Exploring Alternative Distributions in Ito's Lemma for Financial Engineering

AI Thread Summary
The discussion centers on the derivation of Ito's lemma in Hull's derivative pricing book, specifically questioning the assumption that epsilon is normally distributed. A participant suggests that using a random variable with values of +1 or -1 could simplify the derivation, noting that this would affect the variance. Another contributor explains that iterating a mini-epsilon leads to a normal distribution, reinforcing the original assumption. The conversation also briefly shifts to aeronautical engineering, with a request for project ideas related to propulsion or aerodynamics. The primary focus remains on the implications of alternative distributions in Ito's lemma for financial engineering.
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For any financial engineers here who understand Hull's derivative pricing book. I've gone through chapter 12 (Wiener Processes and Ito's Lemma, 2008 edition).

The derivation of Ito's lemma assumes epsilon is normally distributed with a mean of zero and a variance of 1. I have a hard time filling in steps with this assumption.

Would the derivation also work if epsilon was a random variable that could take on only values of +1 or -1 with 50-50 probability? It would make the filling in steps much easier.
 
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I think the variance of the distribution actually delta T. What is 1 is the volatility.

I don't have Hull in front of me, but what you say makes sense. Imagine mini-epsilon that could take a value of +mini-epsilon or -mini-epsilon. Now imagine that you iterate that a large number of steps so that you get epsilon. At that point, what you get is a normal distribution with variance of epsilon.
 
im studying aeronautical engineering..im really interested to do some projects regarding propulsion or aerodynamics topics...i did get a guidance here..so please some one give idea or support ..
 
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