Modelling the Time Series Dynamics of Carbon Emission Markets

Carbon emission markets, which are designed to reduce emissions of global greenhouse gases (GHGs), have experienced rapid ongoing development even during the recent recession and have attracted considerable attention from policy makers and investors. Therefore, it is important to understand the time series dynamics of carbon asset prices and the behaviour of trading activities in carbon emission markets. This thesis, using the second commitment period data of the European Union emission trading scheme (EU ETS), examines the underlying dynamics driving carbon emission markets, including the performance of state dependent hedge ratios, the impact of arbitrage opportunities on feedback trading activities, as well as the influence of carbon allowance submission deadlines on the relationship between carbon spot and futures markets. The research models the relationship between carbon spot and futures markets by incorporating state dependent characteristics into the return and volatility processes, and finds that the class of regime switching hedging strategies, particularly the proposed new framework which combines regime switching behaviour and disequilibrium adjustment in the mean with state dependent dynamic volatility process, significantly outperform competing methods for all the measures considered, and for both in-sample and out-of-sample analysis. The results indicate that risk managers using Markov regime switching models to hedge the risk in carbon markets achieve greater variance reduction and better hedging performance. Secondly, this study extends Sentana and Wadhwani’s (1992) feedback trading model by allowing arbitrage opportunities to affect the demand of feedback traders in carbon markets. The results suggest that there is no evidence of feedback trading in the carbon market, where institutional investors dominate, although the effect persists in a few other energy markets. This finding supports the view that institutional investors are not necessarily all feedback traders. Thirdly, when examining the influence of the carbon allowance submission deadline on the time series dynamics of carbon spot and futures markets, it is found that the equilibrium level, mean-reverting speed and no-arbitrage boundaries are affected by the submission deadline. However, the submission of allowances does not change the price discovery process of carbon emission markets, where this thesis finds that both the spot and futures markets Granger-cause each other. Furthermore, there is evidence that the volatility spillover process is different before and after the submission deadline, particularly from the spot market to the futures market. Therefore, in modelling the relationship between carbon spot and futures prices, the difference in the mean-reverting process of futures mispricing before and after the submission deadline should be accounted for. Overall, the thesis finds that the carbon emission markets yield different time series characteristics and trading behaviours from other financial markets. The findings of this thesis are of interest to risk managers, investors and arbitragers operating in the carbon emission market and could aid regulators in improving the mechanisms of the EU ETS in the next commitment period

Visibility-Aware Pixelwise View Selection for Multi-View Stereo Matching

The performance of PatchMatch-based multi-view stereo algorithms depends heavily on the source views selected for computing matching costs. Instead of modeling the visibility of different views, most existing approaches handle occlusions in an ad-hoc manner. To address this issue, we propose a novel visibility-guided pixelwise view selection scheme in this paper. It progressively refines the set of source views to be used for each pixel in the reference view based on visibility information provided by already validated solutions. In addition, the Artificial Multi-Bee Colony (AMBC) algorithm is employed to search for optimal solutions for different pixels in parallel. Inter-colony communication is performed both within the same image and among different images. Fitness rewards are added to validated and propagated solutions, effectively enforcing the smoothness of neighboring pixels and allowing better handling of textureless areas. Experimental results on the DTU dataset show our method achieves state-of-the-art performance among non-learning-based methods and retrieves more details in occluded and low-textured regions.Comment: 8 page

Опыт постижения истории, как духовного развития человечества

В статье рассматриваются методологические основы формирования концепции истории духовного развития человечества. Делается вывод об ограниченности монистического подхода к анализу истории становления человеческого духа, как материалистического, так и идеалистического. Рассматриваются возможности применения дуалистического подхода, основанного на принципах единства духовного и материального (дух вне материи не существует, материя вне духа бессмысленна), раскрытия их взаимодействия в глобальных противоречиях эпохи и снятия их в процессе цивилизованного переустройства мира. Рассматриваются гуманистические аспекты цивилизации, как наивысшей формы "культурной общности", "способа существования человеческого разума во Вселенной", раскрытия и обретения свободы в преобразовании мира. На основе продвижения человечества от космогенной – к техногенной, и от неё – антропогенной цивилизации, выделяется типы традиционной, инновационной и либеральной духовности. Делается вывод о том, что кризис либерального типа духовности предполагает формирование интеллектуально-нравственного типа духовности, как духовности современного технотронного общества

3D indoor scene modeling from RGB-D data: a survey

3D scene modeling has long been a fundamental problem in computer graphics and computer vision. With the popularity of consumer-level RGB-D cameras, there is a growing interest in digitizing real-world indoor 3D scenes. However, modeling indoor 3D scenes remains a challenging problem because of the complex structure of interior objects and poor quality of RGB-D data acquired by consumer-level sensors. Various methods have been proposed to tackle these challenges. In this survey, we provide an overview of recent advances in indoor scene modeling techniques, as well as public datasets and code libraries which can facilitate experiments and evaluation

Whole-Body Lesion Segmentation in 18F-FDG PET/CT

There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers. Recent advances in the medical image segmentation shows the nnUNET is feasible for diverse tasks. However, lesion segmentation in the PET images is not straightforward, because lesion and physiological uptake has similar distribution patterns. The Distinction of them requires extra structural information in the CT images. The present paper introduces a nnUNet based method for the lesion segmentation task. The proposed model is designed on the basis of the joint 2D and 3D nnUNET architecture to predict lesions across the whole body. It allows for automated segmentation of potential lesions. We evaluate the proposed method in the context of AutoPet Challenge, which measures the lesion segmentation performance in the metrics of dice score, false-positive volume and false-negative volume

The impacts of non-fossil energy, economic growth, energy consumption, and oil price on carbon intensity: evidence from a panel quantile regression analysis of EU 28

This study investigates some determinants of carbon intensity in 28 countries in the European Union (EU), including non-fossil energy, economic growth, energy consumption, and oil price. A panel quantile regression method, which considers both individual heterogeneity and distributional heterogeneity, is applied in this paper. The empirical results imply that the influences of these determinants on carbon intensity are heterogeneous and asymmetric across different quantiles. Specifically, non-fossil energy can significantly decrease carbon intensity, but shows a U-shaped relationship. Economic growth has a negative impact on carbon intensity, especially for medium-emission and high-emission countries. The effects of heating degree days on carbon intensity are positive, although the coefficients are not significant at low quantiles, they become significant from medium quantiles. Besides, we find an inverse U-shaped relationship between crude oil price and carbon intensity. Finally, several relevant policy recommendations are proposed based on the empirical results