Abstract
This chapter examines the relationship between liquidity and market efficiency in carbon markets, by using analysing trading data from the world’s largest carbon exchange, the ECX. Results obtained show that there is a strong relationship between liquidity and market efficiency such that when spreads narrow, return predictability diminishes. This relationship is more pronounced for the highest trading carbon financial instruments and during periods of low liquidity. Since the start of trading in Phase II of the EU-ETS prices have continuously moved nearer to unity with efficient, random walk benchmarks, and this improves from year to year. Overall, findings suggest that trading quality in the EU-ETS has improved markedly and matured over the sample period (2008–2011).
The study described in this chapter is based on Ibikunle et al. (2016). Please see bibliography for the full reference details.
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Notes
- 1.
Our approach is clearly different from the spot-futures relationship approach usually adopted for measuring futures market efficiency (e.g. see Kellard et al. 1999).
- 2.
Kalaitzoglou and Maher Ibrahim (2013) also examine informed trading in the EU-ETS by focusing on identifying the different agents at play in the carbon market.
- 3.
If we apply 5-minute intervals as the basis for the measures, we would be forced to examine only one contract per year for the period under consideration due to non-trading effects in the other contracts; hence, we use 15-minute intervals. In any case, we conduct a robustness analysis using only the highest volume contract, which is the nearest maturity contract, per year. We find that sampling at 5-minute intervals leaves our inferences unchanged.
- 4.
The delineation of the contracts analysed for each year, based on trading activity constraints, is as follows: for Year 2008, the December 2008, 2009 and 2010 maturities are used; for Year 2009, December 2009, 2010 and 2011 maturities are used; for Year 2010, December 2010, 2011 and 2012 maturities are used and the December 2011 and 2012 maturities are used for Year 2011.
- 5.
We also examine the possibility that our order imbalance measures may reflect exogenous shocks by computing absolute values for liquid and low liquidity periods as described in page 259 of Chordia et al. (2008) for robustness. As reported in Sect. 5.3, the results show that there is very low variation across the liquid and illiquid days; hence, the results are not affected by exogenous impact.
- 6.
We also use the mid-point of the last bid and ask transaction prices for every 15-minute period with similar outcomes.
- 7.
We also compute the measures with traded bid and ask prices at the stroke of each 15-minute period. The results are very similar with no material variation in values.
- 8.
Further, we employ the traded spread in all other sections of this chapter for robustness examinations. In all instances, the results yielded are not materially different from those yielded by our use of the relative spread. The results presented in this chapter are thus robust to substitute liquidity proxies.
- 9.
Given the period examined in this chapter, it is possible that the tapering off of the effects of the global financial crisis might have influenced liquidity and the enhancement of pricing efficiency during the Phase II of the EU-ETS. However, this consideration has no implication for the main focus of this study—the intraday links between return predictability and liquidity. This is because there is no basis to expect that illiquidity is jointly determined with signed order imbalances (see also Chordia et al. 2008).
- 10.
In Chap. 5, using the market model (Brown and Warner 1985), we examine calendar effects on the European Energy Exchange (EEX) carbon platform and find no significant effect. We nevertheless apply several dummy regressions to capture the effects of specific dates in further analyses. As discussed in Sect. 5.3, the dummy coefficients and respective t-statistics imply that the events have no intraday impact on our results.
- 11.
We also employ the (−60, +60) window as used by Chung and Hrazdil (2010a), with no substantial differences in the number of liquid and illiquid days. For robustness, we econometrically estimate effective half-spread for days in a randomly selected month in each year using the Huang and Stoll (1997) spread decomposition model. The distribution of liquid/illiquid days observed is not qualitatively dissimilar to the one we use above.
- 12.
In Year IV, the seven illiquid days have lower spread values than the liquid days. This underscores the limitations to our definition of illiquid days, which we control by using another window and seeing very small qualitative differences in the distribution of illiquid days. Suppose we have a period of low spreads leading to April (when compliance traders must submit emission permits) as a result of perceived increase in trading activity, and suppose most of our illiquid days are around this period. Then we are likely to obtain ‘illiquid’ days with lower average spread than the average of the vastly larger number of liquid days. This is plausible since our definition of illiquidity is relative to only a given number of surrounding days in the year for every illiquid day. Moreover, the differences in the values are so small that they are not statistically different from one another, even at the 10% level. The same case is made for the mean values in Year III.
- 13.
The percentage proportion of median relative spread on illiquid days to liquid days for Years I, II, III, and IV are 119, 125, 101, and 90%, respectively. These values (except for Year IV for which we have data for only 1/3 of the year) is consistent with the expectation that the average spread for low liquidity days will be higher than that of liquid days.
- 14.
We also carry out robustness tests by including dummies for specific day-after events such as the submission of annual emission reports, national allocation plans (NAP) and annual emission results announcements. We include dummies for days preceding specific holidays in the United Kingdom. The resultant coefficients show that the events are not significant to our investigations on intraday basis; also our earlier results are not materially altered.
- 15.
The structure of the VAR analysis in this section is different from that of the preceding analysis, where we aim to capture return predictability, which could only be adequately investigated on an intraday basis. Conducting the VAR analysis using daily frequency data following the aggregation of 15-minute interval data into daily measures should have no implication for the results other than to reduce the probability of establishing a statistical relationship. This is because a reduction in the degrees of freedom afforded by the use of daily aggregates of our liquidity and order imbalance measures should make it harder to establish a statistical relationship. Therefore, the results we obtain only serve to underscore the fact that past values of liquidity proxies do indeed help to explain variations in pricing.
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Ibikunle, G., Gregoriou, A. (2018). Liquidity and Market Efficiency in Carbon Markets. In: Carbon Markets. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-72847-6_6
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