The path to a sustainable palm oil futures market

Lee, S.; Yi, Eunjou; Cho, Y.; Ahn, Kwangwon

doi:10.1016/j.egyr.2022.04.048

Cited by 5 publications

(5 citation statements)

References 68 publications

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“…This process disrupts the temporal order in the time series and would therefore remove any possible causal relationship arising from the bias of a finite sample size. The ETE is calculated following [ 29 , 48 – 50 ]: where Y (i) is an independently shuffled series of Y in the i -th trial; thus, ETE is demeaned TE, which is calculated by subtracting the arithmetic average of the randomized TE values from the estimated TE. Specifically, we shuffled 1,000 times and produced 1,000 TE values from independently shuffled time series over the same sample space.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, we standardize the TE to a Z-score. As the mean of the Z-score is approximately zero according to the law of large numbers, the Z-score spread approximately indicates the statistical significance of TE [ 36 , 49 , 50 ]. In sum, we can loosely say that (i) a |Z-score| > 2.58 is in the top 1% of the critical region and hence is comparable to a p -value of 0.01; and (ii) a |Z-score| > 1.96 is in the top 5% of the critical region.…”

Section: Methodsmentioning

confidence: 99%

“…statistical significance of TE [36,49,50]. In sum, we can loosely say that (i) a |Z-score| > 2.58 is in the top 1% of the critical region and hence is comparable to a p-value of 0.01; and (ii) a |Z-score| > 1.96 is in the top 5% of the critical region.…”

Section: Plos Onementioning

confidence: 95%

Section: Andmentioning

confidence: 99%

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Information flow among stocks, bonds, and convertible bonds

Choi

Jeong

et al. 2023

PLoS ONE

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This study examines the information flow between convertible bonds (CBs) and other investment assets, such as stocks and bonds. In particular, we employ transfer entropy (TE) as a proxy for the causal effect between the two assets considering that one of the most widely used methods, Granger causality, requires strict assumptions. When adopting TE, we find that asymmetric information flow arising between assets depends on macroeconomic phases. The stock and bond markets affected the CB market prior to and during the global financial crisis, respectively. In the post-crisis period, we find no meaningful information exchange between CBs and other investment assets concerning their return series. However, we observe a significant cause–effect relationship between CBs and stocks in the rise–fall patterns of their price series. The findings suggest that the appearance of one-directional information flow depends on macroeconomic conditions and the level of data, for example, return series or price fluctuations. Accordingly, investors could exploit this pattern predictability in their portfolio management. In addition, policymakers must closely monitor the information flow among the three markets. When any two markets exchange information in a state of strong market integration, unbalanced regulation between them could lead to market distortions and regulatory arbitrage.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 95%

Section: Andmentioning

confidence: 99%

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Information flow among stocks, bonds, and convertible bonds

Choi

Jeong

et al. 2023

PLoS ONE

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“…By considering the attributes of the interactions, transfer entropy quantifes the amount of information transport in a nonsymmetric manner. In particular, fnding nonzero rates of information transmission in both directions implies a dynamic correlation in producing and receiving information between the two time series [43][44][45]. Following Schreiber [43], we defne the transfer entropy from system Y to X as follows:…”

Section: Transfer Entropymentioning

confidence: 99%

Technological Change and Market Conditions: Evidence from Bitcoin Fork

Kim

Lee

et al. 2022

Complexity

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This article examines the impact of technological changes to cryptocurrency—known as “forking” that triggers blockchain splits—on market conditions. Despite the explicit distinction in log return distributions between the two splitting blockchains, adopting new technology does not result in a disparity in market conditions: no significant difference exists in market efficiency and long-term market equilibrium between the two splitting blockchains. Technological changes accompanying market separation do not impede the underlying uniformity in market conditions. The findings suggest that mutual information flows linked to market liquidity explain the results between the new and old forks.

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