Lecture_6_-_Diversification_-_Defining_Risk_and_Understanding_its_Relationship_with_Return

# Lecture_6_-_Diversification_-_Defining_Risk_and_Understanding_its_Relationship_with_Return

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1 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.

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Lecture_6_-_Diversification_-_Defining_Risk_and_Understanding_its_Relationship_with_Return

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