# Chapter 2 Portfolio Management and the Capital Asset Pricing Model

Save this PDF as:

Size: px
Start display at page:

Download "Chapter 2 Portfolio Management and the Capital Asset Pricing Model"

## Transcription

1 Chapter 2 Portfolio Management and the Capital Asset Pricing Model In this chapter, we explore the issue of risk management in a portfolio of assets. The main issue is how to balance a portfolio, that is, how to choose the percentage (by value) of each asset in the portfolio so as to minimize the overall risk for a given expected return. The first lesson that we will learn is that the risks of each asset in a portfolio alone do not present enough information to understand the overall risk of the entire portfolio. It is necessary that we also consider how the assets interact, as measured by the covariance (or equivalently the correlation) of the individual risks. 2.1 Portfolios, Returns and Risk For our model, we will assume that there are only two time periods: the initial time and the final time. Each asset has an initial value and a final value. Portfolios A portfolio consists of a collection of assets in a given proportion. Formally, we define a portfolio to be an ordered -tuple of real numbers where is the number of units of asset. If is negative then the portfolio has a short position on that asset: a short sale of stock, a short put or call and so on. A positive value of indicates a long position: an owner of a stock, long on a put or call and so on. Asset Weights It is customary to measure the amount of an asset within a portfolio by its percentage by value. The weight of asset is the percentage of the value of the asset contained in the portfolio at time, that is,

2 42 Introduction to the Mathematics of Finance Note that the sum of the weights will always be : Asset Returns The return on asset is defined by the equation which is equivalent to Since the value of an asset at time in the future is a random variable, so is the return. Thus, we may consider the expected value and the variance of the return. The expected return of asset is denoted by The variance of the return of asset Var is called the risk of asset. We will also consider the standard deviation as a measure of risk when appropriate. Portfolio Return The return on the portfolio itself is defined to be the weighted sum of the returns of each asset For instance, suppose that a portfolio has only 2 assets, with weights and and returns equal to % and %, respectively. Then the return on the portfolio is % Since the expected value operator is linear, the expected return of the portfolio as a whole is

3 2. Portfolio Management and the Capital Asset Pricing Model 43 Since the individual returns generally are not independent, the variance of the portfolio's return is given by the formula Cov Var where Cov is the covariance of and and is the correlation coefficient. Let us make some formal definitions. Definition The expected return on a portfolio is the expected value of the portfolio's return, that is, The risk of a portfolio is the variance of the portfolio's return, that is, Cov Var An asset is risky if its risk is positive and riskfree if its risk is. Until further notice, we will assume that the all assets in a portfolio are risky; that is,. More on Risk Let us take a closer look at the notion of risk. Generally speaking, there are two forms of risk associated with an asset. The systematic risk of an asset is the risk that is associated with macroeconomic forces in the market as a whole and not just with any particular asset. For example, a change in interest rates affects the market as a whole. A change in the nation's money supply is another example of a contributor to systematic risk. Global acts such as those of war or terrorism would be considered part of systematic risk.

4 44 Introduction to the Mathematics of Finance On the other hand, unsystematic risk or unique risk is the risk that is particular to an asset or group of assets. For instance, suppose that an investor decides to invest in a company that makes pogs. There are many unsystematic risks here. For example, customers may lose interest in pogs, or the pog company's factory may burn down. The key difference between these two types of risk is that unsystematic risk can be diversified away, whereas systematic risk cannot. For instance, an investor can reduce or eliminate the risk that the pog company's factory will burn down by investing in all pog-making companies. In this way, if one pog factory burns down, another pog company will take up the slack. More generally, an investor can reduce the risk associated with an apathy for pogs by investing in all toy and game companies. After all, when was the last time you heard a child say that he was tired of buying pogs and has decided to put his allowance in the bank instead? A Primer on How Risks Interact To see the effect of individual assets upon risk, consider a portfolio with a single asset, with expected return and risk. The overall risk of the portfolio is also. Let us now add an additional asset to the portfolio. Assume that the asset has expected return and risk. If the weight of asset is then the weight of asset is. Hence, the expected return of the portfolio is and the risk is How does this risk compare to the risks of the individual assets in the portfolio? We may assume (by reversing the numbering if necessary) that.

