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on Market Microstructure |
| By: | Bellia, Mario; Pelizzon, Loriana; Subrahmanyam, Marti; Uno, Jun; Yuferova, Darya |
| Abstract: | We examine the strategic behavior of High Frequency Traders (HFTs) during the pre-opening phase and the opening auction of the NYSE-Euronext Paris exchange. HFTs actively participate, and profitably extract information from the order flow. They also post "flash crash" orders, to gain time priority. They make profits on their last-second orders; however, so do others, suggesting that there is no speed advantage. HFTs lead price discovery, and neither harm nor improve liquidity. They "come early to the party", and enjoy it (make profits); however, they also help others enjoy the party (improve market quality) and do not have privileges (their speed advantage is not crucial). |
| Keywords: | High-Frequency Traders (HFTs),Proprietary Trading,Opening Auction,Liquidity Provision,Price Discovery |
| JEL: | G12 G14 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewp:182&r=mst |
| By: | Endres, Sylvia; Stübinger, Johannes |
| Abstract: | This study derives an optimal pairs trading strategy based on a Lévy-driven Ornstein-Uhlenbeck process and applies it to high-frequency data of the S&P 500 constituents from1998 to 2015. Our model provides optimal entry and exit signals by maximizing the expected return expressed in terms of the first-passage time of the spread process. An explicit representation of the strategy's objective function allows for direct optimization without Monte Carlo methods. Categorizing the data sample into 10 economic sectors, we depict both the performance of each sector and the efficiency of the strategy in general. Results from empirical back-testing show strong support for the profitability of the model with returns after transaction costs ranging from 31.90 percent p.a. for the sector \Consumer Staples" to 278.61 percent p.a. for the sector \Financials". We find that the remarkable returns across all economic sectors are strongly driven by model parameters and sector size. Jump intensity decreases over time with strong outliers in times of high market turmoils. The value-add of our Lévy-based model is demonstrated by benchmarking it with quantitative strategies based on Brownian motion-driven processes. |
| Keywords: | Finance,Pairs trading,Optimal thresholds,Ornstein-Uhlenbeck Lévy process,Mean-reversion,High-frequency data |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwqwdp:172017&r=mst |
| By: | Vincent van Kervel (Pontificia Universidad Católica de Chile); Albert J. Menkveld (VU University Amsterdam; Tinbergen Institute, The Netherlands) |
| Abstract: | Liquidity suppliers lean against the wind. We analyze whether high-frequency traders (HFTs) lean against large institutional orders that execute through a series of child orders. The alternative is HFTs trading "with the wind," that is, in the same direction. We find that HFTs initially lean against these orders but eventually change direction and take position in the same direction for the most informed institutional orders. Our empirical findings are consistent with investors trading strategically on their information. When deciding trade intensity, they seem to trade off higher speculative profit against higher risk of detection by HFTs and being preyed on. |
| Keywords: | High-frequency traders; institutional investors; trading patterns; transaction cost |
| JEL: | G10 G14 G15 |
| Date: | 2017–09–29 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20170092&r=mst |
| By: | Liu, Cheng (Economics and Management School of Wuhan University); Xia, Ningning (School of Statistics and Management, Shanghai University of Finance and Economics); Yu, Jun (School of Economics, Singapore Management University) |
| Abstract: | This paper examines the usefulness of high frequency data in estimating the covariance matrix for portfolio choice when the portfolio size is large. A computationally convenient nonlinear shrinkage estimator for the integrated covariance (ICV) matrix of financial assets is developed in two steps. The eigenvectors of the ICV are first constructed from a designed time variation adjusted realized covariance matrix of noise-free log-returns of rel- atively low frequency data. Then the regularized eigenvalues of the ICV are estimated by quasi-maximum likelihood based on high frequency data. The estimator is always positive definite and its inverse is the estimator of the inverse of ICV. It minimizes the limit of the out-of-sample variance of portfolio returns within the class of rotation-equivalent estimators. It works when the number of underlying assets is larger than the number of time series ob- servations in each asset and when the asset price follows a general stochastic process. Our theoretical results are derived under the assumption that the number of assets (p) and the sample size (n) satisfy p/n -> y > 0 as n -> 8. The advantages of our proposed estimator are demonstrated using real data. |
| Keywords: | Portfolio Choice; High Frequency Data; Integrated Covariance Matrix; Shrinkage Function |
| JEL: | C13 C22 C51 G12 G14 |
| Date: | 2016–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2016_014&r=mst |
| By: | Dare, Wale; Fengler, Matthias |
| Abstract: | We propose a non-parametric procedure for estimating the realized spot volatility of a price process described by an Itô semimartingale with Lévy jumps. The procedure integrates the threshold jump elimination technique of Mancini (2009) with a frame (Gabor) expansion of the realized trajectory of spot volatility. We show that the procedure converges in probability in L2([0, T]) for a wide class of spot volatility processes, including those with discontinuous paths. Our analysis assumes the time interval between price observations tends to zero; as a result, the intended application is for the analysis of high frequency financial data. |
| Keywords: | Nonparametric estimation, Itô semimartingale, Lévy jumps, Gabor frames, realized spot volatility |
| JEL: | C13 C14 |
| Date: | 2017–09 |
| URL: | http://d.repec.org/n?u=RePEc:usg:econwp:2017:15&r=mst |