Carbon prices. Dynamic analysis of European and Californian markets

Dissertação para obtenção do Grau de Doutor em Alterações Climáticas e Políticas de Desenvolvimento SustentávelCarbon markets’ goal is to promote the reduction of emissions of greenhouse gases where it is most cost-efficient. This makes the price of the tradable good – carbon dioxide equivalent (CO2e) - a key variable in management and risk decisions, in markets related to activities connected with the burning of fossil fuels, such as power generation. This work aims to improve the analysis of carbon prices’ dynamics, considering the possibility of multidirectional effects between prices of CO2e, energy (primary and final), offsets licenses and the economy performance, in various frequencies. The two main research questions are: (i) what drives carbon price variations? (ii) what variations do carbon prices drive? We used two comple-mentary methodologies: (a) a vector autoregression model (of common use in macroeconomics and financial markets but not in carbon-energy relations), which allows the analysis of causality and of impulse-response functions of daily prices; and (b) an innovative multivariate wavelet analysis, which allows us to understand the relationship and causal link between the variables in the time and frequency dimensions, particularly in longer cycles (4~8 and 8~20 months), not perceived in previous studies. As case studies we considered the European (EU ETS) and Califor-nia (AB32) carbon markets. This is the first research to present the analysis of the referred US market. The analysis covers the 2008-2013 period, intentionally excluding the EU ETS phase I, for greater consistency of results. Results suggest that the economy and electricity drive the price of European carbon, while gas and oil have a greater role in California. So, there is a greater influence of final energy prices in the most mature market. We also observe that the price of CERs does not affect the European carbon price. On the other hand, this study shows for the first time that carbon prices have impacts on electricity prices over longer cycles (8~20 months) and in coal over short cycles (lim-ited to the first days). It is suggested that the carbon market has more significant effects in longer cycles. The price of European carbon also has impact in CERs prices. The results are statistically significant and relevant, and will improve the quality of decision making of all parties involved in the energy and carbon markets - polluters and regulators included

An Analysis of Temporal and Spectral Connectedness and Spillover in Commodity Markets

PhD thesis in Risk management and societal safetyThis thesis is concerned with evaluating the temporal and spectral connectedness and spillover dynamics of commodity prices. The industries of interest are crude oil, agricultural commodities, aquaculture, and Norwegian salmon as the primary datasets. World agricultural and energy commodity indexes as well as the aquaculture sector and salmon price index have experienced exceptionally volatile periods throughout the last decade. Therefore, the objective of this thesis is to detect and quantify the temporal and spectral connectedness and spillover dynamics in the prices of these assets. This thesis falls in line with a large collection of research papers evaluating the dynamics of commodity markets. More specifically, the first two papers examine the connectedness structure between crude oil and agricultural commodities and between various aquaculture species by utilizing wavelet-based copula approach. By combining the methodologies from physics and econometrics, we evaluate how the dependence structures among the underlying assets varies across di˙erent frequencies and in the tails of the distributions. The third paper evaluates the static and temporal return and volatility spillover dynamics between crude oil and agricultural commodities. The last paper examines the firm-level cointegration relation and return spillover dynamics between Fish Pool Index (FPI) and major salmon producers. Incorporating methodologies from physics, economics, and finance is relevant when examining spectral relationship and providing an alternative angle to examine the commodity markets. The findings of this thesis indicate that the connectedness between oil and agricultural commodities increased during post-2006 across all considered frequencies of return movements. Specifically, the wavelet decomposition reveal that the interconnectedness structure is negative during the pre-2006, but it turns positive over the post-2006 subsample. Furthermore, the findings indicate persistence in dependence variation is higher over the long-run return movements. In terms of spillover analysis, the findings indicate minuscule information transmission between crude oil and agricultural commodities over the pre-2006 subsample, but crude oil tends to be a net receiver of volatility over the post-2006 subsample. Furthermore, we report asymmetric and bidirectional information transmission between crude oil and agriculture during periods of financial and economic turmoil. In terms of connectedness in di˙erent aquaculture species, the findings indicate limited dependence in the short-run horizon, however, the price linkage among various species significantly increased over the medium- and long-run horizon, suggesting market integration over the long-run. In regard to cointegration and spillover among FPI and major salmon producers, we report that the prices of exchange traded salmon stocks reflect the flow of salmon market information earlier than the price index. Furthermore, our findings indicate that the FPI and small producers are net receiver of spillover from major salmon producers

Uncovering hidden information and relations in time series data with wavelet analysis: three case studies in finance

This thesis aims to provide new insights into the importance of decomposing aggregate time series data using the Maximum Overlap Discrete Wavelet Transform. In particular, the analysis throughout this thesis involves decomposing aggregate financial time series data at hand into approximation (low-frequency) and detail (high-frequency) components. Following this, information and hidden relations can be extracted for different investment horizons, as matched with the detail components. The first study examines the ability of different GARCH models to forecast stock return volatility in eight international stock markets. The results demonstrate that de-noising the returns improves the accuracy of volatility forecasts regardless of the statistical test employed. After de-noising, the asymmetric GARCH approach tends to be preferred, although that result is not universal. Furthermore, wavelet de-noising is found to be more important at the key 99% Value-at-Risk level compared to the 95% level. The second study examines the impact of fourteen macroeconomic news announcements on the stock and bond return dynamic correlation in the U.S. from the day of the announcement up to sixteen days afterwards. Results conducted over the full sample offer very little evidence that macroeconomic news announcements affect the stock-bond return dynamic correlation. However, after controlling for the financial crisis of 2007-2008 several announcements become significant both on the announcement day and afterwards. Furthermore, the study observes that news released early in the day, i.e. before 12 pm, and in the first half of the month, exhibit a slower effect on the dynamic correlation than those released later in the month or later in the day. While several announcements exhibit significance in the 2008 crisis period, only CPI and Housing Starts show significant and consistent effects on the correlation outside the 2001, 2008 and 2011 crises periods. The final study investigates whether recent returns and the time-scaled return can predict the subsequent trading in ten stock markets. The study finds little evidence that recent returns do predict the subsequent trading, though this predictability is observed more over the long-run horizon. The study also finds a statistical relation between trading and return over the long-time investment horizons of [8-16] and [16-32] day periods. Yet, this relation is mostly a negative one, only being positive for developing countries. It also tends to be economically stronger during bull-periods