The Influence of the International Price of Oil on the Value of the EUR/USD Exchange Rate

Vochоzka, Marek; Budějovice, Business in České; Rowland, Zuzana; Šuleř, Petr; Maroušek, Josef

doi:10.7441/joc.2020.02.10

Cited by 87 publications

(31 citation statements)

References 19 publications

(11 reference statements)

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“…The coefficient of the degree of freedom hadthe satisfactory value of 7.83, which concludes that this examination has efficaciously captured the fat-tailed distribution properties of the oil market. The study of [45] investigated the effects of variations in the oil prices on the EUR/USD exchange price by using the approach of neural networks. The study argued that EUR/USD exchange price is significantly affiliated with the oil price in the international market.…”

Section: Variance Equation Resultsmentioning

confidence: 99%

Examining the Asymmetric Impact of COVID-19 Pandemic and Global Financial Crisis on Dow Jones and Oil Price Shock

et al. 2021

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COVID-19 has significantly affected the financial and commodity markets. The purpose of this investigation is to understand the impact of the COVID-19 crisis on Dow Jones and West Texas Intermediate (WTI) oil returns in relation to other crises using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model. The results indicate that COVID-19 and the accompanying lockdown have adversely impacted both yields and that the impact on oil prices is more significant than on the Dow Jones index. The variance and squared residuals of oil prices and the Dow Jones reached their highest historical levels during the COVID-19 outbreak, even higher than during the global financial crisis, and especially the VaR of both markets reached their historical peak points during the COVID-19 era. The variance of WTI during COVID-19 is higher than that of DJI, as was also the case during the financial crisis. These findings confirm that COVID-19 has negatively impacted investors’ ability to determine optimal portfolios and thus the sustainability of financial and energy markets more than the global financial crisis of 2007–2009. We, therefore, suggest that policy changes are needed to maintain financial sustainability and help investors deal with future financial and other crises.

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Section: Variance Equation Resultsmentioning

confidence: 99%

Examining the Asymmetric Impact of COVID-19 Pandemic and Global Financial Crisis on Dow Jones and Oil Price Shock

et al. 2021

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“…Vochozka et al [33], used artificial neural networks to create a methodology for the prediction of a company failure. The study of [34] investigated the influence of the international price of oil on the value of the EUR/USD exchange rate by using the approach of neural networks. In another study Vochozka et al [35], used (Long Short-Term Memory) for predicting oil prices.…”

Section: Related Workmentioning

confidence: 99%

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

et al. 2021

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Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

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“…Vochozka, Horak and Suler [17] also talk about a relevant output of the neural network prediction model in economic sectors. Vochozka et al [18] classify positively a neural network prediction model for its ability to give excellent statistical performance compared to conventional models. Specifically, self-learning skills, independence, adaptation, and fault tolerance make neural networks the first choice in predicting complicated economic phenomena.…”

Section: Introduction and Literature Researchmentioning

confidence: 99%

The Dependence of the CZK Exchange Rate on the Development of the GDP in the Czech Republic in the Conditions of an Expected Crisis

Machová

Krulický

2021

SHS Web of Conf.

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Research background: In the past, some studies proved that the development of a currency exchange rate predicts the development of the whole national economy. The monetary market overtakes the development of the actual economy for a few months. Does this apply also in the case of the Czech koruna, in the era of the global Coronavirus pandemics and in the world affected by the pandemics? Purpose of the article: The main objective is to analyze a dependence of the Czech koruna (CZK) to Euro (EUR) exchange rate development on gross domestic product of the Czech Republic in the conditions of an expected crisis. Methods: The data used of the analysis are represented by the information about the CZK and EUR exchange rate from the beginning of 1999 to the 15th June 2020 and by the quarterly development of the Czech GDP. To measure the dependence and predict the development of the GDP based on the CZK exchange rate development, the method of AI is used, namely the regression analysis using the artificial neural networks. Findings & Value added: The effect of EUR/CZK on GDP can be quantified reaching around 31%. It is assumed that the GDP will fall significantly in 2020 with a certain growth only being possibly expected in 2021 (even more significantly in the second quarter of 2021). Due to the GDP development, the development of the EUR/CZK could then be forecasted as well.

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The Influence of the International Price of Oil on the Value of the EUR/USD Exchange Rate

Cited by 87 publications

References 19 publications

Examining the Asymmetric Impact of COVID-19 Pandemic and Global Financial Crisis on Dow Jones and Oil Price Shock

Examining the Asymmetric Impact of COVID-19 Pandemic and Global Financial Crisis on Dow Jones and Oil Price Shock

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

The Dependence of the CZK Exchange Rate on the Development of the GDP in the Czech Republic in the Conditions of an Expected Crisis

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