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対数価格の共和分係数をペアトレードでどう読むか

記事 Quant Q&A · 著者: mathjacks

サマリー

対数価格の共和分関係における係数が、ペアトレードで何を意味するかを検討します。係数を株数比と解釈する見方と、時価総額比と解釈する見方を対比し、それぞれがポジションサイズやリターンの計算にどう違いをもたらすかを説明します。

ある回答は、株価を定数倍しても百分率で見た値動きは変わらず、切片は変化する一方で、推定係数は変わらないと論じています。同じ時価総額を表すのに必要な株数は変わるため、これは係数を時価総額ウェイトと読む根拠になります。別の回答は、対数価格の変化が成長率を表すことを指摘し、投下資本が異なる場合に各レッグのリターンを単純に足し合わせるべきではなく、総利益を投下資本の合計と比較すべきだと述べています。この短い議論では、異なる主張が十分な導出や一般的な取引指針なしに示されています。実際の解釈は、スプレッドとポートフォリオをどう定義するかにも左右されます。

主なアイデア

  • 対数価格の共和分関係では、片方の価格系列を定数倍しても係数は変わりません。
  • このスケール不変性は、係数を単純な株数ではなく相対的な時価総額エクスポージャーと解釈する根拠になります。
  • 対数価格の変化は比例的な価格変動を表すため、レッグのリターンは投下資本を考慮して合算します。
  • この議論では異なる解釈を完全には整理しておらず、普遍的なポジションサイズのルールも示していません。

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# What does the cointegration coefficient represent in pairs trading when cointegrating log stock prices?


# What does the cointegration coefficient represent in pairs trading when cointegrating log stock prices?












In Pairs Trading by Vidyamurthy, on page 83 (and throughout the book), the author describes an elementary example of trading with log prices. The long run equilibrium of the basic portfolio is given by

$$ \log(p_t^A) - \gamma \log(p_t^B) = \mu $$

where $p_t^A$ and $p_t^B$ represent the prices of stocks $A$ and $B$ at time $t$, respectively, and $\gamma$ is the cointegration coefficient. When using these log prices, Vidyamurthy uses the cointegration coefficient ($\gamma$) to indicate the ratio of shares to hold rather than market values of positions (as stated should be the case here, for example).

My questions are:

> What is the correct practical interpretation of $\gamma$ when cointegrating log prices, should it represent the ratio of shares or the ratio of market values? If the latter, why does Vidyamurthy use the former interpretation throughout his book? Could both be valid?

Here is the example from the book:

## Answer by Artem Korol (score 1)

https://quant.stackexchange.com/a/21284

Firstly i think if you use log prices then γ shows by how much B stock growth rate outpaces A stock growth rate, but I don't understand why Ernie says that you need to hold market values fixed, if you do this then how are you going to profit from the spread?

Secondly there are typos in the return calculation: log(20.1)-log(19.5) = 0.03 not 0.3. Which refers to the 3% return on the A leg trade. B leg return is indeed 5.6% (assuming cc returns), however it is incorrect to sum these return to get 9% return on the total trade, since they were obtained from different amounts of capital. The return from A leg is 0.6USD and return from B leg is 0.29USD*1.5 = 0.435USD thus the total return is 1.035USD which we divide by the total capital deployed in the trade at time t, 19.5+1.5*7.46 = 30.69$, so 1.035/30.69 = 0.0337 and this is the real return on this trade, not 9%.

## Answer by Jason Huang (score 1)

https://quant.stackexchange.com/a/39613

It should mean market values (instead of shares). You can see this more obviously by creating an "artificial stock" (stock C) which price equals the price of stock B divided by 10,000 (same fluctuation rate, but just much smaller absolute price). Now the co integration coefficient would not change because C and B have the same variation rate (it would only affect the intercept, mu). If the cointegration coefficient means the shares ratio, then it would not make sense because now C occupy too little of your portfolio. It would only make sense as market values.

出典を明記したうえで、ライセンスに従って全文を掲載しています。 ライセンス: CC BY-SA 4.0 (Stack Exchange)

この要約は原文をもとにStratmillのリサーチエージェントが作成したもので、出典の複製ではありません。