Analysing the potential risk with respect to CERs due to the price spread between EUAs and CERs

Transcription

1 Analysing the potential risk with respect to CERs due to the price spread between EUAs and CERs Fatemeh (MARJAN) Nazifi Research seminar The Behavior of Carbon Prices January 27, 2012 Paris, FRANCE 1

2 Outline: Introduction Econometric models Empirical findings Conclusions 2

3 Purpose of the paper: After showing that the prices of EUA and CER do not converge by using econometric evidence from the EU ETS and the CDM, the contribution of this paper is to analyse the process that describes the dynamic evolution of the price spread through detecting changes in the structural relationship. 3

4 Questions addressed: 1. Do EUA and CER prices converge over time? 2. Which factors may impact on the dynamic behaviour of the price spread over the period investigated? 4

5 Significance of the study: Focus of previous studies was mainly on economic impacts of the CDM, based on simulation analyses (Klepper & Peterson, 2006; Criqui and Kitous, 2003; Anger et al., 2007, Anger, 2008; Nazifi, 2009; Chevallier, 2010) A few studies focused on the price spread between the EUA and the CER (Mizrach, 2009; Mansanet-Bataller et al. 2011; Barrieu and Fehr, 2011) In contrast to previous works, this study assesses the strength of price convergence, and models the price spread by taking into account the possibility of a dynamic structural change through employing a timevarying parameter analysis. It is a pioneer study on the time-varying analysis in the area of carbon market. For the first time, it investigates the role of uncertainty surrounding CERs with respect to the default risk of financial institutions who guaranteed secondary CERs in forming the price spread. 5

6 Figure 1: The price spread between EUA and CER prices EUA Dec'09 CER Dec'09 Spread Dec' mars-08 mai-08 juil.-08 sept.-08 nov.-08 janv.-09 mars-09 mai-09 juil.-09 sept.-09 nov.-09 6

7 Methods of investigation: Time-series econometric techniques Unit-root tests Cointegration tests Convergence tests The Kalman filter analysis 7

8 Stage 1 Empirical findings: Table 1: Cointegration tests Null hypothesis Test value P-value Critical value 90% 95% r = r = Notes: r denotes the cointegrating rank of the system. Tests are performed with optimal number of lags chosen by Akaike Information Criterion and Final Prediction Error. The results of the Johansen trace tests are similar and in favour of no cointegration among series with different number of lags. 8

9 Stage1 Empirical findings: log H1 1 Ht 2 log L( t) = log ( 14.21) ( 10.71) t 9

10 The Kalman Filter: Deals with non-stationarity and non-cointegration in the data (Bomhoff, 1995; King and Cuc, 1996) Takes into consideration the possibility of a dynamic structural change in the prices series Avoids the necessity of having to break the data down into sub-periods (or considering shift dummies) to analyse the spread Computes the optimal estimates of the degree of price convergence for each period 10

11 Stage 2 The State Space Model: (3) 11

12 Figure 2: Time-varying path of β t Mar-08 Jun-08 Aug-08 Oct-08 Jan-09 Mar-09 Jun-09 Aug-09 Oct-09 Dec-09 SV2 ± 2 RMSE 12

13 Table 2: Kalman Filter estimations Variables Coefficients Gas ** Oil ** Coal ** TED spread *** Eurostoxx *** Gold * Financial Crisis ** Linking *** Phase III ** (Final State) α *** β *** Log-Lliklihood Akaike Information Criterion Schwarz Criterion Hannan-Quinn Criterion Note: * denotes statistical significance at 10% level, ** statistical significance at 5% level and *** statistical significance at 1% level. 13

14 Research results: Considering the results of all three time series techniques as a whole, no statistical evidence could be found that EUA and CER prices converged. Factors underlying the price spread are mainly: Different market frameworks, A cap on the amounts of CERs, Uncertainty associated with CERs, and Institutional events and regulatory news regarding both EUAs and CERs (e.g. concerns regarding the long-term future of the CDM; and the lack of clarity regarding the use of CERs) 14

15 Conclusion: Improving the competitive conditions of carbon markets, and increasing the substitutability of CERs for EUAs may develop the integration of the EU ETS and the secondary CER markets. Potential improvements may include providing greater: availability of CERs, clarity and simplicity of the EU s rules regarding the use of CER, harmonization in regulatory frameworks, and coordination between the EU ETS and the CDM 15

16 Thank you! Marjan Department of Economics Macquarie University, Sydney Australia Contact: 16