Graduate Approximation for volatility of random variable

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The discussion centers on seeking clarification regarding an approximation for the volatility of a random variable found in the Hull Derivatives book. The user requests a logical explanation or derivation of this approximation, as the book does not provide sufficient detail. A reference to a related paper is included for additional context. Participants are encouraged to share insights or resources that can help elucidate the concept. Understanding this approximation is crucial for applying it effectively in financial contexts.
Petr Rygr
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Hello, could anyone please explain me some logic or derivation behind the approximation:
explain.jpg

Found it in the Hull Derivatives book without further explanation. Thanks for help
 
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Thread 'Hypothesis testing: Defining H0, HA hypotheses so that ( H_A)_A' makes sense'

The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...
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