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Money Markets and Finance
Money Markets and Finance
Lecture 6
Lecture 6
Diversification: Defining Risk and
Diversification: Defining Risk and
Understanding its Relationship with
Understanding its Relationship with
Return
Return
1. Lecture Overview
1. Lecture Overview
In today’s lecture, we will introduce the
concept of a risky asset.
During the course of
the lecture we will also discuss:
– How the risk of an asset is measured;
– The attitude of investors towards risk; and,
– The relationship between the risk of an asset
and the return required by investors on the
asset.
2. What is a Random Variable?
2. What is a Random Variable?
A
random variable
is one that can take on
any number of different values.
Each value
has an associated probability of occurring.
An example is the return on a share over the
next year.
Many different outcomes are
possible,
and
each
possibility
has
an
associated
probability
of
occurring.
Therefore, the return on a share is a random
variable.
2. What is a Random Variable?
2. What is a Random Variable?
The uncertainty associated with the outcome of a
random
variable
is
described
by
a
probability
distribution, which illustrates the relative likelihood of
each
possible
outcome
occurring.
The
most
commonly used distribution is the
normal distribution
,
an example of which is provided below.
75
65
55
45
35
25
15
5
5
15
25
35
45
55
65
75
0.020
0.018
0.016
0.014
0.012
0.010
0.008
0.006
0.004
0.002
0.000
Probability density
Stock market return
2. What is a Random Variable?
2. What is a Random Variable?
There are 4 “moments” commonly used to describe the shape
or characteristics of a distribution:
–
Mean:
The “average” or “expected “value” of the distribution.
For a
normal distribution, the mean is equal to the mode (value with the
highest probability of occurrence) and the median (the middle of all
possible values after ordering);
–
Variance:
A measure of how closely or widely individual values are
spread around the mean value. A small variance means that the data
doesn’t vary a lot from the mean. A large variance means that the data
is more spread out. The standard deviation,
σ
, is simply the square
root of the variance.
Given its relative ease of interpretation, standard
deviation is often discussed instead of variance;
–
Skewness:
A measure of the lack of symmetry of a distribution about
its mean.
By definition, the normal distribution has zero skewness (ie
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This note was uploaded on 10/13/2011 for the course FINM 1001 taught by Professor Miss during the Three '10 term at Australian National University.
 Three '10
 Miss

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