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on Market Microstructure |
| By: | Barardehi, Yashar H. (Argyros School of Business & Economics, Chapman University); Bernhardt, Dan (University of Illinois & University of Warwick); Ruchti, Thomas G. (Tepper School of Business, Carnegie Mellon University); Weidenmier, Marc (Argyros School of Business & Economics, Chapman University and NBER.) |
| Abstract: | Amihud’s (2002) stock (il)liquidity measure averages the daily ratio of absolute closeto-close return to dollar volume, including overnight returns, while trading volumes come from regular trading hours. Our modified measure addresses this mis-match by using open-to-close returns. It better explains cross-sections of returns, doubling estimated liquidity premia over 1964–2017. Using non-synchronous trading near close as an instrument reveals that overnight returns are primarily information-driven and orthogonal to price impacts of trading. Thus, including them in liquidity proxies magnifies measurement error, understating liquidity premia. Our modification especially matters when applications in finance and accounting render use of intraday data infeasible/undesirable. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:warwec:1211&r=all |
| By: | Matthew Baron (University of Warwick); Björn Hagströmer (Stockholm University); Andrei Kirilenko (Imperial College London) |
| Abstract: | We study performance and competition among high-frequency traders (HFTs). We construct measures of latency and find that differences in relative latency account for large differences in HFTs’ trading performance. HFTs that improve their latency rank due to colocation upgrades see improved trading performance. The stronger performance associated with speed comes through both the short-lived information channel and the risk management channel, and speed is useful for various strategies including market making and cross-market arbitrage. We find empirical support for many predictions regarding relative latency competition. |
| Keywords: | high-frequency trading, low latency, market microstructure |
| JEL: | G10 G19 |
| URL: | http://d.repec.org/n?u=RePEc:cth:wpaper:gru_2017_018&r=all |
| By: | Yin-Wong Cheung (City University of Hong Kong); Rasmus Fatum (University of Alberta); Yohei Yamamoto (Hitotsubashi University) |
| Abstract: | We explore whether the exchange rate effects of macro news are time- and state-dependent by analyzing and comparing the relative influence of US and Japanese macro news on the JPY/USD rate before, during, and after the Global Financial Crisis. A comprehensive set totaling 40 time-stamped US and Japanese news variables and preceding survey expectations along with 5-minute indicative JPY/USD quotes spanning the 1 January 1999 to 31 August 2016 period facilitate our analysis. Our results suggest that while US macro news are now more important than before the Crisis, the influence of Japanese macro news has waned to the point of near-irrelevance. These findings are of particular importance to exchange rate modeling of the New Era. |
| Keywords: | Foreign Exchange Rates; Macro News Surprises; Global Financial Crisis |
| JEL: | F31 G15 |
| URL: | http://d.repec.org/n?u=RePEc:cth:wpaper:gru_2017_007&r=all |
| By: | Johann, Thomas; Scharnowski, Stefan; Theissen, Erik; Westheide, Christian; Zimmermann, Lukas |
| Abstract: | This paper presents the most extensive analysis of liquidity in the German equity market so far. We examine the evolution of liquidity over time, the determinants of liquidity, and commonality across liquidity measures and countries. We make use of a new publicly available dataset, the Market Microstructure Database Xetra (MMDB-Xetra). We find that liquidity has generally increased over time, and that in times of crisis liquidity is lower and the volatility of liquidity is significantly higher. Commonality in liquidity is highest during the financial crisis. We also find significant commonality between liquidity in the US and the German equity markets. |
| Keywords: | Market Microstructure,Liquidity,Volatility,Germany,Xetra |
| JEL: | G10 G14 G15 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:1902&r=all |
| By: | Huiling Yuan; Yong Zhou; Zhiyuan Zhang; Xiangyu Cui |
| Abstract: | Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-It\^{o}-OI model, we assume that the option-implied volatility can influence the security's future volatility, and the option-implied volatility is treated as an observable exogenous variable. In GARCH-It\^{o}-IV model, we assume that the option-implied volatility can not influence the security's volatility directly, and the relationship between the option-implied volatility and the security's volatility is constructed to extract useful information of the underlying security. After providing the quasi-maximum likelihood estimators for the parameters and establishing their asymptotic properties, we also conduct a series of simulation analysis and empirical analysis to compare the proposed models with other popular models in the literature. We find that when the sampling interval of the high-frequency data is 5 minutes, the GARCH-It\^{o}-OI model and GARCH-It\^{o}-IV model has better forecasting performance than other models. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1907.02666&r=all |