Why do carbon prices and price volatility change?

Ibrahim, Boulis Maher; Kalaitzoglou, Iordanis

doi:10.1016/j.jbankfin.2015.11.004

Cited by 34 publications

(33 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Third, individual investors. As stated by Ibrahim & Kalaitzoglou [ 48 ], individual investors have certain effects on the carbon market, and the government should create satisfying conditions to appeal individual investors, this may do some changes for the existing asymmetry.…”

Section: Discussionmentioning

confidence: 99%

Carbon price volatility: The case of China

Zhang

Liu

2018

PLoS ONE

View full text Add to dashboard Cite

Based on carbon spot prices selected from seven carbon pilots, we assess the financial performances related to carbon volatility in China on the overall perspective. According to the results, the Chinese carbon market fluctuated severely at the beginning of carbon trading, but has stabilised in general, despite several dramatic changes related to ‘yearly compliance events’. Long-term memory exists in the volatility series. Moreover, asymmetry exists in the Chinese carbon market, and volatility reacts more severely to good news than to bad news. Finally, we discuss our empirical results, and make certain suggestions regarding firms’ awareness, international cooperation and individual investors not only for policy makers in China but also for other developing countries who are contemplating either commencing carbon trading or improving the current market.

show abstract

Section: Discussionmentioning

confidence: 99%

Carbon price volatility: The case of China

Zhang

Liu

2018

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Using a supply and demand based correlation model, Kanamura (2016) report a positive energy price impact on EUA prices. Ibrahim and Kalaitzoglou (2016) proposed an asymmetric information microstructural pricing model in which price responses to information and liquidity vary with every transaction and find that expected trading intensity to simultaneously increase the information component and decrease the liquidity component of price changes.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility Spillover and Dynamic Correlation Between the Carbon Market and Energy Markets

Chen

et al. 2019

Journal of Business Economics and Management

View full text Add to dashboard Cite

This paper studies the volatility spillover and dynamic correlation between EU emission allowance (EUA) prices and energy prices by considering three energy commodities, including oil, gas, and coal. The asymmetric BEKK model is employed for multi-phase analysis of EU ETS, yet only a little empirical evidence backing up the existence of volatility spillover between EU ETS and energy markets, i.e., the establishments of the EU ETS may not effectively limitation and influence energy markets. The time-varying conditional correlation between EUA and each of energy prices is analyzed. The dynamic correlation shows there is a relatively stable, positive correlation between the EUA and Brent oil, natural gas. However, modeling the dynamics correlation also suggests that the correlation between the EUA and the natural gas, coal became weaker and more volatile since second and third phases, especially after the Global Financial Crisis in 2008, which may indicate that the demand reduction in emission allowances caused by the economic slowdown far exceeds the reduction in the annual restraint of EU ETS.

show abstract

“…While several microstructure models have been designed for and applied to stock markets (Hasbrouck and Harris, 2006;Handa et al, 2003;etc. ), currency markets (see, e.g., Katusiime et al, 2015) and futures markets (see, e.g., Hsu and Lee, 2014), very few models have been built for commodity markets to study exclusively the composition of trading costs (Ibrahim and Kalaitzoglou, 2016). By contrast, econometric estimations have been extensively used to assess the profitability of trading strategies in commodity derivatives markets such as carbon futures markets (see for example Narayan and Sharma, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…34.1%) of the spread on average. As a result, placing limit buy orders is less risky and costly in terms of adverse selection for uninformed traders than placing sell limit orders (Ibrahim and Kalaitzoglou, 2016). Fourth, the spread and the sellers' (resp.…”

Section: Introductionmentioning

confidence: 99%

Limit order books, uninformed traders and commodity derivatives: Insights from the European carbon futures

Rannou¹

2019

Economic Modelling

View full text Add to dashboard Cite

This paper provides a suitable model for studying the strategic behavior of uninformed investors that trade commodity derivatives via limit order books. Two main testable implications are obtained after solving for the model equilibrium. The adverse selection costs of uninformed traders depend on the inflow of market orders and their risk aversion. Next, the adverse selection costs of uninformed buyers and sellers and the difference of their asset valuations determine the size of their bid-ask spread. An analysis of European carbon futures data confirms the relevance of these implications. Moreover, we detect a diagonal effect that results in a positive correlation of market orders, which is driven by adverse selection, then by order splitting strategies and by imitative strategies of uninformed traders to a lesser extent.

show abstract

Why do carbon prices and price volatility change?

Cited by 34 publications

References 46 publications

Carbon price volatility: The case of China

Carbon price volatility: The case of China

Volatility Spillover and Dynamic Correlation Between the Carbon Market and Energy Markets

Limit order books, uninformed traders and commodity derivatives: Insights from the European carbon futures

Contact Info

Product

Resources

About