Stochastic Calculus: Conditional Expectation

In summary, your careless mistakes have prevented you from getting the correct answer for the product of three independent random variables.
  • #1
WMDhamnekar
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Homework Statement
Suppose ##X_1,X_2 \dots ## are independent random variables with ##\mathbb{P}[X_j= 1] =1- \mathbb{P}[X_j=-1]=\frac13## Let ## S_n = X_1 +\dots +X_n## Find ## \mathbb{E}[S_n], \mathbb{E}[S^2_n], \mathbb{E}[S^3_n]##
Relevant Equations
Not applicable
Are my following answers correct?
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  • #2
Have you tried the simple expedient of substituting some values of n and seeing if you get the right answer? Try n=1 to start.
You are making some careless mistakes
e.g. the expression for E(S2) should be n(1) + n(n-1)(1/9)
Your statement about the product of 3 random variables makes no sense; they are certainly not equal to zero. Each of the 3 random variables may independently take either of the values 1 or -1.
 
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  • #3
WMDhamnekar said:
Homework Statement:: Suppose ##X_1,X_2 \dots ## are independent random variables with ##\mathbb{P}[X_j= 1] =1- \mathbb{P}[X_j=-1]=\frac13## Let ## S_n = X_1 +\dots +X_n## Find ## \mathbb{E}[S_n], \mathbb{E}[S^2_n], \mathbb{E}[S^3_n]##
Relevant Equations:: Not applicable

Are my following answers correct?View attachment 323141
View attachment 323142
View attachment 323157
I agree with mjc -- hey, it rhymes!

You can't treat a multinomial ## ( X_1+X_2+....+X_n)^2## as a standard binomial ##( X_1+X_2)^2##. Look up multinomial coefficients.
 
  • #4
mjc123 said:
Have you tried the simple expedient of substituting some values of n and seeing if you get the right answer? Try n=1 to start.
You are making some careless mistakes
e.g. the expression for E(S2) should be n(1) + n(n-1)(1/9)
Your statement about the product of 3 random variables makes no sense; they are certainly not equal to zero. Each of the 3 random variables may independently take either of the values 1 or -1.
So, taking into consideration your this reply, I correct my amswers as follows:
##\mathbb{E}[S_n]=-\displaystyle\frac{n}{3}, \mathbb{E}[S^2_n]= n +\displaystyle\frac{n(n-1)}{9}, \mathbb{E}[S^3_n ] = -\displaystyle\frac{n}{3}-\frac{n(n-1)}{2} -\displaystyle\frac{n(n-1)}{9}##

Now, are these above answers correct?
 
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  • #5
Correct answers are
1677954533684.png
 
  • #6
No, they are not. The answer for E(Sn2) is wrong for n = 2, and that for E(Sn3) is wrong for n = 3.

The answer you gave in post #4 for E(Sn2) is right, but the answer for E(Sn3) is not. You didn't show your working, but I suspect the mistake may lie in enumerating the terms of different kinds.
There are n terms of the form Xi3, each of which has expectation -1/3.
There are 3n(n-1) terms of the form Xi2Xj, each of which has expectation -1/3*1.
There are n(n-1)(n-2) terms of the form XiXjXk, each of which has expectation -1/27.
(Check that the total number of terms is n3.)
 
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  • #7
Sorry to insist on this, but I suggest you check multinomial coefficients , to determine how to expand multinomials ##( x_1+x_2+...+x_k)^n##. I suspect the errors may be partially due to this.
 
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Related to Stochastic Calculus: Conditional Expectation

What is conditional expectation in the context of stochastic calculus?

Conditional expectation in stochastic calculus refers to the expected value of a random variable given the information available up to a certain time. It is a crucial concept for understanding the evolution of stochastic processes, particularly in filtering and prediction problems.

How is conditional expectation used in the Itô calculus?

In Itô calculus, conditional expectation is often used to derive properties of Itô integrals and to solve stochastic differential equations (SDEs). It helps in simplifying expressions involving stochastic processes by conditioning on the information up to a certain time, which can be particularly useful for proving martingale properties and other results.

What is the relationship between conditional expectation and martingales?

Conditional expectation is closely related to martingales. A stochastic process is a martingale if its conditional expectation, given past information, is equal to its current value. This property is fundamental in the study of martingales and is used to prove various theorems and properties within stochastic calculus.

How do you compute conditional expectation for a given stochastic process?

Computing conditional expectation for a given stochastic process often involves using known properties of the process, such as its distribution or moments, and applying the definition of conditional expectation. In practice, this can involve solving partial differential equations or using techniques from measure theory and probability.

What role does conditional expectation play in financial mathematics?

In financial mathematics, conditional expectation is used to model the expected future values of financial assets given current information. It is a fundamental tool in the pricing of derivatives, risk management, and portfolio optimization. Conditional expectation helps in determining fair prices and hedging strategies by accounting for the information available at different times.

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