Pairs Trading in Commodity Futures: Evidence from the Indian Market

2020 ◽  
pp. 363-371
Keyword(s):  
Trading Strategy ◽  
Indian Market ◽  
Pairs Trading ◽  
Distance Method ◽  
Statistical Arbitrage ◽  
Cointegration Tests ◽  
Trading Period ◽  
Expected Values ◽  
Two Stages ◽  
Selection Of

The pairs trading, one of the techniques of the statistical arbitrage, is a market-neutral trading strategy that employs time series methods to identify relative mispricing between securities based on the expected values of these assets. The main objective of this study was to investigate the profitability and risks of pairs trading based on the selection of pairs through minimising the sum of squared deviation (distance method) and the selection based on cointegration tests (cointegration method) using the future daily prices of commodities traded and listed on The Multi Commodity Exchange of India (MCX) over 2011-2017 on a rolling basis. The pairs trading strategy was performed in two stages: the formation period and the trading period. The strategy involved long position in one commodity and short position in other commodity of the pair identified. The study revealed that pairs trading in commodities were significantly profitable, with average annualised profitability of up to 59 percent, including transaction costs.

scholarly journals Evaluation of dynamic cointegration-based pairs trading strategy in the cryptocurrency market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Masood Tadi ◽  
Irina Kortchemski
Keyword(s):  
Trading Strategy ◽  
Significant Finding ◽  
Pairs Trading ◽  
Content Type ◽  
Arbitrage Opportunities ◽  
Cointegration Tests ◽  
Look Back ◽  
Trading Period ◽  
Asset Selection ◽  
Binned Data

Purpose This paper aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market and evaluate its return and risk by applying three different scenarios. Design/methodology/approach This study uses the Engle-Granger methodology, the Kapetanios-Snell-Shin test and the Johansen test as cointegration tests in different scenarios. This study calibrates the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. Findings By considering the main limitations in the market microstructure, the strategy of this paper exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that this study implements a numerous collection of cryptocurrency coins to formulate the model’s spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy’s maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. Originality/value This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, this study simulates the trading signals using best bid/ask quotes and market trades. This study exclusively takes the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

On the sensibility of the Pairs Trading strategy: the case of the FTS stock market index

2020 ◽  
Vol 38 (3) ◽  
Author(s):  
Ainhoa Fernández-Pérez ◽  
María de las Nieves López-García ◽  
José Pedro Ramos Requena
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Trading Strategy ◽  
Stock Market Index ◽  
Pairs Trading ◽  
Empirical Application ◽  
Statistical Arbitrage ◽  
Market Index ◽  
Standard Deviations ◽  
Using Data

In this paper we present a non-conventional statistical arbitrage technique based in varying the number of standard deviations used to carry the trading strategy. We will show how values of 1 and 1,2 in the standard deviation provide better results that the classic strategy of Gatev et al (2006). An empirical application is performance using data of the FST100 index during the period 2010 to June 2019.

Unsupervised Learning for Pairs Trading in the National Stock Exchange of India

2021 ◽  
Vol 23 (06) ◽  
pp. 1068-1082
Author(s):  
Chetan Tayal ◽  
◽  
Lalitha V.P ◽  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Unsupervised Learning ◽  
Stock Exchange ◽  
Statistical Significance ◽  
Search Space ◽  
Trading Strategy ◽  
Pairs Trading ◽  
Statistical Arbitrage ◽  
Main Challenge

Pairs Trading is a widely known and used market-neutral trading strategy that utilizes the concept of statistical arbitrage. It is based on the idea of mean-reverting time series and relies on the spread between two assets to demonstrate that property to buy an asset at a relatively undervalued price and an asset at a relatively overvalued price. This allows investors to manage risk if the market moves strongly in only one direction by making money on one side of the bet. The main challenge of pairs trading is selecting pairs that have an actual underlying relationship and their spread has real statistical significance. In this paper, we present the use of machine learning, specifically unsupervised clustering to construct our search space for pair selection and compare it against a traditional way of selecting pairs. We see that not only are we able to pick out more profitable pairs, these pairs are also less volatile and have less exposure to the market.

