Dynamic Cross-Correlations Analysis on Economic Policy Uncertainty and US Dollar Exchange Rate: AMF-DCCA Perspective

Zhao, Ruwei; Cui, Yian

doi:10.1155/2021/6668912

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…Conversely, in the case of too short a length, some short-term parameters may experience significant fluctuations, making it difficult to find the trend [73]. Therefore, we use Zhao and Cui [74]'s rolling window setting of 1500 days as the window length because of the large sample size. To begin, the first 1500 days from the start of returns are taken to obtain the Hurst exponent (q = 2) and MLM.…”

Section: Resultsmentioning

confidence: 99%

Modeling Dynamic Multifractal Efficiency of US Electricity Market

2021

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The dramatic deregulatory reforms in US electricity markets increased competition, resulting in more complex prices compared to other commodities. This paper aims to investigate and compare the overall and time-varying multifractality and efficiency of four major US electricity regions: Mass Hub, Mid C, Palo Verde, and PJM West. Multifractal detrended fluctuation analysis (MFDFA) is employed to better quantify the intensity of self-similarity. Large daily data from 2001 to 2021 are taken in order to make a more conclusive analysis. The four electricity market returns showed strong multifractal features with PJM West having the highest multifractality (corresponding to lowest efficiency) and Mass Hub having the lowest multifractality (i.e., highest efficiency). Moreover, all series exhibited mean reverting (anti-persistent) behavior in the overall time period. The findings of MFDFA rolling window suggest Palo Verde as the most volatile index, while a significant upward trend in the efficiency of Mass Hub and PJM West is observed after the first quarter of 2014. The novel findings have important implications for policymakers, regulatory authorities, and decision makers to forecast electricity prices better and control efficiency.

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Section: Resultsmentioning

confidence: 99%

Modeling Dynamic Multifractal Efficiency of US Electricity Market

2021

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“…Recent research aimed to shed light on the internal dynamics of these cross-correlations that occur in numerous simultaneously recorded time series (Aslam et al 2022;Podobnik et al 2009;Y. Shi et al 2020a, b;Waţorek et al 2019;Xiong et al 2018;Zhao and Cui 2021).…”

Section: Multifractal Detrended Analysis Methodsmentioning

confidence: 99%

On the inner dynamics between Fossil fuels and the carbon market: a combination of seasonal-trend decomposition and multifractal cross-correlation analysis

Aslam

Ali

Amjad

et al. 2022

Environ Sci Pollut Res

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This study examines the inner dynamics of multifractality between the carbon market (EU ETS) and four major fossil fuel energy markets: Brent Crude Oil (BRN), Richards Bay Coal (RBC), UK Natural Gas (NGH2), and FTSE350 electricity index (FTSE350) from January 04, 2016, to March 04, 2022. First, we decompose the daily price changes by applying seasonal and trend decomposition using loess (STL). Then, we examine the inner dynamics of multifractality and cross-correlation by employing multifractal detrended fluctuation analysis (MFDFA) and multifractal detrended cross-correlation analysis (MFDCCA) using the remaining components of the return series. Our findings reveal that all series and the cross-correlations of carbon and fossil fuels markets have multifractal characteristics. We find crude oil to be the most efficient market (lowest multifractal), while coal is the least efficient (highest multifractal). Only coal shows persistent, whereas the other markets exhibit antipersistent behavior. Interestingly, the coal and EU ETS pair demonstrates a higher degree of multifractal patterns. In contrast, the pair of natural gas and EU ETS exhibits the lowest multifractal characteristics among the energy markets. Only the crude oil market shows persistent cross-correlations in the multifractality. These findings have important academic and managerial implications for investors and policymakers.

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“…However, policies of government subsidies can also have a negative efect, for instance, on the supply chain system's stability [11]. Furthermore, policies are found to have a signifcant impact on frms' production behavior [12,13], economic returns [14], and even the fuctuation of international markets [15]. Tese empirical results all suggest the importance to extract information from policy text and to evaluate the efects of policy.…”

Section: Theoretical and Institutional Backgroundmentioning

confidence: 99%

The Temporal Spatial Dynamic of Land Policy in China: Evidence from Policy Analysis Based on Machine Learning

Yao

Zhu

2022

Mathematical Problems in Engineering

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Extracting useful information from a large number of policy texts is a challenging and insufficiently discussed topic. Utilizing large sample policy texts and a method of machine learning, this study contributes to the research gap by systematically analyzing the temporal evolution and spatial differentiation of China’s land policy from 1998 to 2018. A framework comprising six major themes of land policy, namely, “land development, land acquisition and demolition, cultivated land protection, land planning, land consolidation and utilization, and land confirmation and transfer” is first established, according to the theoretical and institutional background of land management. Based on this framework, the Latent Dirichlet Allocation analysis of more than 20,000 policy documents at different levels of government shows that, (1) temporally, the priority of land policy evolves with the spirit of the central document and the macropolitical and economic conditions and, (2) spatially, there are significant differences in land policies among provinces. Overall, the analysis of land policy documents shows the tradeoff between cultivated land protection and land development and also the emphasize on other topics, with the changes in land policy priorities in different periods and regions.

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Dynamic Cross-Correlations Analysis on Economic Policy Uncertainty and US Dollar Exchange Rate: AMF-DCCA Perspective

Cited by 10 publications

References 39 publications

Modeling Dynamic Multifractal Efficiency of US Electricity Market

Modeling Dynamic Multifractal Efficiency of US Electricity Market

On the inner dynamics between Fossil fuels and the carbon market: a combination of seasonal-trend decomposition and multifractal cross-correlation analysis

The Temporal Spatial Dynamic of Land Policy in China: Evidence from Policy Analysis Based on Machine Learning

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