Introducing Hurst exponent in pair trading

Ramos-Requena, José Pedro; Segovia, Juan Evangelista Trinidad; Sánchez-Granero, M.A.

doi:10.1016/j.physa.2017.06.032

Cited by 42 publications

(38 citation statements)

References 41 publications

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“…As it was explained in Section 2.2, the HP model is proposed as an alternative to correlation, which is why in this section a comparison using both models is done, to check whether it is true that our model is superior to the correlation method. In a previous work [62], it is shown that the strategy of Pairs Trading does not obtain good results by selecting those highly correlated pairs. The goal of this section is to test if a Pairs Trading strategy based on HP improves the performance of the strategy.…”

Section: Paris Trading: Strategy Definition and Applicationmentioning

confidence: 99%

“…This strategy was born in a North American investment bank in the 1980s [59]. In the scientific field, many studies were developed in which different selection methods were introduced, from the simplest ones such as the method of distance [60] or correlation, to more complex methods, such as cointegration [59], copula [61] or the Hurst exponent [62].…”

Section: Paris Trading: Strategy Definition and Applicationmentioning

confidence: 99%

“…1. First, the prices are normalized as defined by Ramos-Requena et al (2017) [62]. Therefore, for a pair of stocks A and B, for each dollar invested in A we will invest b dollars in B, where b is given by b = std(r A )/std(r B ), and r A (resp.…”

Section: Paris Trading: Strategy Definition and Applicationmentioning

confidence: 99%

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An Alternative Approach to Measure Co-Movement between Two Time Series

2020

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The study of the dependences between different assets is a classic topic in financial literature. To understand how the movements of one asset affect to others is critical for derivatives pricing, portfolio management, risk control, or trading strategies. Over time, different methodologies were proposed by researchers. ARCH, GARCH or EGARCH models, among others, are very popular to model volatility autocorrelation. In this paper, a new simple method called HP is introduced to measure the co-movement between two time series. This method, based on the Hurst exponent of the product series, is designed to detect correlation, even if the relationship is weak, but it also works fine with cointegration as well as non linear correlations or more complex relationships given by a copula. This method and different variations thereaof are tested in statistical arbitrage. Results show that HP is able to detect the relationship between assets better than the traditional correlation method. 2 of 24 multivariate GARCH, to estimate large covariance matrices, and Hafner [26] developed the GDCC model which is able to capture the asset-specific heterogeneity in the correlation structure.In this paper, an alternative way to look at correlations and co-movements is proposed by using the Hurst exponent. Along the paper, correlations, co-movements, etc. refer to cross-correlation, i.e., correlation between two different series (or assets). Introducing the HP of Two Series Hurst Exponent of a Time SeriesHurst exponent can be used to measure persistence as well as mean reversion properties in a time series. Introduced by H.E. Hurst in 1951 [27] to study the frequency of rain and drought in order to size the Nile River Dam, its application was extended not only to social sciences (see [28] for an interesting review) but also to meteorology [29], astrophysics [30], geography [31], medicine [32] or culturomics [33]. The first method to estimate the Hurst exponent was the R/S analysis [34]. However, as a consequence of a lack of accuracy of this methodology claimed by several authors [35-38] and its limited validity mainly for fractional Brownian motions [39], there has been an effort in the literature to provide new algorithms to improve the estimation. Some of these techniques are the Hudaks Semiparametric Method (GPH) [40], the Quasi Maximum Likelihood analysis (QML) [41], the Generalized Hurst Exponent (GHE) [42], Wavelets [43], the Centered Moving Average (CMA) [44], the Multifractal Detrended Fluctuation Analysis (MF-DFA) [45], the Lyapunov Exponent [46,47], Geometric Method-Based Procedures (GM) [48] and Fractal Dimension Algorithms (FD) [49].Among all of them, in this paper we will use the GHE algorithm, because it does not require to calculate ranges, it is not biased when applied to short series [49,50] and the calculation is simple.The GHE algorithm is based on the scaling properties of the following statistic [50]:

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Section: Paris Trading: Strategy Definition and Applicationmentioning

confidence: 99%

Section: Paris Trading: Strategy Definition and Applicationmentioning

confidence: 99%

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An Alternative Approach to Measure Co-Movement between Two Time Series

2020

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“…It is reported in [18], that the different correlation measures do show important differences in terms of return and risk.…”

Section: Pearson Correlationmentioning

confidence: 99%

“…The datasets may also be made available on request from the corresponding author. Taking inspiration from [18] we adopted the rolling windows approach with 500 trading days for identifying the best pairs for trade, and the next 250 trading days are for conducting trading on the selected pairs. So, overall we obtain 6 windows for training purpose and 6 windows for testing purpose.…”

Section: Data Descriptionmentioning

confidence: 99%