scholarly journals Energy Security in Danger? A Comparative Analysis of Oil and Copper Supply

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 560
Author(s):  
Maciej Mróz
Keyword(s):  
Comparative Analysis ◽  
Crude Oil ◽  
Energy Security ◽  
New Technologies ◽  
Methodological Approach ◽  
Price Stability ◽  
Garch Models ◽  
Security Of Supply ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

This study aims to examine energy security in terms of crude oil and copper supply. While oil remains the leading energy commodity globally, copper is crucial for many new technologies, foremost for RES. Therefore, both oil and copper are extremely important for current and future energy security. This article contains a bivariate methodological approach to a comparative analysis of oil and copper supply: determining supply security with an Index of security of supply, and examines price stability with generalized autoregressive conditional heteroscedasticity (GARCH) models. This research provides evidence that there are many differences but also significant similarities between these two completely different commodities in terms of both supply security and price stability. Facing the future for RES, significant demand may cause a threat to energy security on a previously unknown scale. Therefore this instability, both supply- and price-related, appears to be the main threat to future energy security.

scholarly journals A Two-Regime Markov-Switching GARCH Active Trading Algorithm for Coffee, Cocoa, and Sugar Futures

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 1001 ◽  
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Markov Switching ◽  
Garch Models ◽  
Arch Model ◽  
Conditional Heteroscedasticity ◽  
Arch Models ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Potential Use ◽  
The U.S

In the present paper we tested the use of Markov-switching Generalized AutoRegressive Conditional Heteroscedasticity (MS-GARCH) models and their not generalized (MS-ARCH) version. This, for active trading decisions in the coffee, cocoa, and sugar future markets. With weekly data from 7 January 2000 to 3 April 2020, we simulated the performance that a futures’ trader would have had, had she used the next trading algorithm: To invest in the security if the probability of being in a distress regime is less or equal to 50% or to invest in the U.S. three-month Treasury bill otherwise. Our results suggest that the use of t-student Markov Switching Component ARCH Model (MS-ARCH) models is appropriate for active trading in the cocoa futures and the Gaussian MS-GARCH is appropriate for sugar. For the specific case of the coffee market, we did not find evidence in favor of the use of MS-GARCH models. This is so by the fact that the trading algorithm led to inaccurate trading signs. Our results are of potential use for futures’ position traders or portfolio managers who want a quantitative trading algorithm for active trading in these commodity futures.

scholarly journals ARCH and GARCH Models: Quasi-Likelihood and Asymptotic Quasi-Likelihood Approaches

2020 ◽  
Author(s):  
Raed Alzghool
Keyword(s):  
Exchange Rate ◽  
Kernel Estimation ◽  
Estimation Procedure ◽  
Conditional Variance ◽  
Estimation Methods ◽  
Garch Models ◽  
Unknown Parameters ◽  
Conditional Heteroscedasticity ◽  
Garch Processes ◽  
Autoregressive Conditional Heteroscedasticity

This chapter considers estimation of autoregressive conditional heteroscedasticity (ARCH) and the generalized autoregressive conditional heteroscedasticity (GARCH) models using quasi-likelihood (QL) and asymptotic quasi-likelihood (AQL) approaches. The QL and AQL estimation methods for the estimation of unknown parameters in ARCH and GARCH models are developed. Distribution assumptions are not required of ARCH and GARCH processes by QL method. Nevertheless, the QL technique assumes knowing the first two moments of the process. However, the AQL estimation procedure is suggested when the conditional variance of process is unknown. The AQL estimation substitutes the variance and covariance by kernel estimation in QL. Reports of simulation outcomes, numerical cases, and applications of the methods to daily exchange rate series and weekly prices’ changes of crude oil are presented.

