European Gas Markets: Market Integration & Market Efficiency: A Network Perspective

Abstract

In this thesis, I study the market integration of European Natural Gas markets through two papers, whilst the third paper considers the impact of News Sentiment on the pricing and trading of Clean Energy and Traditional Energy stocks. Specifically, the first paper studies the level of harmonisation of European Natural Gas prices, characterised by 12 European gas hubs. The key finding is that, under normal market conditions, European Natural Gas markets are becoming increasingly integrated, with few physical barriers to increased market integration. Conversely, the detection of non-physical barriers to trade suggests that the liberalisation and development of certain national gas markets is yet to be fully achieved, inferring that improvements in technical arrangements are required.
The second paper provides a framework for forecasting the short term presence of phys- ical barriers to market integration of European Natural Gas markets. The identification of infrastructure congestion is an important prerequisite in enforcing price competition, and the implementation of an internal European gas market. In order to address this challenge, the underlying infrastructure network is learnt as a graph, and a deep learning framework, Graph Convolutional Long Short-Term Memory Neural Network (GC-LSTM), based on the topology of the infrastructure network, is applied to learn the interactions between different pipelines, and forecast gas flows throughout the network. Empirical results show that the GC-LSTM outperforms baseline methods in predicting gas pipeline flows.
The third paper studies the impact of News sentiment on pricing and trading for European Clean Energy companies and Traditional Energy companies. Using daily news extracted from Bloomberg, we estimate Vector Autoregressive (VAR) models and evaluate the dynamic spillover effects between News sentiment, stock returns and trading volumes. We find that European Clean Energy firms and Traditional Energy firms share the same patterns; that News sentiment positively affects both stock returns and trading volumes, and in return, stock returns and trading volumes have a limited impact on News sentiment. Nevertheless, the spillovers are relatively moderate and asymmetric.