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on Market Microstructure |
| By: | Stübinger, Johannes; Endres, Sylvia |
| Abstract: | This paper develops a pairs trading framework based on a mean-reverting jump-diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015. The established statistical arbitrage strategy enables us to perform intraday and overnight trading. Essentially, we conduct a 3-step calibration procedure to the spreads of all pair combinations in a formation period. Top pairs are selected based on their spreads' meanreversion speed and jump behavior. Afterwards, we trade the top pairs in an out-of-sample trading period with individualized entry and exit thresholds. In the back-testing study, the strategy produces statistically and economically significant returns of 60.61 percent p.a. and an annualized Sharpe ratio of 5.30, after transaction costs. We benchmark our pairs trading strategy against variants based on traditional distance and time-series approaches and find its performance to be superior relating to risk-return characteristics. The mean-reversion speed is a main driver of successful and fast termination of the pairs trading strategy. |
| Keywords: | finance,statistical arbitrage,pairs trading,high-frequency data,jump-diffusion model,mean-reversion |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwqwdp:102017&r=mst |
| By: | Li, Zhiyong; Lambe, Brendan; Adegbite, Emmanuel |
| Abstract: | In this paper, we introduce two low frequency bid-ask spread estimators using daily high and low transaction prices. The range of mid-prices is an increasing function of the sampling interval, while the bid-ask spread and the relationship between trading direction and the mid-price are not constrained by it and are therefore independent. Monte Carlo simulations and data analysis from the equity and foreign exchange markets demonstrate that these models significantly out-perform the most widely used low-frequency estimators, such as those proposed in Corwin and Schultz (2012) and most recently in Abdi and Ranaldo (2017). We illustrate how our models can be applied to deduce historical market liquidity in NYSE, UK, Hong Kong and the Thai stock markets. Our estimator can also effectively act as a gauge for market volatility and as a measure of liquidity risk in asset pricing. |
| Keywords: | High-low spread estimator; effective spread; transaction cost; market liquidity |
| JEL: | C02 C13 C15 |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:79102&r=mst |
| By: | Frédéric Abergel (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec); Côme Huré (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique); Huyên Pham (CREST - Centre de Recherche en Économie et Statistique - INSEE - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, LPMA - Laboratoire de Probabilités et Modèles Aléatoires - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We propose a microstructural modeling framework for studying optimal market making policies in a FIFO (first in first out) limit order book (LOB). In this context, the limit orders, market orders, and cancel orders arrivals in the LOB are modeled as Cox point processes with intensities that only depend on the state of the LOB. These are high-dimensional models which are realistic from a micro-structure point of view and have been recently developed in the literature. In this context, we consider a market maker who stands ready to buy and sell stock on a regular and continuous basis at a publicly quoted price, and identifies the strategies that maximize her P&L penalized by her inventory. We apply the theory of Markov Decision Processes and dynamic programming method to characterize analytically the solutions to our optimal market making problem. The second part of the paper deals with the numerical aspect of the high-dimensional trading problem. We use a control randomization method combined with quantization method to compute the optimal strategies. Several computational tests are performed on simulated data to illustrate the efficiency of the computed optimal strategy. In particular, we simulated an order book with constant/ symmet-ric/ asymmetrical/ state dependent intensities, and compared the computed optimal strategy with naive strategies. |
| Keywords: | Markovian Quantization,Markov Decision Process,Limit Order Book,High-Frequency Trading,Queuing model,pure-jump controlled process,High-dimensional Stochastic Control |
| Date: | 2017–05–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01514987&r=mst |