| Summary: | Forestry activities play a crucial role in climate change mitigation. To make carbon credits generated from such activities a tradable commodity, it is important to analyze the price dynamics of carbon markets. This dissertation contains three essays that examine various issues confronting world’s carbon markets.
The first essay investigates cointegration of carbon markets using Johansen maximum likelihood procedure. All carbon markets of the world are not integrated. North American carbon markets show integration and so do the CDM markets. For future, the possibilities of arbitrage across world’s markets are expected to be limited, and carbon trading in these markets will be globally inefficient. There is a strong need of a global agreement that allows carbon trade to prevent climate change at the least cost options.
The second essay evaluates various econometric models for predicting price volatility in the carbon markets. Voluntary carbon market of Chicago is relatively more volatile; and like other financial markets, its volatility is forecasted best by a complex non-linear GARCH model. The compliance market of Europe, on the other hand, is less volatile and its volatility is forecasted best by simple econometric models like Historical Averages and GARCH and hence is different from other markets. Findings could be useful for investment decision making, and for making choice between various policy instruments.
The last essay focuses on agent based models that incorporate interactions of heterogeneous entities. Artificial carbon markets obtained from such models have statistical properties - lack of autocorrelations, volatility clustering, heavy tails, conditional heavy tails, and non-Gaussianity; which are similar to the actual carbon markets. These models possess considerably higher forecasting capabilities than the traditional econometric models. Forecast accuracy is further improved considerably through experimentation, when agent characteristics like wealth distribution, proportion of allowances and number of agents are set close to the real market situations.
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