5 2. Portfolio Management and the Capital Asset Pricing Model 45 t m 1 t 1 t m t t m 1 t Figure 1: Some risk possibilities. Bold curves indicate no short selling. Suppose first that the assets are uncorrelated, that is, portfolio risk is equal to. The This quadratic in is shown on the left in Figure 1. A bit of differentiation shows that the minimum risk occurs at and is equal to Note that since min the minimum risk is positive but can be made less than either of the risks of the individual assets. Now suppose that the assets are peectly positively correlated, that is,. Then the risk is This quadratic is shown in the middle of Figure 1. The minimum risk is actually and occurs at Note that

6 46 Introduction to the Mathematics of Finance and so the minimum-risk portfolio must take a short position in the asset with larger risk. Finally, suppose that the assets are peectly negatively correlated, that is,. Then the risk is This quadratic is shown on the right side of Figure 1. The minimum risk is again and occurs at In this case and so the minimum-risk portfolio does not require short selling. Thus, the case where the assets are peectly negatively correlated seems to be the most promising, in that the risk can be reduced to without short selling. Short selling has its drawbacks, indeed it is not even possible in many cases and when it is, there can be additional costs involved. Of course, it is in general a difficult (or impossible) task to select assets that are peectly negatively correlated with the other assets in a portfolio. 2.2 Two-Asset Portfolios Let us now begin our portfolio analysis in earnest, starting with portfolios that contain only two assets and, with weights and, respectively. It is customary to draw risk-expected return curves with the risk on the horizontal axis and the expected return on the vertical axis. It is also customary to use the standard deviation as a measure of risk for graphing purposes. The expected return of such a portfolio is given by and the risk is

7 2. Portfolio Management and the Capital Asset Pricing Model 47 As before, we assume that the assets are risky, in fact, we assume that For readability let us write The Case Let us first consider the case. In these cases, the expression for simplifies considerably and we have where the plus sign is taken if and the minus sign is taken if. Since, let us for convenience set to get the parametric equations, where ranges over all real numbers. For in the range both weights are nonnegative and so the portfolio has no short positions. Outside this range, exactly one of the weights is negative, indicating that the corresponding asset is held short and the other asset is held long. To help plot the points in the plane, let us temporarily ignore the absolute value sign and consider the parametric equations These are the equations of a straight line in the -plane. When the plus sign is taken and the line passes through the points and. For the line passes through the points and. These lines are plotted in Figure 2.

8 48 Introduction to the Mathematics of Finance ' ' Figure 2: The graphs before taking absolute values Now, the effect of the absolute value sign is simply to flip that part of the line that lies in the left half-plane over the -axis (since ). The resulting plots are shown in Figure 3. The bold portions correspond to points where both weights are nonnegative, that is, no short selling is required. Figure 3: The risk-return lines From the parametric equations (or from our previous discussion), we can deduce the following theorem, which shows again that there are cases where we can reduce the risk of the portfolio to. Theorem 1 For the risk and expected return of the portfolio are given by the parametric equations where is the weight of asset and ranges over all real numbers. For both weights are nonnegative and the portfolio has no short positions. Outside this range, exactly one of the weights is negative, for

9 2. Portfolio Management and the Capital Asset Pricing Model 49 which the corresponding asset is held short. The plots of shown in Figure 3. are Moreover, we have the following cases. 1) When and then all weights give the same (and therefore minimum) risk min. 2) When and then the minimum risk weights are with min min 3) When then the minimum risk weights are with min min Let us emphasize that it is not in general possible to find assets that satisfy and so the previous theorem is more in the nature of a theoretical result. It does show the dependence of the overall risk upon the correlation coefficient of the assets. The Case When the parametric equations for the risk and expected return are Parametrizing as above by letting, gives

10 50 Introduction to the Mathematics of Finance We next observe that since the coefficient of in satisfies and so the expression for is truly quadratic (not linear). The graph of the points is a parabola lying on its side, opening to the right and going through the points and. Figure 4 shows the graph as well as two possible placements of the points and. In the graph on the right, the minimum-risk requires a short position. Figure 4: The risk-return graph Let us assume again that. Differentiating the risk with respect to gives so the minimum-risk point occurs at and the minimum risk is min min The minimum risk portfolio will have no short positions if and only if min.

