Fluctuation and Noise Letters

The ongoing COVID-19 shocked financial markets globally, including China’s crude oil future market, which is the third-most traded crude oil futures after WTI and Brent. As China’s first crude oil futures are accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore, this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare the market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly used random walk tests. Then, we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring.

This paper studies how return connectedness exhibits potential linkages among 17 economies over a 20-year period starting in 2001. We obtained three main results by employing the dynamic connectedness approach, which is based on vector auto-regression (VAR), to calculate generalized forecast error decompositions. First, although the financial crisis (2007–2008) experienced a high level of connectedness, the spillover index spiked during the early stages of the COVID-19 outbreak. Second, the “return shock sender” is a community of countries that includes the United States, Australia, and European countries, while Vietnam is immune to financial linkages. Third, we discovered the predictive power of U.S. economic policy uncertainty and disease fear with market volatility for the Vietnamese return connectedness. As a result, our research identifies a range of relevant policies to mitigate spillover risks in the context of financial stability.

The complex dynamics of financial asset prices play a pivotal role in the global economy and consequently in the life of the people. Thus, this research encompasses a systematic analysis of the price dynamics of the financial assets considering simultaneously four critical attributes of the financial market (disorder, predictability, efficiency and similarity/dissimilarity). We explore these essential attributes of the financial market using the permutation entropy ($H_s$) and Fisher Information measure ($F_s$), and cluster analysis. Primary, we use the values of the information theory quantifiers to construct the Shannon–Fisher causality plane (SFCP) allows us to quantify the disorder and assess the randomness exhibited by these financial price time series. Bearing in mind the complexity hierarchy, we apply the values of $H_s$ and $F_s$ to rank the efficiency of these financial assets. The overall results suggest that the fiat currencies of developed countries, such as the Canadian dollar (CAD), British pound (GBP), and Norwegian krone (NOK), display higher disorder, lower predictability, and higher efficiency than other financial assets such as Crude oil (WTI) and Foreign exchange rates. Also, the cluster analysis provided by the K-means and the Hierarchical cluster techniques grouped these financial assets into only three distinct groups. We conclude that an oligopolistic market structure drives the WTI. At the same time, the other financial assets are characterized by atomized markets.

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.

We considered the daily price dynamics of the US Bitcoin market in the period from 2015 to 2022. In the first step, we used a singular value decomposition (SVD) entropy method for assessing time-varying informational efficiency over different time scales, from weeks to quarters. It was shown that the US Bitcoin market has been informationally efficient most of the time, except for some isolated periods where the returns exhibited deviations from the random behavior. The COVID-19 pandemic has not impacted the informational efficiency. This suggests that the Bitcoin market is unpredictable, and no reliable predictions can be obtained. A further analysis was carried out by considering the recurrence intervals for different positive and negative returns. We found that the distribution of recurrence intervals for positive and negative returns is asymmetric, with mean values higher for negative returns. We found that the distribution of recurrence intervals can be described by a stretching exponential distribution, such that the empirical and analytical hazard probabilities as functions of the elapsed time show good agreement.

Financial time series are nonlinear, volatile and chaotic. Inspired by quantum computing, this paper proposed a new model, called primary ensemble empirical mode decomposition combined with quantum neural network (PEEMD-QNN) in predicting the stock index. PEEMD-QNN takes the advantages of the PEEMD which retains the main component of modal component and QNN. To demonstrate that our PEEMD-QNN model is robust, we used the new model to predict six major stock index time series in China at a specific time. Detailed experiments are implemented for both of the proposed prediction models, in which empirical mode decomposition combined with QNN (EMD-QNN), QNN and BP neural network are compared. The results demonstrate that the proposed PEEMD-QNN model has higher accuracy than BP neural network, QNN model and EMD-QNN model in stock market prediction.

This work examined the informational efficiency of the European CO₂ emission trading market for the different implementation phases in the period 2008–2022:Q3. The approach is based on a bootstrap singular value decomposition (SVD) approach and the analysis was conducted for a rolling window to assess the time-varying efficiency and over different time scales. The impact of the COVID-19 lockdown and the Russia–Ukraine conflict was evaluated. The results showed that the informational efficiency changes over time and scales, which is in line with adaptive market hypothesis (AMH) notions. High market efficiency was exhibited in Phase II (2008–2012), but large deviations from efficiency, especially for quarterly scale, were exhibited in Phase III. However, Phase IV has shown a behavior consistent with informational efficiency. The COVID-19 outbreak negatively impacted informational efficiency. On the other hand, it seems that the Russia–Ukraine conflict has improved informational efficiency due to the European decisions to move to energy independence policies. The findings in this study suggest that the European carbon market is gradually attaining a state of financial maturity.

Due to the increasing connectedness of international financial markets, the measurement of dynamic connectedness among large-scale assets has become a key component of modern financial risk regulation and asset allocation principles. We quantify the dynamic connectedness among large-scale assets and visualize the return spillover paths using cutting-edge complex network spillover measurement theory and physical complex network methods. For the sample period of January 2, 2018 to June 30, 2022, we calculate the daily returns for thirteen representative global large-scale assets. Then, we construct a time-varying parameter-vector autoregressive-stochastic volatility (TVP-VAR-SV) model and measure the time-varying spillover matrix of returns across large-scale assets. From our analyses, the 10-year U.S. treasury rate (shorted for the US10YR) and Brent oil are found to be the core subject matter of global assets. The US10YR has a significant impact on the commodity market. Moreover, there is a significant impact of foreign exchange on other global large-scale assets, and the spillover effects vary from one country to another. On the basis of the empirical findings, this paper proposes recommendations for financial regulators regarding risk monitoring and forward-looking investment recommendations for financial institutions and investors.

This study proposes a novel approach to investigating the multifractality of time series using the multifractal cross-correlation detrended moving average analysis (MF-X-DMA). The study demonstrates the behavioral differences of MF-X-DMA in coherent and non-coherent time periods. Due to the lack of a mechanism to capture the dynamical cross-correlation in time series, correlated time series with multifractal structure present a barrier for analysis. The study shows that when the wavelet coherence method is applied to time series, co-movement between time series can be easily captured in certain time intervals, providing an efficient way to find time intervals to apply MF-X-DMA. The study applies the wavelet coherence method to the daily spot prices of gold and platinum from January 1987. It shows that the wavelet coherence method is an excellent engine to extract designated time series in certain frequency and time intervals, eliminating the need for windowing or shuffling methods. Additionally, the study observes a long-term power law cross-correlation using detrended cross-correlation analysis coefficients of inversed series for both low-correlated and high-correlated series. Finally, the findings indicate that MF-X-DMA leads to superior results compared to MF-DFA when provided with highly correlated data.

Utilizing wavelet coherence analysis, we investigate the correlation of fluctuations and phase differences between Bitcoin and RMB to identify capital flows between the two currencies. The effects of Digital Currency Electronic Payment (DCEP) on their co-movement are further analyzed. Our findings reveal that the RMB exchange rate leads the price of Bitcoin in all significant co-movement areas. Furthermore, it appears that from February 2017 to September 2018, the Sino-US trade frictions and US dollar interest rate hikes may have resulted in a long-term negative co-movement, which seems to have been driven by RMB and possibly indicated capital flows from RMB to Bitcoin. The short-term positive co-movement between November 2019 and July 2020 could be attributed to the COVID-19 pandemic. Finally, we also demonstrate that the DCEP trial event has the potential to strengthen the positive co-movement between these currencies.