Estimation and Performance Evaluation of Optimal Hedge Ratios in the Carbon Market of the European Union Emissions Trading Scheme

Abstract

Following the introduction of the European Union Emissions Trading Scheme, CO2 emissions have become a tradable commodity. As a regulated party, emitters are forced to take into account the additional carbon emissions costs in their production costs structure. Given the high volatility of carbon price, the importance of price risk management becomes unquestioned. This study is the first attempt to calculate hedge ratios and to investigate their hedging effectiveness in the EU-ETS carbon market by applying conventional and recently developed models of estimation. These hedge ratios are then compared with those derived for other markets. In spite of the uniqueness and novelty of the carbon market, the results of the study are consistent with those found in other markets -that the hedge ratio is in the range of 0.5 to 1.0 and still best estimated by simple regression models.

The influence of the COVID-19 pandemic on the hedging functionality of Chinese financial markets

“…Ignoring the dynamics of second moments of the return distribution in the estimation processes of the MV hedge ratios may lead to sub-optimal decisions, especially in periods of high basis volatility. Time-varying MV hedge ratios have been proposed as an alternative approach by assuming a bivariate generalised autoregressive conditional heteroscedasticity (BGARCH) model, which suggests adjustments of the hedge ratios regularly to capture updated market conditions 1 ( Kroner and Sultan, 1993 ; Bera et al, 1997 ; Brooks et al, 2002 ; Cotter and Hanly, 2006 ; Baillie et al, 2007 ; Park and Jei, 2010 ; Fan et al, 2014 ; Kim and Park, 2016 ). Some studies find that time-varying hedge ratios can beat the constant competitors given the former's higher effectiveness ( Baillie and Myers, 1991 ; Park and Switzer, 1995 ).…”

Do higher order moments of return distribution provide better decisions in minimum-variance hedging? Evidence from US stock index futures

“…The reason is that the derivation of the associated static OHRs ignores the dynamics between the cash and futures returns that are conditional on the past information set. Hence, one might expect the OHRs to be time varying, which has been extensively modelled in the minimum-variance (MV) hedging strategies using the multivariate generalised autoregressive conditionally heteroscedastic (MGARCH) family of models (see, for example, Baillie et al, 2007; Bera et al, 1997; Brooks et al, 2002; Fan et al, 2014; Kim and Park, 2016; Kroner and Sultan, 1993; Park and Jei, 2010). The derivation of the conditional OHRs depends significantly on the multivariate probability density functions (PDF) of return distributions, the logarithmic likelihood of which is maximised to obtain estimates of the MGARCH model.…”

Determinants of the perceived importance of organisational adaptation to climate change in the Australian energy industry

“…While the scientific findings suggest that it is of great importance that organisations include adaptation to climate change in their strategic plans, most research into organisational responses to climate change focuses on the cost of carbon and on mitigation; that is, how greenhouse gas emissions can be reduced (Fan et al, 2014; Grothmann and Patt, 2005). The topic of climate change is starting to gain a larger presence in the management literature and in organisational practice (Ansari et al, 2011; Goodall, 2008), but attention to adaptation as a response option is still limited.…”