Chebyshev's Theorem: Analyzing Investment Portfolio Risk

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SUMMARY

This discussion focuses on the application of Chebyshev's Theorem to analyze investment portfolio risk, specifically comparing the Vanguard Total Stock Index and the Vanguard Balanced Index. Participants calculated the expected values, variances, and standard deviations for both indices using historical return data. The key task involves computing a 75% Chebyshev interval around the mean for both datasets to evaluate risk. The theorem states that at least 75% of the data values lie within k standard deviations from the mean, where k is derived from the formula 1 - 1/k².

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  • Understanding of Chebyshev's Theorem and its applications in statistics
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  • Basic knowledge of standard deviation and its significance in data analysis
  • Experience with investment portfolio analysis and risk assessment
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  • Study the differences between various investment indices, such as stock vs. balanced funds
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Investment analysts, financial advisors, and students studying portfolio management who seek to understand risk assessment through statistical methods.

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1. do bonds reduce the overall risk of an investment portfolio? let x be a random variable representing annual % return for Vangaurd Total Stock Index (all stocks). Let y be a random variable representing annual return for Vangaurd Balanced Index(60% stock and 40% bond). For the past several years we have the following data.

x: 11 0 36 21 31 23 24 -11 -11 -21
y: 10 -2 29 14 22 18 14 -2 -3 -10

a.) Compare Ex, Ex2, Ey and Ey2 (2 = squared)

b.) use results in part (a) to compute the sample mean, variance, and standard deviation for x and for y.

c.) Compute a 75 % Chebyshev interval about the mean for x values and also for y values. Use interval to compare funds.

I was able to do part a & b but have no idea what they want for c. i do have the answer but i am not sure what they used to get the answer.



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The Attempt at a Solution

 
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What is a Chebyshev interval?
 
There are two possibilities - I'm not sure what level of work you're at.

First (and simplest): how many standard deviations around the mean does chebyshev's theorem say you must go to include 75% of the data values? (remember chebychev's theorem says the percentage of values between [tex]\bar x \pm ks[/tex] is at least
[tex]1 - {1}/{k^2}[/tex].

Second (and more complicated) is the idea discussed at the following link:
http://www.quantdec.com/envstats/notes/class_12/ucl.htm

I'm guessing it is option 1 you need to use.
 

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