11 2. Portfolio Management and the Capital Asset Pricing Model 51 We can incorporate the previous cases if we exclude the degenerate case for which the denominator above is. Since the degenerate case is excluded, a little algebra shows that min. (The degenerate case corresponds to min.) Moreover, min min min Here is the final result. Theorem 2 Assume that and let be the correlation coefficient. Assume further that if then. If min denotes the weight of asset required to minimize the risk, then and min min min min Furthermore 1) When and then all weights give the same (and therefore minimum) risk min 2) The condition is equivalent to min and so the minimum risk can be achieved with no short selling. Furthermore, but min min min 3) The condition (except for ) is equivalent to, in which case min

12 52 Introduction to the Mathematics of Finance min which is achieved by holding only asset. 4) The condition is equivalent to min and so short selling of asset is required in order to minimize risk. Furthermore min but min min As with the previous theorem, this result is also in the nature of a theoretical result, but it does show the dependence of the overall risk upon the correlation coefficient of the assets. 2.3 Multi-Asset Portfolios Now let us turn our attention to portfolios with an arbitrary number of assets. The weights of the portfolio can be written in matrix (or vector) form as It is also convenient to define the matrix (or vector) of 's by (this is a script upper-case oh standing for one ) so that the condition can be written as a matrix product where is the transpose of. We will also denote the matrix of expected returns by and the covariance matrix by where Cov

13 2. Portfolio Management and the Capital Asset Pricing Model 53 Note that is the variance of. It can be shown, although we will not do it here, that the matrix is symmetric (that is, ) and positive semidefinite, which means that for any matrix we have. We shall also assume that is invertible, which in this case implies that is positive definite, that is, for any matrix we have. The expected return can now be written as a matrix product and the risk can be written as Var The Markowitz Bullet Let us examine the relationship between the weights of a portfolio and the corresponding risk-expected return point for that portfolio, given by the equations above. Note that we are now referring to risk in the form of the standard deviation. Figure 5 describes the situation is some detail for a portfolio with three assets and this will provide some geometric intuition for the multi-asset case in general. (We will define the terms Markowitz bullet and Markowitz efficient frontier a bit later.) 1 1 w 3 (w 1,w 2,w 3 ) 1 w 1 +w 2 +w 3 =1 w 1 f min min min Markowitz efficient frontier Markowitz bullet w 2 Figure 5: The Markowitz bullet The left-hand portion of Figure 5 shows the -dimensional space in which the weight vectors reside. (In Figure 5 we have of course.) Since the sum of the weights must equal, the weight

14 54 Introduction to the Mathematics of Finance vectors must lie on the hyperplane whose equation is For this is an ordinary plane in -dimensional space, passing through the points, and. For the sake of clarity, the figure shows only that portion of this hyperplane that lies in the positive orthant. This is the portion of the plane that corresponds to portfolios with no short selling. Let us refer to the entire hyperplane as the weight hyperplane. We denote by the function that takes each weight vector in the weight hyperplane to the risk-expected return ordered pair for the corresponding portfolio, that is, where The function is also pictured in Figure 5. Our goal is to determine the image of a straight line in the weight hyperplane under the function. This will help us get an idea of how the function behaves in general. (It is analogous to making a contour map of a function.) The equation of a line in -dimensional space (whether in the weight hyperplane or not) can be written in the parametric form where and where the parameter varies from to. The value corresponds to the point and corresponds to. It is also true that any equation of this form is the equation of a line. Now, for any point on the line, corresponding to a particular value of, the expected return is

15 2. Portfolio Management and the Capital Asset Pricing Model 55 which is a linear function of. This is a critical point. Solving for gives where and are used simply for convenience and where we must assume that the denominator is not. Now let us look at the risk (in the form of the variance) where we have used the letters, and to simplify the expression, which is just a quadratic in. Replacing by its expression in terms of gives which is a quadratic in. Thus, as varies from to and traces out a line in the weight hyperplane, the risk-expected return points trace out a parabola (lying on its side) in the -plane. Taking the square root of the first coordinate produces a curve that we will refer to as a Markowitz curve, although this term is not standard. Thus, straight lines in the weight hyperplane are mapped to Markowitz curves in the -plane under the function. Note that Markowitz curves are not parabolas. Figure 6 shows an example of a Markowitz curve generated using Microsoft Excel. For future reference, we note now that the data used to plot this curve are

