Stats

Question 1

Variance

Answer 1

Measures how far a set of numbers are spread out

Question 2

Variance defined

Answer 2

average squared differences between the mean and each item in the population or in the sample

Question 3

High variance means

Answer 3

data points are very spread out

Question 4

Standard deviation defined

Answer 4

measure of dispersion expressed as the square root of the variance

Question 5

Standard deviation measures

Answer 5

the amount of variability around the average or mean

Question 6

Advantage of standard deviation

Answer 6

expresses dispersion in the same units as the original values in the sample or population

Question 7

A low standard deviation indicates

Answer 7

data points gather close to the means

Question 8

Semi variance measures

Answer 8

data that is below the mean or target value of a data set

Question 9

Semi variance defined

Answer 9

average of the squared deviations of all values less than the average or mean

Question 10

Coefficient of variation defined

Answer 10

ration of the standard deviation to the mean

Question 11

CV formula

Answer 11

standard deviation/mean

Question 12

CV result

Answer 12

shows the extent of variability of relation to mean of the population

Question 13

CV application

Answer 13

for comparison of data sets with different units or widely different means, one should use the CV instead of SDEV

Question 14

Skewness defined

Answer 14

describes asymmetry of data points from a normal distribution

Question 15

Negative skew

Answer 15

skews to the left

Question 16

Positive skew

Answer 16

Skews to the right

Question 17

Non-normal distributions (Skewness applied)

Answer 17

standard mean-variance analysis is limited which means SDEV is less meaningful

Question 18

For a negative skew - SDEV applications

Answer 18

underestimating the risk

Question 19

For a positive skew - SDEV applications

Answer 19

overestimating the risk

Question 20

Kurtosis measures

Answer 20

How fat the tails are on a distribution relative to a normal distribution curve

Question 21

Positive Kurtosis (Leptokurtic)

Answer 21

will show more extreme outcomes, creating a tendency for the observations around the mean to seem great and appearing to have a higher peak

Question 22

Low Kurtosis (Platykurtic)

Answer 22

will show thinner tails (fewer extreme outliers), flatter top, less peakedness

Question 23

Higher kurtosis suggest greater

Answer 23

Risk than reflected in the normal distribution relied upon in the traditional mean-variance framework

Question 24

Standard deviation is a good measure of risk when returns are

Answer 24

symmetric

Question 25

What if excess returns are not normally distributed

Answer 25

Standard deviation is no longer a complete measure of risk; Sharpe ratio is not a complete measure of performance; need to consider skew and kurtosis

Question 26

N vs. N-1

Answer 26

Use N when you are using all of the data available (I.e. Population) then use N-1 when only using a sample (I.e. calculating standard deviation)

Question 27

z = -1

Answer 27

one SDEV covering about 16% of the mean representing 68% of the outcomes

Question 28

z = -2

Answer 28

covers two SDEV covering about 95% of the outcomes

Question 29

z = -3

Answer 29

covers three SDEV covering about 99% of the outcomes

Question 30

z = -1.65

Answer 30

covers 5% (useful for VaR)

Question 31

z = -2.33

Answer 31

covers 1 % (useful for VaR)

Question 32

Monte Carlo Simulation

Answer 32

stat modeling used to approximate the probability of future outcomes through multiple trials using random variables

Question 33

Investment Consultants Fallacy

Answer 33

shows graphically how portfolio risk decreases over time due to clustering of returns around a long term average

Question 34

Consultants Graph

Answer 34

the appearance that risk goes over time due to diversification

Question 35

Samuelson and Merton Graph

Answer 35

the expected terminal (ending) range of values of an investment is much wider with more potential outcomes due to uncertainty over time

Question 36

Covariance Defined

Answer 36

indicates how two variables are related

Question 37

Covariance measures

Answer 37

the degree to which the returns of two assets move together

Question 38

Assets possessing a high covariance with each other

Answer 38

does not offer much diversification

Question 39

Covariance calculation

Answer 39

Correlation coefficient times standard deviations of both assets

Question 40

Correlation coefficients defined

Answer 40

indicates the degree of relationship between two variables

Question 41

Correlation coefficients calculation

Answer 41

covariance divided by sdev * sdev

Question 42

Multiple regression R squared also known as

Answer 42

coefficient of determination

Question 43

R squared (coefficient of determination) gives us

Answer 43

the proportion of variation in one variable than can be explained by another variable

Question 44

R squared (coefficient of determination) metric indicates

Answer 44

the closeness or accuracy of the fit

Question 45

R squared applications

Answer 45

The higher the number, the more meaningful the relationship; it gives us an indication of the level of diversification; gauges the reliability of alpha as an indicator of the managers return and beta as an indicator of risk

Question 46

R squared below .6

Answer 46

benchmark is not as helpful as a comparative tool

Question 47

R squared calculation

Answer 47

Beta Squared times SDEV of market squared divided by SDEV of the portfolio squared

Question 48

Random Walk defined

Answer 48

event in which a set of events or samples follows a pattern of random unpredictable steps

Question 49

Random Walk Application

Answer 49

stock prices cannot be accurately predicted

Question 50

Benefits of multi-period forecasting

Answer 50

improved accuracy, consistency and smoothing of volatility

Question 51

Variance defined

Answer 51

as the expected value of the difference between the variable and its mean squared

Question 52

Variance used in

Answer 52

Monte Carlo analysis and portfolio optimization process

Question 53

Coefficient of variation formula

Answer 53

standard deviation divided by expected return

Question 54

A normal distribution has a kurtosis of

Answer 54

3

Question 55

Anything greater than kurtosis of 3 is considered

Answer 55

Fat tailed

Question 56

One tailed value for 95% percent of the curve tell us

Answer 56

95% of the values are less than 1.64 SDEV above the mean

Question 57

As the normal distribuiton is symetrical, that 5% of the value are great than

Answer 57

1.6 standard deviations below the mean

Question 58

The two tailed value tells us that 95% of the mass is

Answer 58

within + or - 1.96 standard deviations from the mean

Question 59

The two tailed value tell us that 2.5% of the outcomes are

Answer 59

Less than -1.96 standard deviations from the mean, 2.5% are greater than +1.96 Standard deviations from the mean

Question 60

Null hypothesis

Answer 60

default position that there is no relationship between the variables

Question 61

Alternative hypothesis

Answer 61

the opposite of the null hypothesis that is being tested for relevance

Question 62

Regression-based techniques use attribution based on

Answer 62

CAPM and estimating alpha of the manager

Question 63

A key advantage to Monte Carolo analysis is

Answer 63

being able to factor in a number of data points into one model

Question 64

An investor reviewing a sample population data set would want to observe

Answer 64

small standard errors