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I want to determine the kurtosis of a straddle. My question is closely related with the following topic here. According to the following paper of Ben-Meir and Schiff (2012) the expected value of a call is equal to

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where

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The variance of the call is

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Following the standard definition of kurtosis I can write:

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Similar, I can write the same for the puts:

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Is it correct to assume that:

I want to calculate the kurtosis of a straddle

HJA24
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  • No it is not. For the same reason you cannot assume the variance of a portfolio equals the sum of the of the individual variances. And it's even weird to talk about variance of a call/putinstead of variance of the return of a call/put. This may help: https://quant.stackexchange.com/questions/2273/calculating-portfolio-skewness-kurtosis – phdstudent Jun 23 '20 at 15:37

1 Answers1

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Even if you assume null cokurtosis terms, your equality is still off:

\begin{align} \operatorname{Kurt}[X+Y] = {1 \over \sigma_{X+Y}^4} \big( & \sigma_X^4\operatorname{Kurt}[X] + \sigma_Y^4\operatorname{Kurt}[Y] \big). \end{align}

Note that you need $\sigma_{X+Y}^2$. You already have $\sigma_X^2$ and $\sigma_Y^2$ (computed in the paper).

Full formula is:

\begin{align} \operatorname{Kurt}[X+Y] = {1 \over \sigma_{X+Y}^4} \big( & \sigma_X^4\operatorname{Kurt}[X] + 4\sigma_X^3\sigma_Y\operatorname{Cokurt}[X,X,X,Y] \\ & {} + 6\sigma_X^2\sigma_Y^2\operatorname{Cokurt}[X,X,Y,Y] \\[6pt] & {} + 4\sigma_X\sigma_Y^3\operatorname{Cokurt}[X,Y,Y,Y] + \sigma_Y^4\operatorname{Kurt}[Y] \big). \end{align}

ir7
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