Page 52 - DMGT515_PERSONAL_FINANCIAL_PLANNING

Unit 3: Managing Investment Risks

3.4 Measurement of Risk Notes

Risk measurement must provide answers to following two basic questions:
1. How does the target figure change when the risk factors change?

2. What changes in the risk factor must be reckoned with in the future?
Since risk is variability in the expectations, there are many statistical tools that can be employed
to measure risk. The following are the usual statistical techniques that are relied upon.

1. Standard Deviation (SD)
2. Variance (V)
3. Coefficient of Variation (CV)
4. Skewness (Sk)
5. Probability Distribution

1. Standard deviation (σ): SD provides a measure of the spread of the probability distribution.
The larger the SD, the greater would be the dispersion of the distribution. This is denoted
by the symbol (σ) sigma. This is commonly used to measure variability in a given
distribution. The following equation is used to find out the value of SD.

∑(x – x) 2
σ = ; or
N
∑ fx 2 ⎛ ∑ fx ⎞ 2
σ = – ⎜ ⎟
N ⎝ N ⎠
Where x is the variable under consideration

2
(x – x) is the square of deviation from the mean of x
‘f’ is the frequency distribution
In order to understand the significance of SD, let us take a simple example. Let us assume
that we have invested in two stocks, A & B. The following are the returns generated by
them in a period of five years:
Years 1 2 3 4 5
A(%) 30 28 34 32 31
B(%) 26 13 48 11 57
The average return produced by the above two investments is 31%. Whereas the SD of
returns is quite significant. SD for A is 2% and SD for B is 18.5%. This implies that investment
on stock A is less riskier than investment in stock B. Perhaps, we may also tend to conclude
that investment in B is nine times riskier than investment in A.
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2. Variance (σ ): The square of SD is called variance. This measures the dispersion around the
mean.
3. Co-efficient of Variation (CV): This is yet another frequently used measure of variation.
σ
CV = x100
x
The interpretation of this measure is that the lesser the variation in data, the more consistent
it is.

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