Statistical Arbitrage: Profits through Pairs Trading

2015 ◽  
Vol 2 (1) ◽  
pp. 140-148 ◽  
Author(s):  
Saloni Gupta
Keyword(s):  
Statistical Methods ◽  
High Probability ◽  
Efficient Market Hypothesis ◽  
Trading Strategy ◽  
Pairs Trading ◽  
Efficient Market ◽  
Basic Premise ◽  
Statistical Arbitrage ◽  
Starting Point ◽  
Persistent Anomalies

Statistical arbitrage is a popular device among hedge fund managers and assets management professionals. It refers to simultaneous buying and selling two different capital assets to earn super-normal profit. By identifying persistent anomalies that violate the efficient market hypothesis, statistical methods can be used to create a trading strategy to generate profit with high probability. A pair trading is one such trading strategy which is based on statistical arbitrage process. Pairs trading can be simple in concept, but can be one of the most complex types of trading in practice. The starting point of this strategy is that stocks that have historically had the same trading patters will have so in future as well. If there is a deviation from the historical mean this creates a trading opportunity, which can be exploited. Gains are earned when the price relationship is resorted. The basic premise of this strategy is that stock prices follow a mean reverting process. The objective of this paper is to identify arbitrage opportunities and calculating profits earned through these opportunities by using statistical tools. Many questions need to be answered before one can implement such strategy viz. which pair of stocks should be traded, how much do we buy/sell of each stock, how to catch the signal of an opportunity (i.e opening a position) and when to close the position so that profit could be earned. In this paper we have taken daily closing prices from 1/1/2010 to 1/1/2011 of thirty scrips of BSE-Sensex to form pairs. Pairs are formed on the basis of minimum distances between two stocks. We have decided not to invest anything. That is, purchase the same rupee amount of the long stock as we sell of the short stock so that strategy is self-financing. We open a position when the absolute value of the difference gets larger than two of its historical standardization.  To unwind the position, we wait until the first time it crosses zero. To calculate the profit/loss of this strategy, we have used “R-Software”. It is observed that profit could be earned through pairs trading if it is applied without losing patience. By identifying persistent anomalies that violate the efficient market hypothesis, statistical methods can be used to create a trading strategy to generate profit with high probability.

scholarly journals Which Pairs of Stocks should we Trade? Selection of Pairs for Statistical Arbitrage and Pairs Trading in Karachi Stock Exchange

2015 ◽  
Vol 54 (3) ◽  
pp. 215-244
Author(s):  
Laila Taskeen Qazi ◽  
Atta Ur Rahman . ◽  
Saleem Gul
Keyword(s):  
Financial Services ◽  
Commercial Banks ◽  
Stock Exchange ◽  
Pairs Trading ◽  
Services Sector ◽  
Statistical Arbitrage ◽  
Financial Services Sector ◽  
Price Series ◽  
Arbitrage Opportunity ◽  
Selection Of

Pairs Trading refers to a statistical arbitrage approach devised to take advantage from short term fluctuations simultaneously depicted by two stocks from long run equilibrium position. In this study a technique has been designed for the selection of pairs for pairs trading strategy. Engle-Granger 2-step Cointegration approach has been applied for identifying the trading pairs. The data employed in this study comprised of daily stock prices of Commercial Banks and Financial Services Sector. Restricted pairs have been formed out of highly liquid log share price series of 22 Commercial Banks and 19 Financial Services companies listed on Karachi Stock Exchange. Sample time period extended from November 2, 2009 to June 28, 2013 having total 911 observations for each share prices series incorporated in the study. Out of 231 pairs of commercial banks 25 were found cointegrated whereas 40 cointegrated pairs were identified among 156 pairs formed in Financial Services Sector. Furthermore a Cointegration relationship was estimated by regressing one stock price series on another, whereas the order of regression is accessed through Granger Causality Test. The mean reverting residual of Cointegration regression is modeled through the Vector Error Correction Model in order to assess the speed of adjustment coefficient for the statistical arbitrage opportunity. The findings of the study depict that the cointegrated stocks can be combined linearly in a long/short portfolio having stationary dynamics. Although for the given strategy profitability has not been assessed in this study yet the VECM results for residual series show significant deviations around the mean which identify the statistical arbitrage opportunity and ensure profitability of the pairs trading strategy. JEL classifications: C32, C53, G17 Keywords: Pairs Trading, Statistical Arbitrage, Engle-Granger 2-step Cointegration Approach, VECM.

scholarly journals Review of stochastic differential equations in statistical arbitrage pairs trading

2020 ◽  
Vol 20 (2) ◽  
pp. 71
Author(s):  
Sylvia Endres
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Large Body ◽  
Diffusion Models ◽  
Future Research ◽  
Pairs Trading ◽  
Statistical Arbitrage ◽  
Shed Light ◽  
Stationary Component ◽  
Selection Of