16 56 Introduction to the Mathematics of Finance Markowitz Bullet Return Risk Figure 6: A Markowitz bullet The Shape of a Markowitz Curve It is important to make a clear distinction between the parabola traced out by and the Markowitz curve traced out by the points, as pictured in Figure 6. To get a feel for the differences in more familiar territory, consider the functions and for. The first graph is a parabola. The slope of the tangent lines to this parabola are given by the derivative and these slopes increase without bound as tends to. On the other hand, for the function, for large values of the first term dominates the others and so The graph of the equation is a pair of straight lines. This shows that as tends to the graph of flattens, unlike the case of a parabola. In particular, the derivative is

17 2. Portfolio Management and the Capital Asset Pricing Model 57 Squaring this makes it easier to take the limit lim lim so we see that approaches as approaches. Thus, unlike parabolas Markowitz curves flatten out as we move to the right. One of the implications of this fact, which is important to the capital asset pricing model, is that (looking ahead to Figure 9) if is too large, there is no tangent line from the point to the upper portion of the Markowitz curve. The Point of Minimum Risk Let us denote the point of minimum risk by minmin. We will be content with finding the portfolio weights (in the weight hyperplane) that correspond to this point. For any particular case, these weights can easily be plugged into the formulas for and to get the actual point. (As the reader will see, the general formulas can get a bit messy.) The next theorem gives the minimum-risk weights. The proof uses the technique of Lagrange multipliers, which can be found in any standard multivariable calculus book, so we will not go into the details here. The reader may skim over the few proofs that require this technique if desired. Theorem 3 A portfolio with minimum risk has weights given by Note that the denominator is a number and is just the sum of the components in the numerator. Proof. We seek to minimize the expression subject to the constraint

18 58 Introduction to the Mathematics of Finance According to the technique of Lagrange multipliers, we must take the partial derivatives with respect to each and of the function and set them equal to. We leave it as an exercise to show that this results in the equation and so Substituting this into the constraint (and using the fact that and are symmetric) gives and so we get the desired result. The Markowitz Efficient Frontier The set of points min that gives the minimum risk for each expected return is called the Markowitz efficient frontier ( frontier" is another word for boundary). The next theorem describes this set of points. While the formula is a bit messy, there is an important lesson here. Namely, the minimum-risk weights are a linear function of the expected return. This means that as the expected return takes on all values from to, the minimum-risk weights trace out a straight line in the weight hyperplane and the corresponding points min trace out a Markowitz curve! In other words, the Markowitz efficient frontier is a Markowitz curve. The weight line that corresponds to the Markowitz curve is called the minimum-risk weight line. Theorem 4 For a given expected return, the portfolio with minimum risk has weights given by

19 2. Portfolio Management and the Capital Asset Pricing Model 59 In particular, each weight is a linear function of. Proof. In this case, we seek to minimize the expression subject to the constraints and This is done by setting the partial derivatives of the following function to This results in the matrix equation and so Substituting the expression for gives the system of equations into the matrix form of the constraints Cramer's rule can now be used to obtain a formula for and. Substituting this into the expression for gives the desired result. We leave all details to the reader as an exercise.

20 60 Introduction to the Mathematics of Finance An ordered pair is said to be an attainable point if it has the form for some portfolio. Since the Markowitz efficient frontier contains the points of minimum risk, all attainable points must lie on or to the right (corresponding to greater risk) of some point on this frontier. In other words, the attainable points are contained in the shaded region on the right-hand side of Figure 5. This region (including the frontier) is known as the Markowitz bullet, due to its shape. To explain the significance of the Markowitz efficient frontier, we make the following definition. Definition Let and be attainable points. Then dominates if and In words, return. has smaller or equal risk and larger or equal expected Theorem 5 Any attainable point is dominated by an attainable point on the Markowitz efficient frontier. Thus, investors who seek to minimize risk for any expected return need only look on the Markowitz efficient frontier. EXAMPLE 1 Let us sketch the computations needed in order to get the Markowitz bullet in Figure 6. The data are as follows: Since the computations are a bit tedious, they are best done with some sort of software program, such as Microsoft Excel. Figure 7 shows a portion of an Excel spreadsheet that has the required computations. The user need only fill in the gray cells and the rest will adjust automatically.

