Basing on daily price data of carbon emission rights in futures markets of Certified Emission Reduction (CER) and European Union Allowances (EUA), we analyze the multiscale characteristics of the ...markets by using empirical mode decomposition (EMD) and multifractal detrended fluctuation analysis (MFDFA) based on EMD. The complexity of the daily returns of CER and EUA futures markets changes with multiple time scales and multilayered features. The two markets also exhibit clear multifractal characteristics and long-range correlation. We employ shuffle and surrogate approaches to analyze the origins of multifractality. The long-range correlations and fat-tail distributions significantly contribute to multifractality. Furthermore, we analyze the influence of high returns on multifractality by using threshold method. The multifractality of the two futures markets is related to the presence of high values of returns in the price series.
•Asymmetric multifractal method and time-delay DCCA method are used.•The static and dynamic interdependent structures under different trends and fluctuations are studied.•Focuses on the impact of ...China’s policy of shutting down cryptocurrency on market interdependence and risk transmission.
By taking Bitcoin, Litecoin, and China’s gold and RMB/US dollar exchange rate market as research objects, this paper apply the MF-ADCCA and time-delayed DCCA methods to study the impact of China’s mainland shutdown of cryptocurrencies trading on the non-linear interdependent structure and risk transmission of cryptocurrencies and its financial market. Empirical results show that the cross-correlation between cryptocurrencies and China’s financial market has a long memory and asymmetric multifractal characteristics. After the shutdown, the long memory between cryptocurrencies and Chinese gold has weakened, and the long memory between cryptocurrencies and the RMB/US dollar exchange rate market was strengthened. China’s shutdown policy has a certain risk prevention effect. Specifically, after the implementation of the policy, the risk transmission of cryptocurrencies to China’s financial market has weakened, but the influence of China’s financial market has gradually strengthened.
The acceleration of economic globalization gradually shows the linkage of the stock markets in various counties and produces a risk conduction effect. An asymmetric MF-DCCA method is conducted based ...on the different directions of risk conduction (DMF-ADCCA) and by using the traditional MF-DCCA. To ensure that the empirical results are more objective and robust, this study selects the stock index data of China, the US, Germany, India, and Brazil from January 2011 to September 2014 using the asymmetric MF-DCCA method based on different risk conduction effects and nonlinear Granger causality tests to study the asymmetric cross-correlation between domestic and foreign stock markets. Empirical results indicate the existence of a bidirectional conduction effect between domestic and foreign stock markets, and the greater influence degree from foreign countries to domestic market compared with that from the domestic market to foreign countries.
•Asymmetric MF-DCCA method based on different directions of transmission is proposed.•The simulation results of DMF-ADCCA method are discussed.•Asymmetric DMF-DCCA method is applied to the analysis of the stock markets.•Nonlinear Granger causality test is employed to Chinese and foreign stock markets.
• MF-DCCA, MFXDMA, MFDCCA-MODWT, and MF-DPXA the four methods are compared from long-memory level and multifractal features.• Exhaustive series length (from 2000 to 20,000) and Hurst exponent are ...generated to ensure the confidence of this paper.• The effectiveness of MF-DPXA was verified by two-component ARFIMA process.
The two-component autoregressive fractionally integrated moving average (ARFIMA) process and mix-correlated ARFIMA(MC-ARFIMA) are applied in this paper to generate artificial sequence with Hxy = 1/2(Hx + Hy) and Hxy < 1/2(Hx + Hy) respectively and simulate the results of multifractal detrended cross-correlation analysis (MFXDFA), multifractal detrending moving average cross-correlation analysis (MFXDMA), MFDCCA based on maximum overlap wavelet transform (MFDCCA-MODWT), and multifractal detrended partial cross-correlation analysis(MF-DPXA). The advantages and disadvantages of MFXDFA, MFXDMA(θ = 0,0.5,1), and MFDCCA-MODWT are compared to the long-memory of sequences. In the case of Hxy < 1/2(Hx + Hy), these three methods keep around the theoretical value with small fluctuations in a variety of sequence lengths. In the case of Hxy = 1/2(Hx + Hy), the curves are significantly stable and are slightly smaller than the theoretical value. The precision of these estimators may be influenced by the relationship between Hxy and 1/2(Hx + Hy). Multifractal features is detected and the result shows that MFXDMA-0 and MFXDMA-1 is optimal to detect the multifractality. An interesting finding is that MFDCCA-MODWOT performs best in both case of Hxy = 1/2(Hx + Hy) and Hxy < 1/2(Hx + Hy), but it performs worst to detect the multifractality. When Gaussian noise is added to the sequences with different long-memory levels, MFDPXA can eliminate the noise interference compared with MFXDFA, thereby verifying the effectiveness of this method.
Based on the analysis of the shortage of emergency rescue services and the supervision of public-private-partnership (PPP) projects, this paper explores the rent-seeking game predicament and ...establishes a three-player rent-seeking game model among government regulators, private enterprises, and government agents in the bidding operation of PPP projects by learning from Stackelberg game theory. Through the numerical simulation analysis, this paper explores the impact of the relevant variables on the model results, which can lead to the following conclusions: the emphasis on government regulators changes due to rent-seeking cost changes; when the success rate of verification is high, government regulators can take supervision randomly. Inversely, supervision channels should be broadened when the supervision fails. When the punishment on government agents and private enterprises is great, relaxing supervision may be appropriate. This study proposes the relevant policy recommendations for government regulators to improve their work.
