EVALUATING EMISSION TRADING AS A POLICY TOOL–EVIDENCE FROM CONDITIONAL JUMP MODELS

Marc Gronwald, ifo Institute for Economic Research, Phone +49 89 9224 1400, e-mail:

Janina Ketterer, ifo Institute for Economic Research, Phone +49 89 9224 1433, e-mail:

Overview

This paper's contribution to the literature is twofold. First, it applies Chan and Maheu's (2002) auto-regressive jump-intensity (ARJI)-GARCH model to Phase I emission allowance prices and second, evaluates emission trading as a policy tool. Jump models, in general, have proven to be a useful tool for capturing sudden price movements due to the occurrence of unexpected news. The distinct feature of ARJI-models is that they allow the jumps to occur at differing size and frequency. Empirically analyzing emission allowance (EUA) prices receives growing attention in the literature. On the one hand, Daskalakis et al. (2005) findthat the EUA future is characterized by jumps. On the other hand, GARCHstructure in the carbon price returns is found by Paolella and Taschini (2008) as well as Benz and Trück (2009) who choose an AR-GARCH model. However, jumps and conditional heteroscedasticity has not yet been treated in asingle approach as brought forward in this paper.

The impact of emission trading on the abatement strategy of European firmsis evaluated from the real option perspective, which states that there is aninverse relationship between uncertainty and investment. Especially in thecase of an irreversible and industry-specific investment, a firm chooses to delay the expenditure if the future cash show is uncertain [Dixit and Pindyck,1994]. The real option approach has also been applied to topics in resourceand environmental economics, dealing with the effect of uncertainty on the timing of policy actions [Pindyck, 2000; Pindyck, 2002]. Under the new European Emission Trading Scheme (ETS) firms face a fundamental decision. Either they acquire sufficient certificates in the market or they reduce thecarbon emissions they generate by investing in abatement technologies. Byinvesting in a cleaner and more efficient production process, enterprises save costs of future certificates and energy, but also spend on irreversible projects.According to real option literature, an uncertain outlook affects the firm'sabatement decision. This paper argues that the peculiar behaviour of certificate prices introduces additional uncertainty.

What is more, the evidence of volatile EUA prices has not been discussedwith respect to the implication on abatement decisions under the ETS.

The paper is organized as follows: After the Chan and Maheu (2002) method is outlined, the empirical results are presented and discussed.

Methods

Auto-regressive Jump Intensity GARCH models proposed by Chan and Maheu (2002).

Results

Strong evidence of conditional jump-intensity in daily EUA pricechanges is found, indicated by highly significant jump coefficients.

Conclusions

The real option literature is most relevant as it investigates the effect of uncertainty on economic decision making. Dixitand Pindyck(1994) emphasize the need to consider a real option approachwhich originates from the finance literature. Under uncertainty, a firm's decision when to invest changes, as the option to wait for additional informationecomes more attractive. Uncertainty makes future profits hard to calculateand can for example arise from prices [Pindyck, 1981] or from future demand[Pindyck, 1993]. Theory predicts a negative relationship between the waitingoption and an irreversible investment.

Drawing from this literature, the evidence presented in this paper contributed to the evaluation of climate change policies. EUA prices that exhibitjump behaviour make calculation of compliance costs more difficult. Facingthis uncertainty, firms will become more hesitant about investments andemission-reducing retrofits will be realized later in time. This effect is oftenneglected when assessing emission trading against other environmental policies.

On the one hand, the evidence of jumps shows that information frequently surprisesmarket participants. Unexpected news on excess supply or national allocation decisions led to a peculiar price behaviour. Certainly, Phase I was planned as test period, introducing regulators as well as firms to the newlyinstalled mechanism. Policy makers have to learn from the Phase I eventsand try to improve the system. Already, the European Commission decidedon the absolute number of certificates until 2020 (1.72 billion tons CO2) andthe rate at which this cap will be lowered from 2013 onwards.

On the other hand, evidence of jumps is found throughout the estimation period - with time-varying intensity. Therefore, a considerable degree of uncertaintyseems to be inherent in the system and not only related to few events. Possible sources may be the impact of increased EUA auctioning, the nationallimits on CDM and JI credits which can be used for compliance to some extent.

This paper aims to evaluate different policy measures and their performance with regard to the reduction of carbon emissions. Evidence is foundthat investments might be postponed under the ETS. According to Sinn

(2008), later abatement of carbon emissions leads to higher atmospheric carbon concentration and therefore accelerates climate change.

References

Chan, W.H. and J.M.Maheu (2002), “Conditional Jump Dynamics in Stock Market Returns”, Journal of Business & Economic Statistics 20: 377-389

Dixit, A.K., R.S. Pindyck (1994). Investment under Uncertainty, PrincetonUniversity Press, Princeton

Pindyck, R.S. (2000). Irreversibilities and the Timing of Environmental Policy, Resource and Energy Economics 22: 233-259

Pindyck, R.S. (2002). Optimal Timing Problems in Environmental Economics, Journal of Economic Dynamics and Control 26: 1677-1697

Sinn, H.W. (2008), “Public Policies against Global Warming: a Supply Side Approach”, International Tax and Public Finance 15, 360-394