21 2. Portfolio Management and the Capital Asset Pricing Model 61 Capital Asset Pricing Model-Fill In Grey Cells User Data Returns i Risks i Correlation i= i= i= C=(c i,j )=( i,j i j ) j=1 j=2 j=3 i= i= i= Inverse of C Min Risk Point OC -1 = OC -1 O t = W= = WC= WCW t = = Min Risk Line MC -1 = MC -1 O t = OC -1 M t = MC -1 M t = Denom Det= Figure 7: Excel worksheet Referring to Figure 7, the point of minimum risk is given in Theorem 3 by These weights can be used to get the expected return and risk and Thus, the minimum-risk point is min min % % Next, we compute the minimum risk for a given expected return. The formula for the minimum risk is given in Theorem 4. All matrix products are computed in Figure 7, and so is the denominator, which does not

22 62 Introduction to the Mathematics of Finance depend on. Figure 8 shows the computation of the minimum risk for three different expected returns. Figure 8: Computing minimum risk for a given expected return The Capital Asset Pricing Model Now that we have discussed the Markowitz portfolio theory, we are ready to take a look at the Capital Asset Pricing Model, or CAPM (pronounced Cap M ). The major factor that turns Markowitz portfolio theory into capital market theory is the inclusion of a riskfree asset in the model. (Recall that up to now we have been assuming that all assets are risky.) As we have said, a riskfree asset is one that has risk, that is, variance. Thus, its risk-expected return point lies on the vertical axis, as shown in Figure 9. The inclusion of a riskfree asset into the Markowitz portfolio theory is generally regarded as the contribution of William Sharpe, for which he won the Nobel Prize, but John Lintner and J. Mossin developed similar theories independently and at about the same time. For these reasons, the theory is sometimes referred to as the Sharpe Lintner Mossin (SLM) capital asset pricing model. The basic idea behind the CAPM is that an investor can improve his or her risk/expected return balance by investing partially in a portfolio of risky assets and partially in a riskfree asset. Let us see why this is true. Imagine a portfolio that consists of a riskfree asset with weight and the risky assets as before, with weights. Note

23 2. Portfolio Management and the Capital Asset Pricing Model 63 that now the sum of the weights of the risky assets will be at most. In fact, we have risky The expected return of the complete portfolio is risky and since the variance of the riskfree asset is, the return constant. Hence, its covariance with any other return is and so Var Var risky is a Hence risky We also want to consider the portfolio formed by removing the riskfree asset and beefing up the weights of the risky assets by the same factor to make the sum of these weights equal to. Let us call this portfolio the derived risky portfolio (a nonstandard term). For example, if the original portfolio is composed of a riskfree asset with weight risky asset with weight risky asset with weight 5 then the sum of the risky weights is so the derived risky portfolio consists of the risky asset with weight risky asset with weight 5

24 64 Introduction to the Mathematics of Finance which has a total weight of. Let us denote the expected return of the derived risky portfolio by and the risk by. It follows that and der risky risky der risky der Var der risky riskyvar risky Thus risky der risky der or since risky riskyder risky der (1) As risky ranges over all real numbers, equations (1) trace out a straight line. Solving the second equation for risky and using that in the first equation, we get the equation Figure 9 shows this line. der (2) der

25 2. Portfolio Management and the Capital Asset Pricing Model 65 Capital market portfolio m m Capital market line der der Figure 9: The capital market line It is clear that if risky then and if risky then der der Moreover, the point derder corresponding to risky, being the risk-expected return point for a purely risky portfolio, must lie in the Markowitz bullet. So where do we stand? An investor who invests in a riskfree asset along with some risky assets will have risk-expected return point lying somewhere on the line joining the points and der der. But it is clear from the geometry that among all lines joining the point with various points derder in the Markowitz bullet, the line that produces the points with the highest expected return for a given risk is the tangent line to the upper portion of the Markowitz bullet, as shown in Figure 10. Investment Portfolio Capital market line A B Capital market portfolio Acceptable risk Figure 10: The investment portfolio for a given level of risk

27 2. Portfolio Management and the Capital Asset Pricing Model 67 that correspond to the market portfolio's risk and expected return are given in the next theorem. Theorem 6 For any expected riskfree return portfolio has weights, the capital market Note that the denominator is just a number, being the sum of the coordinates of the vector in the numerator. Proof. For any point in the Markowitz bullet, the slope of the line from to is It is intuitively clear that the point of tangency is the point with the property that this slope is a maximum among all points in the Markowitz bullet. So we seek to maximize subject to the constraint that. Using Lagrange multipliers once again, we must take the partial derivatives of the following function and set the results to : We leave it as an exercise to show that the resulting equations are This can be cleaned up to get where is the th row of the covariance matrix. This can be written Since this holds for all, we have

