Trade friction and price discovery in the USD–CAD spot and forward markets

Meng, Yan; Chen, Jian; Song, Victor; Xu, Ke

doi:10.1016/j.najef.2021.101628

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…If the autocorrelation function of the cointegration vector β X ′ t exhibits long memory that is not consistent with an I(0) process, we need to consider the possibility of fractional cointegration. Studies have shown that fractional cointegration describes rather well financial time series data such as exchange rates Baillie and Bollerslev (1994), Yan et al (2022) and commodity spot and futures prices Dolatabadi et al (2018) and Wu et al (2021). We can do likelihood ratio tests with the null hypothesis being d b = to examine whether the FCVAR model fits the data better than the CVAR model.…”

Section: The Fcvar Modelmentioning

confidence: 99%

Price discovery and long‐memory property: Simulation and empirical evidence from the bitcoin market

Xu,

Chen,

Liu

et al. 2024

Journal of Futures Markets

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Price discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery.

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Section: The Fcvar Modelmentioning

confidence: 99%

Price discovery and long‐memory property: Simulation and empirical evidence from the bitcoin market

Xu,

Chen,

Liu

et al. 2024

Journal of Futures Markets

Self Cite

View full text Add to dashboard Cite

show abstract

“…Chatrath and Song (1998) [2] asserted that the Japanese foreign exchange futures market is more effective than the spot market at capturing information when the United States makes unexpected macroeconomic statements, hence playing a preeminent role in price discovery. In a similar vein, Yan et al (2021) [3] discovered that the trade frictions between Canada and its major trading partners impacted the information structure of the Canadian foreign currency market, where the futures market integrated information more quickly and led the price discovery process.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of ESG Information Disclosures

Ye¹,

Kassim²,

Zhang³

2023

FBEM

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ESG is a framework for the disclosure of non-financial information about companies, an investment philosophy and corporate evaluation criteria that focuses on non-financial performance. ESG ratings are a key part of ESG development, and the current number of global ESG rating agencies, with very different backgrounds and divergent ratings, makes it difficult to generate consensus on the ratings of the same subject. Therefore, on the basis of sorting out the situation of 11 famous ESG rating agencies and comparing and analyzing the ESG evaluation system of each rating agency, the study found that China's existing ESG rating system has core problems such as poor quality of information disclosure, inconsistent ESG rating results and imperfect ESG ecosystem, and accordingly put forward several suggestions for China's future ESG development.

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Empirical Analysis on Price Discovery Efficiency of Cotton Futures in China

Li¹

2023

FBEM

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Futures markets are fundamentally driven by price discovery. China has the highest cotton output and consumption, so enhancing the cotton futures price discovery function promotes the sustainable and healthy growth of China's cotton sector. We used cotton futures and spot prices from 2018 to 2022 as research samples to examine the changes in cotton futures price discovery efficiency amid the Sino-US trade war and the appearance of Covid-19. Using the IS model, we observed that: (1) the cotton futures market always holds a dominating position in price discovery throughout 2018-2022, and (2) the impact of Covid-19 significantly lowered the price discovery function of the cotton futures market, which shows that: (1) the Chinese cotton futures market is not yet able to endure the impact of unexpected and large-scale health events such as Covid-19; and (2) when emergencies arise, we should respond expeditiously in order to reestablish market stability.

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Trade friction and price discovery in the USD–CAD spot and forward markets

Cited by 3 publications

References 35 publications

Price discovery and long‐memory property: Simulation and empirical evidence from the bitcoin market

Price discovery and long‐memory property: Simulation and empirical evidence from the bitcoin market

Comparative Analysis of ESG Information Disclosures

Empirical Analysis on Price Discovery Efficiency of Cotton Futures in China

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