Adaptive approach to the analysis of correlation properties of financial time series

Zhukova, Galina; D.R, Kagirova

doi:10.1109/mlsd.2018.8551811

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methodological basis of this study is adaptive forecasting [3][4]. In [5] showed that the accuracy of forecasts for time series obtained on the basis of an econometric model with exogenous real data is worse than that for adaptive models. The essence of this approach is to adjust the initial estimate of the base model parameters in time based on the new data obtained at each next step.…”

Section: Methodological Basementioning

confidence: 99%

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

Orlik¹,

Khasanova²

2020

Proceedings of the International Conference on Economics, Management and Technologies 2020 (ICEMT 2020)

View full text Add to dashboard Cite

Multivariate generalizations of the modified and adaptive time series correlation coefficients are obtained using the example of the dependence of currency pairs quotations and Brent crude oil price. The analysis of the movement of exchange rates and oil price in the R software environment. A much more detailed data analysis than the classical theory suggestion is obtained. Based on the identified trends in the dynamics of these markets, short-term forecasting was carried out using ARIMA, TBATS models and neural networks.

show abstract

Section: Methodological Basementioning

confidence: 99%

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

Orlik¹,

Khasanova²

2020

Proceedings of the International Conference on Economics, Management and Technologies 2020 (ICEMT 2020)

View full text Add to dashboard Cite

show abstract

“…As a statistical base, we will take data on the cost of an ounce of gold, bitcoin cryptocurrency and one barrel of Brent oil. The adaptive correlation coefficient allows us to identify the dynamics of the considered time series [6,7].…”

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

confidence: 99%

Gold, Cryptocurrency and Oil Adaptive Analysis and Forecasting

Khasanova¹,

Orlik²,

Zhukova³

2021

Advances in Social Science, Education and Humanities Research

View full text Add to dashboard Cite

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.

show abstract

“…Secondly, the copula, being insensitive to monotonous transformations, allows modeling a nonlinear relationship between components of assets [8]. A non-linear relationship is characteristic of returns on financial assets, as shown in [9][10][11][12][13]. The authors point out the shortcomings of the linear correlation coefficient for assessing the relation of random variables that obviously do not have a normal distribution.…”

Section: Modeling Of Joint Distribution Using Copula Functionsmentioning

confidence: 99%

“…Thus, the insufficient efficiency of managing pension savings funds and a weak risk management system in the pension sector determine the relevance of the study on portfolio management in funded pension plans. Many researchers have studied the risk management of various portfolios, for example, [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. One of the most popular indicators for measuring the total risk of a portfolio of financial assets is Value-at-Risk (VaR).…”

Section: Introductionmentioning

confidence: 99%

Development of Models of Optimal Investment Strategies

Zhukova¹,

Kagirova²

2020

Proceedings of the International Conference on Economics, Management and Technologies 2020 (ICEMT 2020)

View full text Add to dashboard Cite

The aim of the study is to model the stochastic average annual return on the investment portfolio of pension savings for various combinations of asset weights over a 40-year horizon, with an assessment of the risk level for each combination. To estimate the loss of portfolio VaR, an approach that combines copula functions, extreme value theory (EVT) and GARCH models is used. The main results obtained are the following: the average annual return and risk levels of the conservative and expanded portfolios of Vnesheconombank of Russia over the long-term investment period are predicted; a comparative analysis of the impact on the portfolio of legislative restrictions on the assets of the portfolio of pension savings and restrictions on the portfolio VaR; optimal strategies have been selected to achieve a level of pension provision that meets international standards, with a minimum risk. The results of the study and the optimization procedure proposed in the work can be applied in the field of asset management and risk management.

show abstract

Adaptive approach to the analysis of correlation properties of financial time series

Cited by 4 publications

References 0 publications

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

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

Gold, Cryptocurrency and Oil Adaptive Analysis and Forecasting

Development of Models of Optimal Investment Strategies

Contact Info

Product

Resources

About