The use of stochastic differential equations offers great advantages for statistical arbitrage pairs trading. In particular, it allows the selection of pairs with desirable properties, e.g., strong mean-reversion, and it renders traditional rules of thumb for trading unnecessary. This study provides an exhaustive survey dedicated to this field by systematically classifying the large body of literature and revealing potential gaps in research. From a total of more than 80 relevant references, five main strands of stochastic spread models are identified, covering the ‘Ornstein–Uhlenbeck model’, ‘extended Ornstein–Uhlenbeck models’, ‘advanced mean-reverting diffusion models’, ‘diffusion models with a non-stationary component’, and ‘other models’. Along these five main categories of stochastic models, we shed light on the underlying mathematics, hereby revealing advantages and limitations for pairs trading. Based on this, the works of each category are further surveyed along the employed statistical arbitrage frameworks, i.e., analytic and dynamic programming approaches. Finally, the main findings are summarized and promising directions for future research are indicated.

scholarly journals Seleção de uma Carteira de Pares de Ações Usando Cointegração: Uma Estratégia de Arbitragem Estatística

2013 ◽  
Vol 11 (1) ◽  
pp. 49 ◽  
Author(s):  
João Frois Caldeira ◽  
Gulherme Valle Moura
Keyword(s):  
Empirical Analysis ◽  
Stock Exchange ◽  
Sharpe Ratio ◽  
Trading Strategies ◽  
Excess Returns ◽  
Pairs Trading ◽  
Statistical Arbitrage ◽  
Low Correlation ◽  
Cointegration Tests

Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean- reverting spreads with a certain degree of predictability. This paper applies cointegration tests to identify stocks to be used in pairs trading strategies. In addition to estimating long-term equilibrium and to model the resulting residuals, we select stock pairs to compose a pairs trading portfolio based on an indicator of profitability evaluated in-sample. The profitability of the strategy is assessed with data from the São Paulo stock exchange ranging from January 2005 to October 2012. Empirical analysis shows that the proposed strategy exhibit excess returns of 16.38% per year, Sharpe Ratio of 1.34 and low correlation with the market.

scholarly journals Loss protection in pairs trading through minimum profit bounds: A cointegration approach

2006 ◽  
Vol 2006 ◽  
pp. 1-14 ◽  
Author(s):  
Yan-Xia Lin ◽  
Michael McCrae ◽  
Chandra Gulati
Keyword(s):  
Statistical Modeling ◽  
Necessary Conditions ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Pairs Trading ◽  
Simulation Data ◽  
Equilibrium Pricing ◽  
Statistical Arbitrage ◽  
Statistical Validity ◽  
Step Procedure

Pairs trading is a comparative-value form of statistical arbitrage designed to exploit temporary random departures from equilibrium pricing between two shares. However, the strategy is not riskless. Market events as well as poor statistical modeling and parameter estimation may all erode potential profits. Since conventional loss limiting trading strategies are costly, a preferable situation is to integrate loss limitation within the statistical modeling itself. This paper uses cointegration principles to develop a procedure that embeds a minimum profit condition within a pairs trading strategy. We derive the necessary conditions for such a procedure and then use them to define and implement a five-step procedure for identifying eligible trades. The statistical validity of the procedure is verified through simulation data. Practicality is tested through actual data. The results show that, at reasonable minimum profit levels, the protocol does not greatly reduce trade numbers or absolute profits relative to an unprotected trading strategy.

scholarly journals Statistical Arbitrage in Emerging Markets: A Global Test of Efficiency

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 179
Author(s):  
Karen Balladares ◽  
José Pedro Ramos-Requena ◽  
Juan Evangelista Trinidad-Segovia ◽  
Miguel Angel Sánchez-Granero
Keyword(s):  
Emerging Markets ◽  
Market Efficiency ◽  
Hurst Exponent ◽  
Mean Reversion ◽  
Trading Strategy ◽  
Emerging Countries ◽  
Pairs Trading ◽  
Global Test ◽  
Statistical Arbitrage

In this paper, we use a statistical arbitrage method in different developed and emerging countries to show that the profitability of the strategy is based on the degree of market efficiency. We will show that our strategy is more profitable in emerging ones and in periods with greater uncertainty. Our method consists of a Pairs Trading strategy based on the concept of mean reversion by selecting pair series that have the lower Hurst exponent. We also show that the pair selection with the lowest Hurst exponent has sense, and the lower the Hurst exponent of the pair series, the better the profitability that is obtained. The sample is composed by the 50 largest capitalized companies of 39 countries, and the performance of the strategy is analyzed during the period from 1 January 2000 to 10 April 2020. For a deeper analysis, this period is divided into three different subperiods and different portfolios are also considered.

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