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It might help to think of the two as special cases of $$S_{i+1}-S_i = \sigma (c+S_i)^\beta \epsilon$$ which looks like a Constant Elasticity of Variance extension. Taking squares of both sides and then logs will (nearly) linearise it, allowing you to carry some basic estimation using OLS. The parameter $c$ will control the lower bound and can impose some ...

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As a short summary and adaption of the question: You better redefine $\hat{r}_i= \frac{S_{i-1}}{S_1}-1$ and $\hat{S}_i = (1+\hat{r}_i)S_0$. The above definition of $\hat{S}_i$ yields a sample of potential values for $S$ for the future day. This approach is usually applied in historical simulation. The aim here is to use information of the past about the ...

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There are actually a lot of options nowadays. Adjusting your data using historical realized inflation is certainly one way to go. And as @User1996 mentioned, the CPI for All Urban Consumers is the frequently quoted "headline" number. However, to the extent that asset prices reflect inflation expectations, it might be better to use forward-looking ...

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The U.S. Consumer Price Index For All Urban Consumers (http://research.stlouisfed.org/fred2/series/CPIAUCSL) is the CPI you hear in the news, and is the standard inflation number.

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The data has definitely not disappeared, it's a problem with your vendor. There has been a corporate action on 2014-02-27 and hence the strike prices have been adapted accordingly. According to Bloomberg bsym your P69 (composite ID BBG004L7P7L6) became P68.63, and P70 (BBG004L7P8C4) became P69.63.

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