Geopolitical risk and the Saudi stock market: Evidence from a new wavelet packet multiresolution cross-causality

Recently, due to OPEC Plus cutting oil production, geopolitical tensions have escalated between the United States and Saudi Arabia. We explore the relationship between geopolitical risk and the Saudi stock market, developing a deep cross-causality approach based on wavelet methodology. Our sample includes Brexit, COVID-19, and the Russian–Ukrainian war. We identify causal patterns especially during times of crisis, evidencing one-way causality of geopolitical factors impacting the Saudi market. Scholars and policymakers will be interested in the sensitivity of the Saudi market to geopolitical risk. © 2023 Elsevier Inc.

Weibull-like bivariate probability density function and associated estimation algorithms

We propose to introduce a new class of bivariate probability distributions, which we believe is of great interest to statisticians and data scientists. However different from the conventional Weibull it might be, the density function posited herein allows to generalize its properties in two dimensions (2D). This new function, essentially, has structure characteristics and properties different from those of the various bivariate Weibull-type functions found in the literature. The main features, such as the marginal distributions, moments, characteristic functions of this bivariate density are defined. Two related maximum likelihood estimation algorithms are also explicated, tested, and compared. Numerical simulations show the practicality of these algorithms as well as the interest of the new density in several areas of data analysis and extreme values modeling.

VMD-based Multiscaled LSTM-ARIMA to Forecast Post-COVID-19 US Air Traffic

Accurate traffic flow forecasting is an essential component of the Intelligent Transportation System (ITS). However, existing traffic forecasting methods using deep learning pay little attention to the pandemic's repercussions. This paper proposes a multiscaled deep learning framework called VMD-LSTM-ARIMA, which couples the variational mode decomposition (VMD) algorithm, long short-term memory (LSTM) neural network, and autoregressive integrated moving average (ARIMA) to accurately predict traffic flow time series. Just like any hybrid model, the proposal takes advantages of each one of these approaches, which enhances the performance of the overall forecasting model. Experiments were conducted on a US public traffic datasets, and the results showed that VMD-LSTM-ARIMA effectively increased the prediction accuracy. © 2022 IEEE.