Understanding Stochastic Calculus and Expected Value Formulas

Click For Summary
SUMMARY

This discussion focuses on the principles of Stochastic Calculus and Expected Value, specifically in the context of a simple betting game involving a Bernoulli Random Variable. Participants analyze the expected value calculations for random outcomes of $1 and -$1, concluding that E[R_i] = 0, E[R^2_i] = 1, and E[R_iR_j] = 0 due to equal probabilities of heads and tails. The total winnings after multiple tosses, represented by S_i, leads to E[S_i] = 0 and E[S_i^2] = i, demonstrating the linear growth of expected winnings over time.

PREREQUISITES
  • Understanding of Bernoulli Random Variables
  • Familiarity with Expected Value calculations
  • Basic knowledge of probability theory
  • Concept of stochastic processes
NEXT STEPS
  • Study the properties of Bernoulli Trials in depth
  • Learn about the Law of Large Numbers and its implications
  • Explore Stochastic Calculus applications in finance
  • Investigate variance and covariance in random variables
USEFUL FOR

Students of probability theory, mathematicians, and anyone interested in the applications of stochastic processes in real-world scenarios, particularly in finance and risk assessment.

courtrigrad
Messages
1,236
Reaction score
2
Hello all

If you throw a head I give you $1. If you throw a tail you give me $1. If [tex]R_i[/tex] is the random amount ($1 or -$1) you make on the [tex]ith[/tex] toss then why is: [tex]E[R_i] = 0, E[R^2_i]=1, E[R_iR_j] = 0[/tex]? If [tex]S_i = \sum^i_{j=1} R_j[/tex] which represents the total amount of money you have won up to and including the ith toss, then why does [tex]E[S_i] = 0, E[S_i^2] = E[R_1^2 + 2R_1R_2 + ...] = i[/tex]? I know that if there had been five tosses already then [tex]E[S_6|R_1,...,R_5] = S_5[/tex]

Any help is appreciated

Thanks
 
Physics news on Phys.org
I know that Expected Value is defined as: [tex]E[X] = \sum_x f(x)P(x)[/tex] for a discrete variable.
 
courtrigrad said:
I know that Expected Value is defined as: [tex]E[X] = \sum_x f(x)P(x)[/tex] for a discrete variable.

Right, and Ri is what is called (in my probability theory class I'm taking anyways) a Bernoulli Random Variable. There are only two possible outcomes: success, or failure, usually denoted by 1 or 0 respectively. However, since Ri represents the winnings, and they are -1 for tails, the expectation, given your definition is (letting H denote a heads and T a tails):

[tex]E[X] = \sum_x xp(x) = 1\cdot P(H) -1 \cdot P(T) = 1/2 - 1/2 = 0[/tex]

The expectation can be interpreted as the average value (ie the value expected over time) that the random variable takes on. For a random variable that can only take on the values 1 and -1, what is the average? Since both outcomes have equal weighting (p = 1-p = 1/2), on average you'd expect no net winnings, since the chances of getting a dollar are about equal to that of having to pay up. I hope this gets you started. Now consider the other cases: what is Ri squared? Well, what is 1^2? What is (-1)^2?
ETC...
 

Similar threads

  • · Replies 29 ·
Replies
29
Views
3K
  • · Replies 8 ·
Replies
8
Views
2K
  • · Replies 7 ·
Replies
7
Views
4K
  • · Replies 2 ·
Replies
2
Views
1K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 15 ·
Replies
15
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 2 ·
Replies
2
Views
5K
Replies
16
Views
3K
Replies
2
Views
2K