cointegration Archives - Hudson & Thames

Introduction to Distance Approach in Pairs Trading: Part II

Front Page Research, Statistical Arbitrage

We have discussed Basic Distance Approach in the previous blog post. In this post, we’ll look into one of the advanced methods in the Distance Approach and its differences to the Basic Distance Approach. If you haven’t read the previous blog post, we recommend reading it before you read this post.

So, what is the Pearson Correlation Approach? It is a type of Distance Approach and applies Pearson correlation on return level for identifying pairs. The main concept is similar to the Basic Distance Approach, where pairs are formed with a particular rule, and a portfolio is constructed based on the trading signals of pairs.

Introducing: Arbitragelab Tear Sheets

Front Page Research, Statistical Arbitrage

Pairs selection is the first crucial step to building a pairs trading strategy. And it is no surprise, to perform it correctly, one must diligently examine, compare and contrast numerous test results, graphs and characteristics. For example, cointegration analysis alone can be performed in one of two methods – utilizing the Engle-Granger approach or the Johansen approach. To truly have the complete picture of the pairs suitability, with the Engle-Granger approach, the researcher should perform the test(and further analysis) for both possible combinations, A/B or B/A, in a pair since it is sensitive to which asset we choose to be the “dependent” one.
The Johansen test, in turn, provides multiple cointegration vectors, which also should be examined separately and taken into account. Not to mention the possible analysis of the residuals, auto-correlation tests, etc., brings even more data to the table for you to make your judgement.

And now, we have two options: memorize everything or constantly switch between numerous parameters and plots to check, contrast and compare. It results in loading your brain with tons of ‘noise’ that distracts from focusing on the evaluation itself. But it doesn’t have to be this way. Data analysis thrives when there is order, accessibility and clarity. And what embodies these three qualities better than combining everything into an interactive well-rounded tear sheet?

An Introduction to Cointegration for Pairs Trading

Front Page Research, Statistical Arbitrage

Cointegration, a concept that helped Clive W.J. Granger win the Nobel Prize in Economics in 2003 (see Footnote 1), is a cornerstone of pairs and multi-asset trading strategies. Anecdotally, forty years have passed since Granger coined the term “cointegration” in his seminal paper “Some properties of time series data and their use in econometric model specification” (Granger, 1981), yet one still cannot find the term in Merriam-Webster, and some spell checkers will draw a wavy line without hesitation beneath its every occurrence.

Indeed, the concept of cointegration is not immediately apparent from its name. Therefore, in this article, I will attempt to answer the following questions:

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