No abstract

This paper examines the dynamic relationship between crude oil prices and the U.S. exchange rate within the structural break detection context. Based on monthly data from January 1996 to April 2019, this paper identifies structural breaks in movements of oil price and examines the dynamic relationship between crude oil prices and the U.S. exchange rate movement by introducing the economic policy uncertainty and using the TVP-VAR (Time-Varying Parameter-Vector Auto Regression ) model. Empirical results indicate that shocks to crude oil prices have immediate and short-term impacts on movements in the exchange rate which are emphasized during the confidence intervals of structural breaks. Oil price shocks and economic policy uncertainty are interrelated and influence movements in the U.S. exchange rate. Since the U.S. dollar is the main currency of the international oil market and the U.S. has become a major exporter of crude oil, the transmission of price shocks to the U.S. exchange rate becomes complicated. In most cases, the relationship between oil prices and the U.S. exchange rate movements is negative.

In response to the dilemma between economic development and environmental protection, green finance is an effective tool for environmental regulation. Based on the stochastic frontier analysis method to measure the energy efficiency of China’s provinces from 2001 to 2017, the promotion effect of green finance on energy efficiency and the intermediary effect of green technology innovation are tested and analyzed in our study. The results show that green finance can significantly improve energy efficiency. Specifically, green finance makes stronger effect on energy efficiency in provinces with rich resource endowments, high levels of economic development, and high degree of marketization. Green finance can improve energy efficiency through the development of new energy technologies and disruptive green innovation, which provides important supports for formulating policies to optimize energy structure and improve energy efficiency.

No abstract

The uncertainty in the evolution of crude oil price fluctuation has a significant impact on economic stability. Based on the decomposition of crude oil price fluctuation by the state-space model, this paper studies the fluctuation trend of crude oil prices and its causes. The nonlinearity autoregressive distribute lag approach (NARDL) model is used to capture the influence mechanism characteristics of crude oil prices at different positions and different fluctuation trends. An event study model with dummy variables is constructed to compare the effects of different types of events on crude oil price fluctuations. The empirical results indicate that the fluctuation of crude oil prices tends to strengthen on the whole, and there is a remarkable correlation between this trend and the influencing mechanism of crude oil price, namely, the fluctuation source structure. The influence mechanism of crude oil price fluctuation is asymmetric when the crude oil price is at different positions and under different trends. There is a strong correlation between event shocks and event types in the evolution of crude oil price fluctuation.

No abstract

Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The original graph data are encoded into spike trains based on the incorporation of graph convolution. We further model biological information processing by utilizing a fully connected layer combined with neuron nodes. In a wide range of scenarios (e.g., citation networks, image graph classification, and recommender systems), our experimental results show that the proposed method could gain competitive performance against state-of-the-art approaches. Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.

No abstract