Narrow Results

The gold futures in emerging markets have gained more importance in parallel to the increase in the size of gold trading in these markets. This research aims to detect the long-run price linkages and causality effects in these markets. China, Brazil, Russia, India, Korea, Taiwan, Turkey and Indonesia have been selected to represent emerging markets. US and Japan are also included as benchmark markets. The results denote the existence of long-term price dependencies and limited risk diversification benefits in the sample countries. The results further signify that China and Russia are the most isolated countries among the emerging markets sample.

The price of gold and its determining factors have been studied extensively in the literature. However, there is a lack of research on structural break in the long memory of the gold markets. This paper examines the long memory properties of gold prices. In particular, it attempts to test the stability of the long range dependence of gold returns and volatility. The results suggest that long memory exists in gold returns and volatility, and that the volatility of daily gold futures returns can be characterized by a hyperbolic decaying long memory process. Three episodes of structural breaks are found.

Important nodes can determine the internal structure of complex networks and reveal the internal relationships of real-world systems, and identifying key nodes in complex networks is one of the important research areas of complex network science. As the king of commodities, changes in the price of gold significantly impact the economic development of various countries. Especially in the early stages of the outbreak of war between Russia and Ukraine, the price of gold futures has been greatly impacted, and the systemic risks are gradually spreading. In this paper, a gold future price series is mapped into a visibility graph (VG), the characteristics of the gold price time series and key points-in-time, have been explored from the perspective of complex network. First, according to the data structure characteristics of gold futures, this paper converts the closing prices of gold futures of the New York Mercantile Exchange into a complex network through the VG model. Then, by using the complex network model to further delve into the price of gold futures, it is found that the degree distribution of the gold futures network follows a power-law distribution, and has obvious scale-free characteristics. Finally, this paper uses the visual network node shrinking algorithm and the technique for order preference by similarity to ideal solution (TOPSIS) analysis method to identify the key nodes of the gold futures visual map to find the key time nodes in the timeline of gold futures market. Analysis of the key time nodes of this market by four methods reveals that the repetition rate of the key time nodes in the methods’ top 10 ranking is as high as 82.5%, indicating that the results obtained in this paper are robust. This study introduces a new model to describe the characteristics of gold futures price series, one which can find key time nodes in gold futures prices and provide potential help for predicting gold futures prices.