|
on Market Microstructure |
| By: | Sandrine Jacob Leal (CEREFIGE-ICN Business School (Nancy Metz) France); Mauro Napoletano (OFCE Sciences Po & SKEMA Business School) |
| Abstract: | We investigate the effects of different regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low- and high-frequency (HF) traders. We analyze the impact of the imposition of minimum resting times, of circuit breakers (both ex-post and ex-ante types), of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and duration of ash crashes. In the model, low- frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. In contrast, high-frequency traders activation is event-driven and depends on price fluctuations. In addition, high-frequency traders employ low-latency directional strategies that exploit market information and they can cancel their orders depending on expected profits. Monte-Carlo simulations reveal that reducing HF order cancellation, via minimum resting times or cancellation fees, or discouraging HFT via financial transaction taxes, reduces market volatility and the frequency of ash crashes. However, these policies also imply a longer duration of flash crashes. Furthermore, the introduction of an ex-ante circuit breaker markedly reduces price volatility and removes ash crashes. In contrast, ex-post circuit breakers do not affect market volatility and they increase the duration of flash crashes. Our results show that HFT-targeted policies face a trade-o between market stability and resilience. Policies that reduce volatility and the incidence of flash crashes also imply a reduced ability of the market to quickly recover from a crash. The dual role of HFT, as both a cause of the flash crash and a fundamental actor in the post-crash recovery underlies the above trade-off . |
| Keywords: | Hifgh frequency trading, Flash crashes, Regulatory policies, Agent based models, limit order book, Market volatility |
| JEL: | G12 G01 C63 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:fce:doctra:16012&r=mst |
| By: | Sandrine Jacob Leal; Mauro Napoletano |
| Abstract: | We investigate the effects of different regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low- and high-frequency (HF) traders. We analyze the impact of the imposition of minimum resting times, of circuit breakers (both ex-post and ex-ante types), of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and duration of flash crashes. In the model, low-frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. In contrast, high-frequency traders activation is event-driven and depends on price fluctuations. In addition, high-frequency traders employ low-latency directional strategies that exploit market information and they can cancel their orders depending on expected profits. Monte-Carlo simulations reveal that reducing HF order cancellation, via minimum resting times or cancellation fees, or discouraging HFT via financial transaction taxes, reduces market volatility and the frequency of flash crashes. However, these policies also imply a longer duration of flash crashes. Furthermore, the introduction of an ex-ante circuit breaker markedly reduces price volatility and removes flash crashes. In contrast, ex-post circuit breakers do not affect market volatility and they increase the duration of flash crashes. Our results show that HFT-targeted policies face a trade-off between market stability and resilience. Policies that reduce volatility and the incidence of flash crashes also imply a reduced ability of the market to quickly recover from a crash. The dual role of HFT, as both a cause of the flash crash and a fundamental actorin the post-crash recovery underlies the above trade-off. |
| Keywords: | High-frequency trading, Flash crashes, Regulatory policies, Agent-based models, Limit order book, Market volatility |
| Date: | 2016–12–04 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2016/15&r=mst |
| By: | Corey Garriott; Adrian Walton |
| Abstract: | In August 2012, the New York Stock Exchange launched the Retail Liquidity Program (RLP), a trading facility that enables participating organizations to quote dark limit orders executable only by retail traders. A Hasbrouck (1991) structural vector autoregression shows that the facility increased the information content of the order flow by distinguishing retail trades from relatively more informed trades. A differences-in-differences event study finds that the RLP launch impacted market quality. Stocks with substantial RLP activity experienced mildly improved relative bid-ask spreads, effective spreads, price impacts and return autocorrelations in both the RLP and non-RLP segments. |
| Keywords: | Financial markets, Financial system regulation and policies, Market structure and pricing |
| JEL: | G20 G14 L10 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:16-20&r=mst |
| By: | Sandrine Jacob Leal (Groupe de Recherche en Droit, Economie et Gestion); Mauro Napoletano (OFCE) |
| Abstract: | We investigate the effects of different regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low- and high-frequency (HF) traders. We analyze the impact of the imposition of minimum resting times, of circuit breakers (both ex-post and ex-ante types), of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and duration of ash crashes. In the model, low- frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. In contrast, high-frequency traders activation is event-driven and depends on price fluctuations. In addition, high-frequency traders employ low-latency directional strategies that exploit market information and they can cancel their orders depending on expected profits. Monte-Carlo simulations reveal that reducing HF order cancellation, via minimum resting times or cancellation fees, or discouraging HFT via financial transaction taxes, reduces market volatility and the frequency of ash crashes. However, these policies also imply a longer duration of flash crashes. Furthermore, the introduction of an ex-ante circuit breaker markedly reduces price volatility and removes ash crashes. In contrast, ex-post circuit breakers do not affect market volatility and they increase the duration of flash crashes. Our results show that HFT-targeted policies face a trade-o between market stability and resilience. Policies that reduce volatility and the incidence of flash crashes also imply a reduced ability of the market to quickly recover from a crash. The dual role of HFT, as both a cause of the flash crash and a fundamental actor in the post-crash recovery underlies the above trade-off . |
