽  
Vol 14 (2) ◽  
pp. 105-116
Author(s):  
Nawaz Ahmad ◽  
Keyword(s):  
Crude Oil ◽  
Granger Causality ◽  
Oil Prices ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Long Run ◽  
Gold Market ◽  
Oil Markets ◽  
Causality Tests ◽  
Multiplicative Effects

To model the nonlinear analysis of commodities, Gold market and crude oil market have importance to test their lead and lag price mechanism between the two. For this purpose, the log transformation has been done to calculate easier multiplicative effects. However, to record the dynamic effects of long run cointegreation model applied and tested to find the significance of the problem statement issues. Furthermore, granger causality approach also uses to examine the fundamental linkages between Gold Prices and Crude Oil prices. Meanwhile, the study of Gold markets and oil markets gained popularity among development economists during in last some decades. And try to find out stochastic relationship between the two nonlinear markets. The academic practitioners paved their efforts to run casual time series models in order to find out the robust results which help the economists and financial experts to drive the industry indicator in positive way. This study confirmed that there is cointegration between the two important indicators of large market commodities i.e Gold and crude oil and also casual interactions. Pairwise Granger Causality Tests concluded that Gold Prices return has Granger Cause on Oil Prices return in the long run and if the βeta change in the prices of gold may affect on the prices of crude oil in the long run.

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 Oil Price Forecasting Using a Time-Varying Approach

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1403
Author(s):  
Lu-Tao Zhao ◽  
Shun-Gang Wang ◽  
Zhi-Gang Zhang
Keyword(s):  
Crude Oil ◽  
Time Window ◽  
Oil Prices ◽  
Oil Price ◽  
Price Forecasting ◽  
Time Varying ◽  
Crude Oil Price ◽  
Trend Prediction ◽  
Oil Market ◽  
Polynomial Decomposition

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

Linking crude oil prices and Middle East stock markets

2019 ◽  
Vol 43 (2) ◽  
pp. 136-167 ◽  
Author(s):  
Mousa Tawfeeq ◽  
Alan R. Collins ◽  
Levan Elbakidze ◽  
Gulnara Zaynutdinova
Keyword(s):  
Middle East ◽  
Crude Oil ◽  
Stock Markets ◽  
Oil Prices ◽  
Crude Oil Prices

scholarly journals Heterogeneous Causal Relationships between Spot and Futures Oil Prices: Evidence from Quantile Causality Analysis

2019 ◽  
Vol 11 (5) ◽  
pp. 1359
Author(s):  
Xianfang Su ◽  
Huiming Zhu ◽  
Xinxia Yang
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Causal Relationships ◽  
Causality Analysis ◽  
Futures Prices ◽  
Spot Prices ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Heterogeneous Effects ◽  
Quantile Causality

The causal relationships between spot and futures crude oil prices have attracted the attention of many researchers in the past several decades. Most of the studies, however, do not distinguish among the various oil market situations in analyses of linear and nonlinear causalities. In light of the fact that a booming or depressing oil market produces heterogeneous investment behaviors, this study applied a quantile causality framework to capture different causalities across various quantile levels and found that the causal relationships between crude oil spot and futures prices significantly derive from tail quantile intervals and appear as heterogeneous effects. Before the Iraq War, crude oil spot and futures prices were mutually Granger-caused at lower quantile levels, and only futures prices led spot prices at upper quantile levels. Since the war, a clear bidirectional causality has existed at the upper quantile levels, but only in lower quantile levels have futures prices led spot prices. These results provide useful information to investors using crude spot or futures prices to hedge or manage downside or upside risks in their portfolios.

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.

Russia's Oil Prices: Moving toward the Market

1995 ◽  
Vol 13 (6) ◽  
pp. 553-582
Author(s):  
Eugene M. Khartukov
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Oil Industry ◽  
World Market ◽  
Market Pricing ◽  
Oil Market ◽  
Crude Oil Prices ◽  
Domestic Prices ◽  
Feasible Alternative ◽  
Oil And Oil Products

The painful perestroika of the ex-Soviet oil industry has been accompanied by an accelerated transition from the previous all-embracing and inflexible price control to actually decontrolled market pricing for both crude oil and oil products. The freed and soaring oil prices quickly hit equilibrium levels, led to sizeable contraction of inland oil demand, and generated two interrelated crises of nonpayment and overproduction. Rising transportation costs resulted in spontaneous “regionalization” of the national oil market and made oil product imports a feasible alternative to long-haul domestic supplies. While retail product prices became comparable with those in some Western countries, the backwardness of the country's refining industry and the resultant low gross product worth still are keeping domestic prices of crude oil substantially below world market parities. Though the rapid “globalization” of internal crude oil prices is on the Russian government's agenda, an immediate rise to world price levels is neither desirable nor actually possible.

Oil and risk premia in equity markets

2020 ◽  
Vol 37 (4) ◽  
pp. 697-723
Author(s):  
Satish Kumar ◽  
Riza Demirer ◽  
Aviral Kumar Tiwari
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Systematic Risk ◽  
Oil Prices ◽  
Risk Premia ◽  
Market Size ◽  
Predictive Information ◽  
Content Type ◽  
Oil Market ◽  
Factor Returns

Purpose This study aims to explore the oil–stock market nexus from a novel angle by examining the predictive role of oil prices over the excess returns associated with the market, size, book-to-market and momentum factors via bivariate cross-quantilograms. Design/methodology/approach This study makes use of the bivariate cross-quantilogram methodology recently developed by Han et al. (2016) to analyze the predictability patterns across the oil and stock markets by focusing on various quantiles that formally distinguish between normal, bull and bear as well as extreme market states. Findings The study analysis of systematic risk premia across the four regions shows that crude oil returns indeed capture predictive information regarding excess factor returns in stock markets, particularly those associated with market, size and momentum factors. However, the predictive power of oil return over excess factor returns is asymmetric and primarily concentrated on extreme quantiles, suggesting that large fluctuations in oil prices capture markedly different predictive information over stock market risk premia during up and down states of the oil market. Practical implications The findings have significant implications for the profitability of factor- or style-based active portfolio strategies and suggest that the predictive information contained in oil market fluctuations could be used to enhance returns via conditional strategies based on these predictability patterns. Originality/value This study contributes to the vast literature on the oil–stock market nexus from a novel perspective by exploring the effect of oil price fluctuations on the risk premia associated with the systematic risk factors including market, size, value and momentum.

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