This book is based on the insights gained from studies led by the authors between 2010 and 2015; a few of the studies are also extensively described in four of the chapters here. The focus of this book sits at the nexus of three interrelated fields of study: environmental policy , market microstructure and environmental financial economics . Specifically, three main issues (liquidity, price discovery and market efficiency) are investigated using data from the two major emissions permit trading venues within the European Union-Emissions Trading Scheme (EU-ETS) . These two venues, along with several others in Europe, constitute the largest regional market for emission permits (see Daskalakis et al. 2011 for detailed discussions; Chap. 2 also provides a descriptive analysis of the market).
According to OāHara (2003), organised markets fulfil two major functions. The first is the provision of liquidity , while the second is the provision of a mechanism for achieving price discovery . These two, although frequently overlooked by symmetric information-based asset pricing models, are vital to pricing assets of any kind, including emission permits , which are traded in carbon markets. Although distinct in certain respects, liquidity and price discovery are inextricably linked, both theoretically and empirically. In this book, we hold the view that a market is only informationally efficient to the extent to which its instruments or assets traded through it reflect all available information. This includes publicly available information and private information already used in trading, that is, if one limits the argument to the price adjustment process once a foundation price exists. If the instruments reflect all available information, such a market can be considered informationally efficient (Fama 1970). This implies that prices should only move based on innovation in beliefs (developed based on new information), and if prices were to move without supporting information arriving in the market, the market can be considered relatively less efficient.
Liquidity plays an important role in effecting the incorporation of new information into asset prices. OāHara (2003) makes an interesting analogy: consider a market with only sellers and no buyers on a particular day of the week; unless the sellers are willing to wait for the arrival of the buyers who are expected to arrive on a later day during the week, there will be no trades, hence no liquidity. Now, imagine that an unaligned agent decides to buy off instruments from the sellers on their day of arrival and keeps the instruments until the buyers arrive on a later day, then we have trades, and hence, liquidity. Liquidity is thus simply the process of connecting a buyer to a seller in as frictionless a manner as possible. A spread between the selling and buying prices naturally develops as a result of the service provided by the unaligned agent or middleman. The liquidity of this hypothetical market, based on the spread earned by the agent, is the transaction cost that ultimately affects asset pricing (e.g. Amihud 2002; Grossman and Miller 1988).
Information, liquidity and price discovery are therefore key issues in financial markets, including environmental markets such as the EU-ETS , which is used for trading carbon /carbon dioxide (CO2) emission permits . The smooth functioning of the EU-ETS can therefore not be evidenced without considering its performance based on the major market functions of liquidity , price discovery and related microstructure functions. This implies that the efficiency of the market relies on its liquidity and price discovery process. These issues are therefore interrelated in a well-functioning market. Evidence suggesting relatedness of these issues can be found in the market microstructure literature (e.g. see Hendershott et al. 2011). In a unique market, such as the EU-ETS, where trading arises as a result of governmental policy , it is important to understand how policy informs the evolution of the market microstructure. As this book aims to address the knowledge gap on the microstructure of the EU-ETS, in the following paragraphs, we review the microstructure literature for financial markets price discovery, price impact of block trades and liquidity by linking them together from the transaction costs perspective. We also link market efficiency to liquidity based on the current literature, since this connection affects transaction costs. We then extend the links from these strands of the market microstructure literature to the growing literature on the EU-ETS . We conclude this introductory section by providing a brief introduction of the empirical contributions contained in this book.
The explanation of liquidity given in the foregoing paragraph creates the impression of illiquidity as the premium paid by a buyer of an asset in a buyer-initiated trade or price concession by a seller in a seller-initiated trade . Thus, the larger the spread between the bid and offer prices the larger the cost of trade. The spread is a necessity reflecting the costs borne by the traders and the economic gain for the intermediary, or as normally referred to in the market microstructure literature, the market maker. Therefore, in quote-driven markets , the market maker quotes provide the basis for measuring transaction costs . The costs, however, are not entirely due to the need for immediacy or processing order costs; they arise as a result of inventory and adverse selection/information asymmetry as well (see Glosten and Milgrom 1985). When considering regular-sized trades, the spread is usually the only microstructure impact on prices; this explains why Brennan and Subrahmanyam (1996) measure illiquidity by price impact. Their measure is based on Kyleās (1985) model and is estimated analogously to Hasbrouck (1991a) and Foster and Viswanathan (1993). They initially employ the Lee and Ready (1991) algorithm in classifying trades into signed trades and then simply estimate the slope coefficient of tick-by-tick price adjustments on signed order flow (size). Their analysis is valid based on several earlier theoretical works (e.g. see Easley and OāHara 1987; Kyle 1985) and at least an empirical study (see Glosten and Harris 1988), which suggest that the liquidity effects of transaction costs (especially asymmetric information) are captured by trade impacts. Liquidity effects thus play a role in the price discovery process. However, block (large) trades can potentially cause price shocks larger than what the spread components would have (see Chiyachantana et al. 2004; Kraus and Stoll 1972); hence, Brennan and Subrahmanyam (1996) control for earlier information based on order size and pri...