Jumping Risk Communities in the Energy Industry: An Empirical Analysis Based on Time-Varying Complex Networks

This paper uses the 5-five-minute high-frequency data of energy-listed companies in China's A-share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time-varying characteristics. However, the infection problem of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time-varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry.

Potential factors in determining cross-border price spillovers in the pork sector: Evidence from net pork-importing countries

Although food self-sufficiency is regarded as a potent strategy to secure food supply in the policy circle, the efficacy of policy measures, especially in terms of their quantitative effects, is still not fully understood. We analysed the relationships between international and local prices of pork between January 2001 and December 2018 for 10 net pork-importing countries. The primary outcome obtained in our research is that high self-sufficiency and a small trade volume of pig meat commodities could impair price volatility transmission from the global market. This result does not suggest that a protectionist regime should be established to stabilise the national food supply. It presents useful information to balance the benefit from highly efficient resource allocation and the market steadiness gained from higher self-sufficiency in food, considering the maximisation of the expected utility of economic agents.

An approach for forecasting the Onion Price volatility in Indian Wholesale Markets using hybrid GARCH-LSTM Deep learning models

The fluctuations in the Onion prices have led to political and economic ramifications in countries such as India. In this study, we intend to estimate and then forecast the price volatility of Onion sales prices in major Indian wholesale markets. Initially, we take daily price data from major vegetable wholesale markets across India and simulate them to compute corresponding daily conditional volatilities using the traditional GARCH method. We then forecast the volatilities for the upcoming 10,15 and 21 days using the same traditional GARCH method and compare its forecasting accuracy with recent AI-led models. According to our comparisons, the deep learning-based LSTM model with various configurations provides superior results when compared to other traditional models with the highest accuracy in more than 70% of the cases. We expect that the given study could help the policymakers in managing sufficient buffer stock levels and the food supply chain stakeholders in hedging against the overall market risks due to the fluctuations in prices.

Fiscal Policy, Monetary Policy and Price Volatility: Evidence from an Emerging Economy

Vietnam is an Asian emerging country, which now is ranked in the group of the fastest-gro-wing economies worldwide. However, this economy has faced galloping inflation in recent years. So the Vietnamese experience is a valuable reference for the policymakers in the developing world in order to successfully control price volatility. Our study applies the Vector autoregressive method, the Johansen cointegration test, and the Granger causality test to examine the impact of fiscal and monetary policy on price volatility in Vietnam with a quarterly data sample collected over the period from 2004 to 2018. The study results confirm the existence of a long-term cointegration relationship between these policies and price volatility in Vietnam. Besides, the variance decomposition and impulse response function also show that the impact of these policies on inflation is clear, however, the fiscal policy more strongly affects inflation than the monetary policy. Finally, the Granger causality test also indicates one-way causality relationships from the government expenditure as well as the exchange rate to price volatility in the study period.