Quantitative Research in High Frequency Trading for Natural Gas Futures Market
High frequency trading (HFT) in micro or milliseconds has recently drawn attention of financial researches and engineers. In nowadays algorithmic trading and HFT account for a dominant part of overall trading volume. The main objective of this research is
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Faculty of Humanities, Vilnius University, Muitines 8, 44280 Kaunas, Lithuania [email protected], [email protected]
Abstract. High frequency trading (HFT) in micro or milliseconds has recently drawn attention of financial researches and engineers. In nowadays algorithmic trading and HFT account for a dominant part of overall trading volume. The main objective of this research is to test statistical arbitrage strategy in HFT natural gas futures market. The arbitrage strategy attempts to profit by exploiting price differences between successive futures contracts of the same underlying asset. It takes long/short positions when the spread between the contracts widens; hoping that the prices will converge back in the near future. In this study high frequency bid/ask and last trade records were collected from NYMEX exchange. The strategy was back tested applying MatLab software of technical computing. Stat‐ istical arbitrage and HFT has given positive results and refuted the efficient market hypothesis. The strategy can be interesting to financial engineers, market micro‐ structure developers or market participants implementing high frequency trading strategies. Keywords: High frequency trading · Algorithmic trading · Futures market · Statistical arbitrage
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Introduction
One of the least discussed aspects on the financial science base is the high frequency trading (HFT). HFT execute thousands of orders a second and alter strategies in a matter of milliseconds. High frequency trading typically refers to trading activity that employs extremely fast automated programs for generating, routing, canceling, and executing orders in electronic markets [1]. HF algorithms trade in and out of positions in milli‐ seconds and leave flat positions at end of the day. Recent expansion of HFT mostly relates to adoption of electronic trading and algorithmic model-based order generation in the exchanges, which exceeds 70–80 % of overall market trading activity [2]. HFT is widespread in all the most important markets of the world, like stocks, currencies, futures, options, and other derivatives [3, 4]. The dominant HFT strategies contribute to market liquidity, i.e. market making strategies [5, 6], and to price discovery and market efficiency, i.e. arbitrage strategies [7, 8]. The shortage of hard scientific evidences about the profitability of HFT algorithms was a driving force for this study. A contribution of this paper lies in testing HFT and statistical arbitrage strategy with natural gas futures © Springer International Publishing Switzerland 2015 W. Abramowicz (Ed.): BIS 2015 Workshops, LNBIP 228, pp. 29–35, 2015. DOI: 10.1007/978-3-319-26762-3_3
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S. Masteika and M. Vaitonis
contracts, one of the most liquid instruments in global energy market [9, 10]. This paper presents the details and the results of the research.
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The Basic Algorithm of Statistical Arbitrage in HFT
The roots of statistical arbitrage can be traced back to the first hedge funds, round 1950, running portfolio hedging strategies which had long a
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