Dynamics of Exchange Rates and Oil Price: Adaptive Analysis and Forecasting

Orlik, Lubov; Khasanova, I.F.

doi:10.2991/aebmr.k.200509.042

Cited by 1 publication

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“…In [5], using the example of currency quotes, it is shown that, despite similar values, the adaptive coefficient is more sensitive than the modified correlation coefficient. Let's analyse the financial time series using an adaptive correlation coefficient.…”

Section: Analysis Of Financial Time Series Using the Adaptive Correlation Coefficientmentioning

confidence: 99%

“…According to the graphs obtained, a fall in the rates of Bitcoin and gold is predicted. The work [5] shows the analysis of the movement of oil prices and their fall in the long term. The behaviour of all the variables under consideration is similar; accordingly, when analysed, the adaptive correlation coefficient will show a strong positive relationship.…”

Section: Analysis Of Financial Time Series In the R Software Environmentmentioning

confidence: 99%

See 1 more Smart Citation

Gold, Cryptocurrency and Oil Adaptive Analysis and Forecasting

Khasanova¹,

Orlik²,

Zhukova³

2021

Advances in Social Science, Education and Humanities Research

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The work is devoted to identifying and tracking development trends, structural shifts in the economy under the influence of world markets, represented by non-stationary time series of gold, bitcoin and oil prices. The heuristic potential of the concept of the long and medium wave is used for forecasting purposes. The analysis of financial time series using the adaptive correlation coefficient is carried out. The dynamics of the traditional coefficient appears to be a significantly smoothed graph, which prevents sufficient qualitative analysis of the data. The results obtained are analysed to identify wave fluctuations, to determine the phases of growth, prosperity, recession and stagnation in the economy. An overview of the situation on the world markets for gold, bitcoin and oil based on the considered time series is presented. Based on the identified trends in the dynamics of these markets, short-term forecasting was carried out using ARIMA models and neural networks. The statistical calculations R environment is used.

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Section: Analysis Of Financial Time Series Using the Adaptive Correlation Coefficientmentioning

confidence: 99%

Section: Analysis Of Financial Time Series In the R Software Environmentmentioning

confidence: 99%