28 68 Introduction to the Mathematics of Finance Taking transposes and recalling that gives Multiplying on the right by and recalling that, we get or and so We can now use this value of in an earlier equation to get This can be rewritten as Multiplying on the right by and noting that we get Using this in the previous equation gives as desired. To illustrate, let us continue Example 1 to derive the market portfolio. EXAMPLE 2 Continuing Example 1, Figure 11 shows more of our Excel worksheet. This portion computes the market portfolio's riskexpected return based on various riskfree rates (in this case only three rates).

29 2. Portfolio Management and the Capital Asset Pricing Model 69 Figure 11: The market portfolio For instance, a riskfree return on investment of expected return of leads to an and a risk of More on the Market Portfolio According to our theory, all rational investors will invest in the market portfolio, along with some measure of riskfree asset. This has some profound consequences for this portfolio. First, the market portfolio must contain all possible assets! For if an asset is not in the portfolio, no one will want to purchase it and so the asset will wither and die. Since the market portfolio contains all assets, the portfolio has no unsystematic risk this risk has been completely diversified out. Thus, all risk associated with the market portfolio is systematic risk. In practice, the market portfolio can be approximated by a much smaller number of assets. Studies have indicated that a portfolio can achieve a degree of diversification approaching that of a true market portfolio if it contains a well-chosen set of perhaps 20 to 40 securities. We will use the term market portfolio to refer to an unspecified portfolio that is highly diversified and thus can be considered as essentially free of unsystematic risk. The Risk Return of an Asset Compared with the Market Portfolio Let us consider any particular asset in the market portfolio. We want to use the best linear predictor, discussed in Chapter 1, to approximate the return of asset by a linear function of the return of the entire market portfolio. According to Theorem 9 of Chapter 1, we can write

30 70 Introduction to the Mathematics of Finance where Cov and is the error (residual random variable). The coefficient is the beta of the asset's return with respect to the market portfolio's return and is the slope of the linear regression line. To get a feel for what to expect, Figure 12 shows the best linear predictor in the case of a relatively large beta and three magnitudes of error, ranging from very small to rather large. R k R k R k R M R M R M Figure 12: A large beta and different magnitudes of error Because the beta is large, in all three cases when the market return fluctuates a certain amount, the asset's return fluctuates a relatively larger amount. Put another way, if the market returns should fluctuate over a specific range of values (as measured by the variance, for example), the asset returns will fluctuate over a larger range of values (as measured by the variance). Thus, the market risk is magnified in the asset risk. Figure 13 shows the best linear predictor when the beta is small, again with three magnitudes of error.

31 2. Portfolio Management and the Capital Asset Pricing Model 71 R k R k R k R M R M R M Figure 13: A small beta and different magnitudes of error Because the beta is small, in all three cases when the market return fluctuates a certain amount, the asset's return fluctuates a relatively smaller amount. Thus, the market risk is demagnified in the asset risk. It is intuitively clear then that an asset's systematic risk, that is, the risk that comes from the asset's relationship to the market portfolio (whose risk is purely systematic) is related in some way to the beta of the asset. In addition, it can be seen from the graphs in Figures 12 and 13 that there is another factor that contributes to the asset's risk, a factor that has nothing whatsoever to do with the market risk. It is the error. The larger the error, as measured by its variance Vaor example, the larger the uncertainty in the asset's expected return. Now let us turn to the mathematics to see if we can justify these statements. In fact, the BLP will provide formulas for the expected return and the risk of the individual asset in terms of the beta. The Risk As to the risk, we leave it as an exercise to show that Cov and so the risk associated with the asset is (since is a constant) Var Var Var Var Thus, the quantity, which is referred to as the systematic risk of the asset, is proportional to the market risk, with a proportionality factor of.