Modelling the dynamics of carbon emission allowance prices has been of keen interest to academicians from around the world. The distribution of tails is different from that of centres for carbon ...allowance prices, invalidating regular methods and conventions applied in quantitative finance. Value-at-Risk (VaR) and Expected Shortfall (ES) are traditionally useful tools to measure the extreme risk of carbon markets. On this basis, this study uses the semiparametric method to estimate the time-varying VaR and ES of European Union Allowance (EUA) and Chinese carbon emission allowance (CEA) in downside and upside events. Our results show that the GAS model outperforms the rolling estimate in describing the time-varying downside and upside VaR and ES. We find two breaks in VaR and ES for EUA whilst no break for CEA. We also find the evidence of asymmetry in CEA and reversed asymmetry in EUA, but it differs across periods and between VaR and ES. In addition, the results reveal that the downside CEA VaR and ES are significantly greater than those of EUA. Meanwhile, the relationship between upside EUA and CEA series varies across periods as well as between VaR and ES. These findings have important implications for entrepreneurs, investors, risk managers, and policy makers.
•We evaluate model efficiency when estimating extreme risks of carbon prices.•We extend the extreme risk by estimating the time-varying upside VaR and ES.•There are structural breaks and asymmetry in extreme risks of carbon prices.•The VaR and ES of carbon prices differ between Chinese and European market.
Under the current carbon neutrality goal, energy structure transformation and international oil price fluctuations make research on the dependence of crude oil and clean energy has important ...theoretical and practical significance. This study proposes an asymmetric variable coefficient quantile regression model to measure the dependence and asymmetry of crude oil futures and clean energy stock markets under different market conditions. The results show that there is a positive dependence between crude oil futures and clean energy which is asymmetric in quantile. The positive and negative returns of crude oil futures have a time-varying asymmetric impact on the clean energy stock markets. In addition, the portfolio results show that options involving the Chinese clean energy market has a lower VaR.
•Researched asymmetric spillover effects between cryptocurrencies and China's financial markets.•The spillover effects of positive volatility and negative volatility are compared.•Using TVP-VAR to ...construct spillover index to study dynamic connectedness characteristics.
In this paper, we constructed a volatility spillover index based on the time-varying parameter vector autoregressions (TVP-VAR) model to study the asymmetric volatility spillover effect between cryptocurrency and China's financial market. Our results show that the impact of cryptocurrency on China's financial market is relatively strong, but the impact of China's financial market on cryptocurrency is very weak. Furthermore, negative spillovers are stronger than positive spillovers. The average negative volatility spillover is dominant for Bitcoin and Ethereum, but the average positive volatility spillover is dominant for Ripple. This study has implications for investors and policymakers.
The risk conduction mechanism between a cryptocurrency and the US dollar (USD), the renminbi (RMB), and gold markets is helpful for investors' risk management. Based on the non-linearity and ...asymmetry of the correlation between the fractal market hypothesis and the financial market, this study uses asymmetric multifractal cross-correlation analysis to study the correlation between a cryptocurrency and USD, RMB, and gold markets. First, using the MF-ADCCA method, an obvious asymmetric cross-correlation is found between a cryptocurrency and USD, RMB, and gold markets. USD, RMB, and gold markets are more effective than cryptocurrency markets. In particular, the impact of the RMB market on the cryptocurrency market is the largest among USD, RMB, and gold. When USD and RMB are rising, the risk conduction to cryptocurrencies is more significant. When gold is falling, the risk conduction to cryptocurrencies is more significant. When cryptocurrencies are falling, the risk conduction to USD is more significant. Among cryptocurrencies, Bitcoin, Ethereum, Litecoin, and New Economic Coin have more significant risk conduction to RMB when it falls. Through the DMF-ADCCA method, a two-way asymmetric conduction effect is found between the lagging cryptocurrency and the USD, RMB, and gold markets. Compared with USD and gold, the risk correlation and conduction between RMB and cryptocurrency are the highest. Preventing and controlling the risk impact of cryptocurrency on the RMB and further strengthening the research between cryptocurrency and the RMB are crucial to the internationalization of the RMB and the improvement of international competitiveness.
This paper investigates the nonlinear structure between carbon and energy markets by employing the maximum overlap wavelet transform (MODWT) as well as the multifractal detrended cross-correlation ...analysis based on maximum overlap wavelet transform (MFDCCA-MODWT). Based on the MODWT multiresolution analysis and the statistic Qcc(m) significance, relatively significant cross-correlations are obtained between carbon and energy future markets either on different time scales or on the whole. The result of the Granger causality test indicates bidirectional Granger causality between carbon and electricity future markets, although the Granger causality relationship between the carbon and oil price is not evident. The existence of multifractality for the returns between carbon and energy markets is proven with the MFDCCA-MODWT algorithm. In addition, results of investigating the origin of multifractality demonstrate that both long-range correlations and fat-tailed distributions play important roles in the contributions of multifractality.
•We employ MFDCCA based on MODWT to conduct an empirical analysis of the cross-correlation of returns between carbon and energy markets.•We investigate the nonlinear structure and cross-correlation between carbon and energy markets by employing MODWT on different scales.•We use the Granger causality test to discuss the transmission direction of the cross-correlation.•We draw the original of multifractality by applying shuffle and surrogate approach.
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