Quantile dependencies and connectedness between the gold and cryptocurrency markets: Effects of the COVID-19 crisis

This paper examines the quantile dependence, connectedness, and return spillovers between gold and the price returns of leading cryptocurrencies, using quantile cross-spectral, the return spillovers based the quantile VAR, and quantile connectedness approaches. The results show that the dependencies within cryptocurrencies are highly symmetric and sensitive to different quantile arrangements. Under normal market conditions, we find a high positive dependence within cryptocurrencies and a low positive dependence between cryptocurrencies and gold. The dependence is higher at long term than intermediate- and short- terms before the pandemic during bearish market conditions. In contrast, the degree of dependence decreases at the intermediate- and long-terms during COVID-19 period than before. Moreover, the magnitude of return spillovers is higher at lower quantile (bearish market) than upper quantile (bullish market). Gold serves as a safe haven and diversifier asset for cryptocurrencies during COVID-19 outbreak at both intermediate and long terms. © 2023 Elsevier B.V.

Multi-class LDA classifier and CNN feature extraction for student performance analysis during Covid-19 pandemic

In our modern world, education is essential for developing high moral values and excellence in individuals. But the spread of Covid-19 widely affects the student's education, the majority of students have continued their education via online learning platforms. The academic performance of students has been sluggish across the globe during this pandemic. This problem is solved using a multiclass Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) model which predicts the student learning rate and behavior. This research aims to classify the students' performance into low, medium, and high grades in order to assist tutors in predicting the low-ranking students. The student data log is collected from the Kaggle student performance analysis dataset and pre-processed to remove the noise and non-redundance data. By analyzing the pre-processed data, the CNN extracts feature that are based on student interest and subjective pattern sequences. Then extracted features are filtered by the Minimum Redundancy Maximum Relevance(mRMR) method. mRMR selects the best features and dilutes the least one which handles each feature separately. The feature weights are measured by Stochastic Gradient Descent (SGD) and updated for better feature learning by CNN. At the last stage, the Multi-class LDA classifier evaluates the result into categorized classes. Based on the prediction, the tutors can easily find the low ranks of students who need a high preference for improving their academic performance. Experiments showed that the proposed model achieves greater accuracy (96.5%), precision (094), recall (092), F-score (095), and requires less computation time than existing methods.

The impacts of COVID-19 crisis on spillovers between the oil and stock markets: Evidence from the largest oil importers and exporters

This study examines the multiscale spillovers and nonlinear causalities between the crude oil futures market and the stock markets of the United States (US), Canada, China, Russia, and Venezuela before and during the COVID-19 pandemic. Using the wavelet coherency method, we find strong co-movement between the oil futures market and these five stock markets, particularly from March 2020 to May 2020 (initial period of the COVID-19 outbreak) at high frequency. Furthermore, we find positive co-movements at low frequency during the overall COVID-19 period. This finding suggests that the bearish trend of stock markets is associated with a downward movement in oil prices. Using the wavelet-based Granger causality approach, we find that the oil and stock indices have less co-movement on a smaller scale but greater movement on a larger scale across all periods. As an exception, the Russian market is significantly influenced by oil prices, even on a small scale, before the COVID-19 period, but not after the beginning of the pandemic. We also find effects in the opposite direction—the Canadian and U.S. markets influence oil prices on a small scale during the COVID-19 period, an effect that is not visible for the U.S. market in the pre-COVID-19 sample. The results also show a significant bidirectional causality from oil to stock markets and vice versa during Russian-Saudi oil price war at high scale. Furthermore, we find that investors should hold more oil futures than stock shares in their portfolios for all periods. This evidence confirms that oil instruments are important for hedging during normal periods and act as safe-haven assets during crisis periods. We observe that the U.S. and Canadian stock markets were more affected by oil price shocks than were other countries. © 2021 Economic Society of Australia, Queensland