32 72 Introduction to the Mathematics of Finance The remaining portion of the asset's risk is the term Var, which is precisely the measure of the error that we discussed earlier. This is called the unsystematic risk or unique risk of the asset. According to economic theory, when adding an asset to a diversified portfolio, the unique risk of that asset is canceled out by other assets in the portfolio. Hence, the unique risk should not be considered when evaluating the risk-return peormance of the asset and so the asset's beta becomes the focal point for the risk-return analysis of an asset. The Expected Return To justify this viewpoint further consider the expected return of the market portfolio and the expected return of the individual asset where is the matrix with a in the th position and s elsewhere. To relate these two quantities, we need an expression for. Recall that the weights of the market portfolio are given in Theorem 6 by Since the denominator is just a constant, let us denote its reciprocal by. Thus Solving for gives We can now write

33 2. Portfolio Management and the Capital Asset Pricing Model 73 Also Cov Now, the reader may notice a resemblance between some of these terms and the beta Cov Solving the previous equations for the numerator and denominator of gives Finally, solving this for Cov gives Let us collect these important formulas in a theorem. Theorem 7 The expected return and risk of an asset in the market portfolio is related to the asset's beta with respect to the market portfolio as follows: and (3)

34 74 Introduction to the Mathematics of Finance Var where is the error (residual random variable). The expression for the expected return justifies our earlier discussion: An asset's expected return depends only on the asset's systematic risk (through its beta) and not on its unique risk Var. This justifies considering only the term in assessing the asset's risk relative to the market portfolio. Since is positive under normal conditions, the slope of the linear relation is positive, meaning that large betas imply large expected returns and vice versa. This makes sense the more (systematic) risk in an asset the higher should be its expected return under market equilibrium. Of course, there is no law that says that higher risk should be rewarded by higher expected return. However, this is the condition of market equilibrium. If an asset is returning less than the market feels is reasonable with respect to the asset's perceived risk, then no one will buy that asset and its price will decline, thus increasing the asset's return. Similarly, if the asset is returning more than the market feels is required by the asset's level of risk, then more investors will buy the asset, thus raising its price and lowering its expected return. The mathematics bears this out. For instance, if the asset's systematic risk is less than the risk in the market portfolio, that is, if or equivalently if then the asset's return satisfies that is, its expected return is less than that of the market portfolio. On the other hand, if then and if then, just as we would expect in a market that is in equilibrium. The graph of the line in equation (3) is called the security market line or SML for short. The equation shows that the expected return of an asset is equal to the return of the riskfree asset plus the risk premium of the asset.

35 2. Portfolio Management and the Capital Asset Pricing Model 75 EXAMPLE 3 Suppose that the riskfree rate is 3% and that the market portfolio's risk is 12%. Consider the following assets and their betas Asset Beta Since the security market equation is We can now compute the expected returns under market equilibrium Asset Beta Expected Return % % % % % The expected returns in the previous table are the values that the market will sustain based on the market portfolio's overall systematic risk (and the riskfree rate). For example, since asset has a beta less than, it has a smaller risk than the market portfolio. Therefore, the market will sustain a lower expected return than that of the market portfolio in this case % rather than %. Asset has the same systematic risk as the market portfolio so the market will sustain an expected return equal to that of the market portfolio. Exercises 1. What is the beta of the market portfolio? Can a portfolio have any real number as its? 2. For a riskfree rate of 4% and a market portfolio expected return of 8% calculate the equation of the security market line.

36 76 Introduction to the Mathematics of Finance 3. Show that the parametric equations are the equations of a straight line in the -plane. (Here is the parameter and ranges over all real numbers. Take the plus sign first and then the minus sign.) 4. For we have for. If the risk is then compute the expected return. 5. Under the assumption that and show that min 6. If is the error in the best linear predictor of an asset with respect to the market portfolio, show that Cov. 7. Show that the regression lines for all assets in the market portfolio go through a single point. What is that point? 8. Let be the market portfolio, where asset has weight. Write the best linear predictor of as BLP Consider the first two assets and, with their respective weights and. The return from these two assets is If the best linear predictor is w BLP what is the relationship between, and and between, and? Can you generalize this result to any subset of assets in the market portfolio, that is, to any subportfolio? 9. Verify the data in Figure 14 (at least to a few decimal places). For a 5% return, show that the minimum risk is. If the riskfree rate is 3% show that the market portfolio has weights and risk return.