Dylematy infrastrukturalnego bezpieczeństwa energetycznego państw tranzytowych ropy naftowej – na przykładzie Polski, Białorusi i Ukrainy

2020 ◽  
Vol 18 (4) ◽  
pp. 75-96
Author(s):  
Maciej Mróz
Keyword(s):  
Comparative Analysis ◽  
Crude Oil ◽  
Energy Security ◽  
Important Determinant ◽  
International Strategy ◽  
Literature Research ◽  
The Subject ◽  
Political Actions

Energy security is a sine qua non condition for functioning and development of the economy of individual countries. The presented analysis concerns the issues of energy security of Poland, Belarus and Ukraine from the perspective of importers and transit countries of crude oil in the infrastructural dimension. The aim of the article is to answer the question: which direction to develop the strategic transmission infrastructure in the transit countries in order to ensure energy security? Literature research on the subject and a comparative analysis shows that from the perspective of these countries, one of the most important determinant of their energy security is the strategic transmission infrastructure that allows for the diversification of the supplier’s sources and directions. However, the implementation of this strategy requires coordinated and consistent political actions as part of an international strategy.

Comparison of linear and non-linear GARCH models for forecasting volatility of select emerging countries

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudhi Sharma ◽  
Vaibhav Aggarwal ◽  
Miklesh Prasad Yadav
Keyword(s):  
Global Financial Crisis ◽  
Emerging Countries ◽  
Expected Returns ◽  
Garch Models ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Volatility Models ◽  
Non Linear ◽  
Forecasting Volatility ◽  
Autoregressive Conditional Heteroscedasticity

PurposeSeveral empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico.Design/methodology/approachThe volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1, 1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001–December 2008 and January 2009–December 2019, respectively.FindingsThe findings indicate that GARCH (1, 1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities.Practical implicationsThis study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility.Originality/valueEarlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors’ searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature.

scholarly journals Modeling the Behavior of Inflation Rate in Albania Using Time Series

2017 ◽  
Vol 13 (3) ◽  
pp. 7257-7263
Author(s):  
Rozana Liko
Keyword(s):  
Time Series ◽  
Serial Correlation ◽  
Inflation Rate ◽  
Garch Models ◽  
Conditional Heteroscedasticity ◽  
One Year ◽  
Autoregressive Conditional Heteroscedasticity

In this paper, time series theory is used to modelling monthly inflation data in Albania during the period from January 2000 to December 2016. The autoregressive conditional heteroscedastic (ARCH) and their extensions, generalized autoregressive conditional heteroscedasticity (GARCH)) models are used to better fit the data. The study reveals that the inflation series is stationary, non-normality and has serial correlation.   Based on minimum AIC and SIC values the best model turn to be GARCH (1, 1) model with mean equation ARMA (2, 1)x(2, 0)12. Based on the selected model one year of inflation is forecasted (from January 2016 to December 2016).

scholarly journals Volatility of cryptocurrencies

Notitia ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 13-23
Author(s):  
Branimir Cvitko Cicvarić
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Information Criterion ◽  
Garch Models ◽  
Arch Model ◽  
Model Estimate ◽  
Conditional Heteroscedasticity ◽  
Financial Time ◽  
Egarch Model ◽  
Autoregressive Conditional Heteroscedasticity

Many models have been developed to model, estimate and forecast financial time series volatility, amongst which are the most popular autoregressive conditional heteroscedasticity (ARCH) model introduced by Engle (1982) and generalized autoregressive conditional heteroscedasticity (GARCH) model introduced by Bollerslev (1986). The aim of this paper is to determine which type of ARCH/GARCH models can fit the best following cryptocurrencies: Ethereum, Neo, Ripple, Litecoin, Dash, Zcash and Dogecoin. It is found that the EGARCH model is the best fitted model for Ethereum, Zcash and Neo, PARCH model is the best fitted model for Ripple, while for Litecoin, Dash and Dogecoin it depends on the selected distribution and information criterion.

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

2021 ◽  
pp. 002072092098849
Author(s):  
Xinzhe Yin ◽  
Jinghua Li
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Financial Market ◽  
Prediction Method ◽  
Good Effect ◽  
Trend Prediction ◽  
Bp Network ◽  
Conditional Heteroscedasticity ◽  
Bp Model ◽  
Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

Sign in / Sign up

Export Citation Format

Share Document