| Keywords: | High frequency trade; Flash crashes; Regulatory policies; Agent-based models; Limit order book; Market volatility |
| JEL: | G12 G01 C63 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/3utlh0ehcn860pus6p2p683ade&r=mst |
| By: | Kyle, Albert S.; Obizhaeva, Anna A.; Tuzun, Tugkan |
| Abstract: | This paper studies invariance relationships in tick-by-tick transaction data in the U.S. stock market. Over the 1993–2001 period, the estimated monthly regression coefficients of the log of trade arrival rate on the log of trading activity have an almost constant value of 0.666, strikingly close to the value of 2/3 predicted by the invariance hypothesis. Over the 2001–14 period, the estimated coefficients rise, and their average value is equal to 0.79, suggesting that the reduction in tick size in 2001 and the subsequent increase in algorithmic trading resulted in a more intense order shredding in more liquid stocks. The distributions of trade sizes, adjusted for differences in trading activity, resemble a log-normal before 2001; there is clearly visible truncation at the round-lot boundary and clustering of trades at even levels. These distributions change dramatically over the 2001–14 period with their means shifting downward. The invariance hypothesis explains about 88 percent of the cross-sectional variation in trade arrival rates and average trade sizes; additional explanatory variables include the invariance-implied measure of effective price volatility. |
| Keywords: | market microstructure ; transactions data ; market frictions ; trade size ; tick size ; order shredding ; clustering ; TAQ data |
| JEL: | G10 G23 |
| Date: | 2016–04–19 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2016-34&r=mst |
| By: | Elena Andreou |
| Abstract: | Many empirical studies link mixed data frequency variables such as low frequency macroeconomic or financial variables with high frequency financial indicators’ volatilities, especially within a predictive regression model context. The objective of this paper is threefold: First, we relate the standard Least Squares (LS) regression model with high frequency volatility predictors, with the corresponding Mixed Data Sampling Nonlinear LS (MIDAS-NLS) regression model (Ghysels et al., 2005, 2006), and evaluate the properties of the regression estimators of these models. We also consider alternative high frequency volatility measures as well as various continuous time models using their corresponding relevant higher-order moments to further analyze the properties of these estimators. Second, we derive the relative MSE efficiency of the slope estimator in the standard LS and MIDAS regressions, we provide conditions for relative efficiency and present the numerical results for different continuous time models. Third, we extend the analysis of the bias of the slope estimator in standard LS regressions with alternative realized measures of risk such as the Realized Covariance, Realized Beta and the Realized Skewness when the true DGP is a MIDAS model. |
| Keywords: | MIDAS regression model, high-frequency volatility estimators, bias, efficiency. |
| JEL: | C22 C53 G22 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:ucy:cypeua:03-2016&r=mst |
| By: | Gomber, Peter |
| Abstract: | This paper describes cash equity markets in Germany and their evolution against the background of technological and regulatory transformation. The development of these secondary markets in the largest economy in Europe is first briefly outlined from a historical perspective. This serves as the basis for the description of the most important trading system for German equities, the Xetra trading system of Deutsche Börse AG. Then, the most important regulatory change for European and German equity markets in the last ten years is illustrated: the introduction of the Markets in Financial Instruments Directive (MiFID) in 2007. Its implications on equity trading in Germany are analyzed against the background of the current status of competition in Europe. Recent developments in European equity markets like the emergence of dark pools and algorithmic / high frequency trading are portrayed, before an outlook on new regulations (MiFID II, MiFIR) that will likely come into force in early 2018 will close the paper. |
| Keywords: | MiFID II,MiFIR,equity trading,electronic trading,cash equity markets |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewh:34&r=mst |
| By: | Alasdair Brown (University of East Anglia); Dooruj Rambaccussing (University of Dundee); J. James Reade (University of Reading); Giambattista Rossi (Birkbeck, University of London) |
| Abstract: | Information extracted from social media has been used by academics, and increasingly by practitioners, to predict stock returns. But to what extent does social media output predict asset fundamentals, and not simply short-term returns? In this paper we analyse 13.8m posts on Twitter, and high-frequency betting data from Betfair, concerning English Premier League soccer matches in 2013/14. Crucially, asset fundamentals are revealed at the end of play. We find that the Tweets of certain journalists, and the tone of all Tweets, contain fundamental information not revealed in betting prices. In particular, Tweets aid in the interpretation of news during matches. |
| Keywords: | social media; prediction markets; fundamentals; sentiment; mispricing |
| JEL: | G14 G17 |
| Date: | 2016–02 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2016-002&r=mst |