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

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.

When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

We present a textual analysis that explains how Elon Musk’s sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world’s wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla’s CEO. However, we did not find evidence to support linkage between Musk’s sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.

An Empirical Analysis on the Volatility of Return of CSI 300 Index

In order to better observe the trend of the stock market, this paper selects the daily closing price data of CSI 300 index from April 12, 2016 to September 30, 2021, and makes an empirical analysis on the logarithmic return of CSI 300 index. It is found that: (1) the return series of the CSI 300 index shows the statistical characteristics of peak, thick tail, bias, asymmetry and persistence. The ARMA (2,3) model can effectively fit the yield series and predict the future trend to a certain extent. (2) The residuals of ARMA model show obvious cluster effect and ARCH effect (conditional heteroscedasticity). GARCH (1,1) model can better fit the conditional heteroscedasticity, so as to eliminate the ARCH effect. (3) By constructing GARCH (1,1) model, it is found that the sum of ARCH term coefficient and GARCH term coefficient is very close to 1, indicating that GARCH process is wide and stable, the impact on conditional variance is lasting, and the market risk is large, that is, the impact plays an important role in all future forecasts.

POSSIBILITY OF RUSSIAN BONDSPOTFOLIO DIVERSIFICATION THROUGH THE USE OF ISLAMIC BONDS DURING THE COVID-19 PANDEMIC

Диверсификация - один из важнейших элементов в инвестиционной деятельности. Инвесторы пытаются найти баланс при формировании портфеля и его реструктуризации, стремясь одновременно максимизировать доходность и минимизировать риски. Целью данной работы является оценка возможности диверсификации портфеля облигаций российского рынка с помощью включения альтернативной традиционным облигациям формы - сукук в условиях пандемии COVID-19. Представленный в статье анализ такой возможности составляет определенный элемент новизны. В качестве наиболее подходящей модели для корреляционного анализ выбрана «DCC-MGARCH» модель (динамическая модель авторегрессионной условной гетероскедастичности). Результаты исследования показывают, что инвесторы, предпочитающие долговые суверенные ценные бумаги России и корпоративные облигации российских компаний, имеют возможность диверсифицировать портфель путем включения исламских облигаций. Данный вывод объясняется наличием отрицательной корреляционной связи между индексом сукук и индексами российских облигаций, как корпоративных, так и суверенных. Diversification is one of key elements in investment management. Investors strive to find a balance in the formation of a portfolio and its restructuring, simultaneously maximizing profitability and minimizing risks. The purpose of this work is to assess the possibility of diversification of the Russian bonds portfolioby including an alternative to traditional bonds-sukuk. The DCC-MGARCH model (Dynamic Conditional Correlation Multivariate General Autoregressive Conditional Heteroscedasticity Model) was chosen as the most suitable model for correlation analysis. The results of the study show that investors who prefer Russian sovereign debt securities or corporate bonds of Russian companies couldeffectively diversify their portfolio by including Islamic bonds during the COVID-19 pandemic. This conclusion is explained by the presence of a negative correlation between the Dow Jones Sukuk Index as a proxy for sukuk market and the indices of Russian bonds, both corporate and sovereign.

Penerapan Model ARIMA-ARCH untuk Meramalkan Harga Saham PT. Indofood Sukses Makmur Tbk

Abstract– The stock price is the value of the stock in the market that fluctuates from time to time. Time series data in the financial sector generally have quite high volatility which can cause heteroscedasticity problems. This study aims to model and to predict the stock price of PT Indofood Sukses Makmur Tbk using the ARIMA-ARCH model. The data used is daily stock prices from 2nd June 2020 to 15th February 2021 as training data, while from 16th February 2021 to 1st March 2021 as testing data. ARIMA-ARCH model is a model that combines Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH), which can be used to overcome the residues of the ARIMA model which are indicated to have heteroscedasticity problems. The result showed that the model that could be used was ARIMA(1,1,2)-ARCH(1). This model can provide good forecasting result with a relatively small MAPE value of 0.515785%. Abstrak– Harga saham adalah nilai saham di pasar yang berfluktuasi dari waktu ke waktu. Data runtun waktu di sektor keuangan umumnya memiliki volatilitas cukup tinggi yang dapat menyebabkan masalah heteroskedastisitas. Penelitian ini bertujuan untuk memodelkan dan meramalkan harga saham PT Indofood Sukses Makmur Tbk menggunakan model ARIMA-ARCH. Data yang digunakan adalah harga saham harian dari 2 Juni 2020 hingga 15 Februari 2021 sebagai data training, sedangkan dari 16 Februari 2021 hingga 1 Maret 2021 sebagai data testing. Model ARIMA-ARCH merupakan suatu model yang menggabungkan Autoregressive Integrated Moving Average (ARIMA) dan Autoregressive Conditional Heteroscedasticity (ARCH), yang dapat digunakan untuk mengatasi residu dari model ARIMA yang terindikasi memiliki masalah heteroskedastisitas. Hasil penelitian menunjukkan bahwa model yang dapat digunakan adalah ARIMA(1,1,2)-ARCH(1). Model tersebut mampu memberikan hasil peramalan yang baik dengan perolehan nilai MAPE yang relatif kecil yaitu 0,515785%.