Influence Oil Price towards Economic Indicators in Russia

Nyangarika, Anthony; Tang, Bao-Jun

doi:10.1088/1755-1315/192/1/012066

Cited by 41 publications

(38 citation statements)

References 9 publications

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“…Considering how the financial market correlates with crude oil, based on a VAR model, Nyangarika et al 32 find that crude oil prices affect the exchange rate of the Russian Federation directly; rising oil prices cause the lower the US dollar rate to the Russian ruble. Arouri et al 16 use VAR-GARCH models to examine the volatility spillovers between oil and stock markets in Europe.…”

Section: Literature Reviewmentioning

confidence: 99%

Spillover effects between energies, gold, and stock: the United States versus China

Takiguchi

Nakajima

et al. 2020

Energy & Environment

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This study investigates the time–frequency dynamics of return and volatility spillovers between the stock market and three commodity markets: natural gas, crude oil, and gold via a comparative analysis between the United States and China is conducted with the help of new empirical methods. Our findings are as follows. First, in terms of time, return spillovers between crude oil and the stock market are strongest in two of the three commodity markets. Crude oil emits a net negative return spillover to the US stock market, and a net positive return spillover to the Chinese stock market. By contrast, the strongest volatility spillover effect is transmitted to the stock markets of both countries through gold. However, gold has a net positive volatility spillover effect on the US stock market and a net negative effect on the Chinese stock market. In the frequency domain, most of the return spillover is produced in the short term, and most of the volatility spillover occurs in the long term. In addition, the moving-window method reveals the dynamic nature of the spillover effect. Some extreme events can have a dramatic effect on the spillover index. Conversely, the spillover effect differs significantly between the two countries and is characterized by time variation and frequency dependence.

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Section: Literature Reviewmentioning

confidence: 99%

Spillover effects between energies, gold, and stock: the United States versus China

Takiguchi

Nakajima

et al. 2020

Energy & Environment

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“…Interestingly, the study observes a medium negative correlation between TSLA and USO in contrast to the obsolete spillover effect between USO and TAN. This data indicates that although the two researched ETFs do not have any correlation with each other, the Tesla stock serves as a "middleman" or "bridge" between a possible negative spillover effect of these two assets (Nyangarika et al, 2019b). These data findings can be the subject of further researches in order to learn about the specifics of the link of these three assets.…”

Section: Resultsmentioning

confidence: 72%

Renewable Energy Transition: Evidence From Spillover Effects in Exchange-Traded Funds

Lisin

Senjyu

2021

IJEEP

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The article examines the spillover effect of the transition to renewable energy on different financial markets: specifically, the stock market. The research paper analyzes the prices and dynamics of oil-related assets and renewable funds, as well as reviews literature on the topic in order to evaluate the most important factors of development in these industries. The article utilizes methods of correlation analysis, analysis of variance, regression analysis and standards of deviation and error with the intention to test null hypotheses from previous research. The study uses empirical data spanning the majority of the last decade, in which a decline in the oil market can be observed. This is juxtaposed by the rapid and intense development of renewable energy -solar energy in particular. As concluded in the article, there is a strong spillover effect of this growth onto other markets, emphasizing the investments flowing into renewable energy. Moreover, the ambitious prospects of renewable energy development and it being a strong competitor to fossil fuels and, specifically, oil is drawn as a result of the conducted research.

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“…Prior to this study, there was a similar trend in the behavior of open innovation indicators in emerging countries. Some authors have argued for the existence of a relationship between the indicators under consideration, regardless of the time interval [66,67].…”

Section: IXmentioning

confidence: 99%

Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators

Baboshkin

Yegina

Zemskova

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

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This article aims to highlight various methods and approaches to grouping countries, according to the behavior of their open innovation indicators. GDP, inflation and unemployment are the most important indicators of the economic and social policies of states, allowing them to be evaluated and models built. To find the relationships between open innovation indicators the paper uses marginal analysis and feature reduction, as well as machine learning methods (shift to the mean, agglomerative clustering and random forest methods). The results showed that, after isolating all groups, the importance of the signs was established and the patterns of behavior of indicators for each group were compared and open innovation dynamics was analyzed. The conclusions showed that it is obvious that increasing the number of variables in the model and using more extensive indicators can greatly increase the accuracy, in contrast to the generally accepted simple classifications. This approach makes it possible to more accurately find the connections between sectors of the economy or between state economies in general. An accompanying result of the study was the clarification of the equality of open innovation indicators for the analysis of their interrelationships between countries.

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Influence Oil Price towards Economic Indicators in Russia

Cited by 41 publications

References 9 publications

Spillover effects between energies, gold, and stock: the United States versus China

Spillover effects between energies, gold, and stock: the United States versus China

Renewable Energy Transition: Evidence From Spillover Effects in Exchange-Traded Funds

Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators

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