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The Carbon Market, Oil, and the Macroeconomy

Rittler, Daniel

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This thesis consists of five essays in the field of applied financial econometrics. The first part of the thesis contains chapters 1 and 2 and is concerned with the high-frequency price and volatility analysis in the European Union Emissions Trading Scheme. The second part contains chapters 3, 4, and 5 and investigates the relationship between energy markets, stock markets, and the macroeconomy.

The first chapter models the adjustment process of European Union Allowance prices to the releases of announcements at high-frequency controlling for intraday periodicity, volatility clustering and volatility persistence. The chapter reveals that the high-frequency EUA price dynamics are very well captured by a Fractionally Integrated Asymmetric Power GARCH process. The decisions of the European Commission on second National Allocation Plans have a strong and immediate impact on EUA prices. Further, EUA prices increase in response to better than expected news on the future economic development as well as the current economic activity in Germany and the U.S.

The second chapter models the relationship of European Union Allowance spot prices and futures prices within the second commitment period of the European Union Emissions Trading Scheme. Based on high-frequency data, the transmission of information in the first and second conditional moments is analyzed. To reveal long-run price discovery, common factor weights and information shares are computed. The chapter identifies that the futures market is the leader of the long-run price discovery process and that the informational role of this market increases over time. In addition to the price discovery analysis, a version of the unrestricted extended Constant Conditional Correlation-GARCH process is estimated to analyze the volatility transmission structure. The analysis reveals a close relationship between the volatility dynamics of both markets, where in particular spillovers from the futures to the spot market are observed. As a whole the investigation reveals that information is first incorporated in the futures market and then transferred to the spot market.

The third chapter empirically assesses whether the owners of companies covered by the EU-ETS are hit by the regulatory burden of the system. For this, a data set which contains companies of the sectors covered by the EU-ETS is analyzed. The chapter reveals that in the first commitment period the links between the carbon and the stock market are rather loose. For the second commitment period a strong impact of the carbon price on stock returns is found. Most importantly, the analysis reveals that sign and magnitude of the effect of the carbon price on stock returns reflect country- and sector-specific net compliance positions induced by heterogeneity in the allocation of emission allowances. Electricity stock returns are negatively affected by the carbon price, where in particular, the effect is most pronounced in countries with more restrictive emissions caps. On the other hand, non-electricity company stock returns are positively affected, where the effects are stronger in countries with more generous emissions caps. In conclusion, regulatory burden is shifted to owners of electricity companies, while owners of non-electricity companies seem to profit from the regulation.

The fourth chapter investigates the relationship between U.S. stock price returns and crude oil returns. For this, a Dynamic Conditional Correlation Mixed Data Sampling GARCH specification is developed to endogenize the long-term correlation between crude oil and stock price returns with respect to the stance of the U.S. macroeconomy. The chapter reveals that variables which contain information on current and future economic activity are helpful predictors for changes in the oil-stock correlation. For the period 1993-2011 there is strong evidence for a counter cyclical behavior of the long-term correlation. For prolonged periods with strong growth above trend the model predicts a negative long-term correlation, while before and during recessions the sign changes and remains positive throughout the economic recovery. The results strongly suggest that crude oil prices cannot be viewed as being exogenous with respect to the U.S. macroeconomy and explain the controversial results concerning the oil-stock relationship in the previous literature.

The fifth chapter empirically assesses the link between the U.S. stock market and the oil price in the period 2001-2011 given the theoretical framework of the ICAPM. For this, a three-step estimation strategy is applied, where dynamic conditional volatilities and correlations are estimated to compute conditional covariances between the individual stock returns, the market return, and the crude oil returns. Finally, individual stock returns are explained by the estimated covariances. The results indicate a positive relationship between the association of an asset and the market and the asset's expected return. Most importantly, the chapter reveals that the sensitivity of the investor's marginal utility of wealth with respect to the oil price is negative. This reflects that the investor is financially well off in periods of high oil prices which advocates the view that the oil price has predominantly been driven by shocks related to aggregate global demand during the analyzed sample period. It is shown that the investor demands for industry-specific stocks which are inversely related to the oil price and tend to pay off in periods of low oil prices and high marginal utility of wealth.

Item Type: Dissertation
Supervisor: Conrad, Prof. Dr. Christian
Date of thesis defense: 9 April 2013
Date Deposited: 16 May 2013 06:11
Date: 2013
Faculties / Institutes: The Faculty of Economics and Social Studies > Alfred-Weber-Institut for Economics
Subjects: 300 Social sciences
Controlled Keywords: Energiemarkt, Umweltökonomik, Ökonometrie, GARCH-Modell
Uncontrolled Keywords: Energy Market, Carbon Market, Oil Market, Stock Market, Applied Financial Econometrics, Applied Economics, High-Frequency Finance, Multivariate GARCH Models
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