nep-mst New Economics Papers
on Market Microstructure
Issue of 2017‒04‒09
four papers chosen by
Thanos Verousis
  1. Identifying Drivers of Liquidity in the NBP Month-ahead Market By Lilian de Menezes; Marianna Russo; Giovanni Urga
  2. Microstructure under the Microscope: Tools to Survive and Thrive in The Age of (Too Much) Information By Ravi Kashyap
  3. Incorporating Signals into Optimal Trading By Charles-Albert Lehalle; Eyal Neuman
  4. Do monetary policy announcements affect foreign exchange returns and volatility? Some evidence from high-frequency intra-day South African data. By Cyril May; Greg Farrell; Jannie Rossouw
  1. By: Lilian de Menezes; Marianna Russo; Giovanni Urga
    Abstract: 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. Overall, the present study suggests increases in market transparency and competition over time. These findings are reassuring for policy makers and regulators. Nonetheless, no significant differences in correlations appear to have followed the entering into force of REMIT. References Barndorff-Nielsen, O., Hansen, P., Lunde, A., & Shephard, N. (2009). Realised kernels in practice: Trades and quotes. Econometrics Journal, 12, C1-C32. Bessembinder, H. (1994). Bid-ask spreads in the interbank foreign exchange markets. Journal of Financial Economics, 35, 317-348. Brownlees, C. & Gallo, G. (2006). Financial econometric analysis at ultra-high frequency: Data handling concerns. Computational Statistics and Data Analysis, 51, 2232-2245. Chordia, T., Sarkar, A., & Subrahmanyam, A. (2005). An empirical analysis of stock and bond market liquidity. Review of Financial Studies 18, 85–129. Chordia, T., Roll, R., & Subrahmanyam, A. (2002). Order imbalance, liquidity, and market returns. Journal of Financial Economics, 65, 111-130. Chordia, T., Roll, R., & Subrahmanyam, A. (2000). Commonality in liquidity. Journal of Financial Economics, 56, 3-28. Danielsson, J. & Payne, R. (2012). Liquidity determination in an order driven market. The European Journal of Finance, 18, 799-821. Easley, D. & O'Hara, M. (1987). Price, trade size, and information in securities markets. Journal of Financial Economics, 19, 69-90. Glosten, L. & Milgrom, P. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics, 14, 71-100. IGU, (2014): International Gas Union, Whole Sale Gas Price Survey Report, June 2015. Available on 28 January 2016: http://www.igu.org/sites/default/files/node-page-field_file/IGU%20Whole%20Sale%20Gas%20Price%20Survey%20Report%20%202015%20Edition.pdf. Kyle, A. (1985). Continuous auctions and insider trading. Econometrica, 53, 1315-1335. Subrahmanyam, A. (1994). Circuit breakers and market volatility: A theoretical perspective. Journal of Finance, 49, 237-254.
    Keywords: United Kingdom, Energy and environmental policy, Macroeconometric modeling
    Date: 2016–07–04
    URL: http://d.repec.org/n?u=RePEc:ekd:009007:9570&r=mst
  2. By: Ravi Kashyap
    Abstract: Market Microstructure is the investigation of the process and protocols that govern the exchange of assets with the objective of reducing frictions that can impede the transfer. In financial markets, where there is an abundance of recorded information, this translates to the study of the dynamic relationships between observed variables, such as price, volume and spread, and hidden constituents, such as transaction costs and volatility, that hold sway over the efficient functioning of the system. "My dear, here we must process as much data as we can, just to stay in business. And if you wish to make a profit you must process at least twice as much data." - Red Queen to Alice in Hedge-Fund-Land. In this age of (Too Much) Information, it is imperative to uncover nuggets of knowledge (signal) from buckets of nonsense (noise). To aid in this effort to extract meaning from chaos and to gain a better understanding of the relationships between financial variables, we summarize the application of the theoretical results from (Kashyap 2016b) to microstructure studies. The central concept rests on a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions, and the Johnson Lindenstrauss Lemma, a technique for dimension reduction, providing us with a simple yet powerful tool that allows comparisons between data-sets representing any two distributions. We provide an empirical illustration using prices, volumes and volatilities across seven countries and three different continents. The degree to which different markets or sub groups of securities have different measures of their corresponding distributions tells us the extent to which they are different. This can aid investors looking for diversification or looking for more of the same thing.
    Date: 2017–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1703.08812&r=mst
  3. By: Charles-Albert Lehalle; Eyal Neuman
    Abstract: Optimal trading is a recent field of research which was initiated by Almgren, Chriss, Bertimas and Lo in the late 90's. Its main application is slicing large trading orders, in the interest of minimizing trading costs and potential perturbations of price dynamics due to liquidity shocks. The initial optimization frameworks were based on mean-variance minimization for the trading costs. In the past 15 years, finer modelling of price dynamics, more realistic control variables and different cost functionals were developed. The inclusion of signals (i.e. short term predictors of price dynamics) in optimal trading is a recent development and it is also the subject of this work. We incorporate a Markovian signal in the optimal trading framework which was initially proposed by Gatheral, Schied, and Slynko [20] and provide results on the existence and uniqueness of an optimal trading strategy. Moreover, we derive an explicit singular optimal strategy for the special case of an Ornstein-Uhlenbeck signal and an exponentially decaying transient market impact. The combination of a mean-reverting signal along with a market impact decay is of special interest, since they affect the short term price variations in opposite directions. Later, we show that in the asymptotic limit were the transient market impact becomes instantaneous, the optimal strategy becomes continuous. This result is compatible with the optimal trading framework which was proposed by Cartea and Jaimungal [10].
    Date: 2017–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1704.00847&r=mst
  4. By: Cyril May; Greg Farrell; Jannie Rossouw
    Abstract: This paper examines the temporal effect of domestic monetary policy surprises on both the levels and volatility of the South African rand/United States dollar exchange rate. The analysis in this ‘event study’ proceeds using intra-day minute-by-minute exchange rate data, repo rate data from the South African Reserve Bank’s scheduled monetary policy announcements, and Bloomberg market consensus repo rate forecasts. We find statistically and economically significant responses in intra-day high-frequency exchange rate returns and volatility to domestic interest rate surprises, but anticipated changes have no bearing on the rand. Our results suggest that monetary policy news is an important determinant of the exchange rate for approximately 5 to 40 minutes after the estimated time of the pronouncement – suggesting a relatively high degree of market ‘efficiency’ in its mechanical sense (and not ‘efficient’ market in the deeper economic-informational sense) in processing this information.
    Keywords: Exchange rate, expectations, monetary policy surprises, repo rate, returns, volatility
    JEL: C22 E52 E58 F31 F41 G14 G15
    Date: 2017–03
    URL: http://d.repec.org/n?u=RePEc:rza:wpaper:672&r=mst
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