Identifying Drivers of Liquidity in the NBP Month-ahead Market

The liberalization of the natural gas market in the European Union has created incentives for the development of hubs and gas-to-gas competition. With increasing spot trading, there has been a progressive shift in pricing mechanisms from the traditional oil-linked pricing towards hub-linked pricing. In 2014, the share of gas traded that was indexed to hubs reached 61%, which can be compared to 15% in 2005 and 36% in 2010 (IGU, 2015). In addition, as energy companies respond to their exposure to the spot market, a greater use of forward contracts is made. In this context, liquidity, which can be described as the ability to match buyers and sellers at the lowest transaction cost (O’Hara, 1995) is of particular interest for researchers, participants and policy makers. Liquidity affects the cost of hedging and investment decisions and is a barometer of market quality. The importance of liquidity is reflected in a vast literature that focuses on different classes of financial assets such as stocks and bonds (e.g. Chordia et al. 2000, 2005) or foreign exchanges (e.g. Bessembinder, 1994; Danielsson and Payne, 2012). There are reports that liquidity may impact other measures of market quality, as for example, price volatility (e.g. Subrahmanyam, 1994) and trading activity (e.g. Chordia et al., 2002). Nevertheless, when academic studies of energy markets are considered, to date liquidity appears to have been neglected. In the present study, what may drive liquidity in the European natural gas market is considered. The National Balancing Point (NBP) forward market, which is the largest in Europe, is used as a proxy for the European natural gas forward market. The link between liquidity, trading activity and volatility is examined within the period 2010-2014, which includes when the EU Regulation on Market Integrity and Transparency (REMIT) came into effect. Therefore, the question of whether changes in link between these measures of market quality could reflect REMIT is also investigated. A unique dataset consisting of tick-by-tick indicative quotes (best ask and best ask) and transaction prices and volumes, obtained from the inter-deal broker Tullett Prebon http://www.tpinformation.com) is used. The data correspond to approximately a third of the NBP month-ahead forward market in the period studied. In order to account for the discrete and irregular nature of the data, cleaning and resampling procedures based on Brownlees and Gallo (2006) and Barndorff-Nielsen et al. (2009) is adopted. The focus is on the trading time interval 7:00-17:00, and observations outside this interval are discarded, as well as entries with negative spreads. Simultaneous quotes and transaction prices are aggregated using their respective medians, volumes and transaction counts are aggregated by using their respective totals. Outliers are detected. In all, approximately 2% of the observations are discarded. The data are then resampled at regular time intervals of 60 minutes leading to a sample size of 12,870 observations. Furthermore, the expected effect of the yearly seasonality of the demand for natural gas on the link between these measures is also addressed, and, in the spirit of Chordia et al. (2005), adjustment regressions are performed on the raw series. A vector autoregressive (VAR) model is adopted to investigate the link between liquidity, volatility and trading activity in the NBP month-ahead forward market. This approach has been used by Chordia et al. (2005), to investigate the correlation between liquidity, volatility, returns and order imbalance in the stock market, and Danielsson & Payne (2012), to analyze the correlation between volume, volatility and spread in the foreign exchange markets. Compared to previous research, a time-varying approach is used in this study, so that any change in the links between the market quality measures can be assessed. Parameters are estimated over rolling windows of fixed size (252 days, m=2,268) through the sample in order to evaluate their stability. Consistent with microstructure theory on asymmetric information (Glosten & Milgrom, 1985, Kyle, 1985; Easley & O'Hara, 1987), we find a positive correlation between trading activity and volatility plus a negative correlation between volatility and subsequent liquidity. The analysis suggests, however, these correlations are time-varying and follow changes in the fundamental values of demand, supply and inventory. 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