37 2. Portfolio Management and the Capital Asset Pricing Model 77 Capital Asset Pricing Model-Fill In Grey Cells User Data Returns i Risks i Correlation i= i= i= C=(c i,j )=( i,j i j ) j=1 j=2 j=3 i= i= i= Inverse of C Min Risk Point OC -1 = OC -1 O t = W=OC -1 /OC -1 O t = =MW t = WC= =WCW t = = For Min Risk Line MC -1 = MC -1 O t = OC -1 M t = MC -1 M t = Denom Det= Figure 14

38

### 1 Portfolio mean and variance

Copyright c 2005 by Karl Sigman Portfolio mean and variance Here we study the performance of a one-period investment X 0 > 0 (dollars) shared among several different assets. Our criterion for measuring

### 1 Capital Asset Pricing Model (CAPM)

Copyright c 2005 by Karl Sigman 1 Capital Asset Pricing Model (CAPM) We now assume an idealized framework for an open market place, where all the risky assets refer to (say) all the tradeable stocks available

### CHAPTER 10 RISK AND RETURN: THE CAPITAL ASSET PRICING MODEL (CAPM)

CHAPTER 10 RISK AND RETURN: THE CAPITAL ASSET PRICING MODEL (CAPM) Answers to Concepts Review and Critical Thinking Questions 1. Some of the risk in holding any asset is unique to the asset in question.

### Answers to Concepts in Review

Answers to Concepts in Review 1. A portfolio is simply a collection of investments assembled to meet a common investment goal. An efficient portfolio is a portfolio offering the highest expected return

### Lecture 2: Equilibrium

Lecture 2: Equilibrium Investments FIN460-Papanikolaou Equilibrium 1/ 33 Overview 1. Introduction 2. Assumptions 3. The Market Portfolio 4. The Capital Market Line 5. The Security Market Line 6. Conclusions

### The Capital Asset Pricing Model

Finance 400 A. Penati - G. Pennacchi The Capital Asset Pricing Model Let us revisit the problem of an investor who maximizes expected utility that depends only on the expected return and variance (or standard

### CHAPTER 7: OPTIMAL RISKY PORTFOLIOS

CHAPTER 7: OPTIMAL RIKY PORTFOLIO PROLEM ET 1. (a) and (e).. (a) and (c). After real estate is added to the portfolio, there are four asset classes in the portfolio: stocks, bonds, cash and real estate.

### Lecture 1: Asset Allocation

Lecture 1: Asset Allocation Investments FIN460-Papanikolaou Asset Allocation I 1/ 62 Overview 1. Introduction 2. Investor s Risk Tolerance 3. Allocating Capital Between a Risky and riskless asset 4. Allocating

### Chapter 7 Portfolio Theory and Other Asset Pricing Models

Chapter 7 Portfolio Theory and Other sset Pricing Models NSWERS TO END-OF-CHPTER QUESTIONS 7-1 a. portfolio is made up of a group of individual assets held in combination. n asset that would be relatively

### Solution: The optimal position for an investor with a coefficient of risk aversion A = 5 in the risky asset is y*:

Problem 1. Consider a risky asset. Suppose the expected rate of return on the risky asset is 15%, the standard deviation of the asset return is 22%, and the risk-free rate is 6%. What is your optimal position

### Chap 3 CAPM, Arbitrage, and Linear Factor Models

Chap 3 CAPM, Arbitrage, and Linear Factor Models 1 Asset Pricing Model a logical extension of portfolio selection theory is to consider the equilibrium asset pricing consequences of investors individually

### On the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information

Finance 400 A. Penati - G. Pennacchi Notes on On the Efficiency of Competitive Stock Markets Where Traders Have Diverse Information by Sanford Grossman This model shows how the heterogeneous information

### Portfolio Performance Measures

Portfolio Performance Measures Objective: Evaluation of active portfolio management. A performance measure is useful, for example, in ranking the performance of mutual funds. Active portfolio managers

### Lecture 2: Delineating efficient portfolios, the shape of the meanvariance frontier, techniques for calculating the efficient frontier

Lecture 2: Delineating efficient portfolios, the shape of the meanvariance frontier, techniques for calculating the efficient frontier Prof. Massimo Guidolin Portfolio Management Spring 2016 Overview The

### Chapter 15 Introduction to Linear Programming

Chapter 15 Introduction to Linear Programming An Introduction to Optimization Spring, 2014 Wei-Ta Chu 1 Brief History of Linear Programming The goal of linear programming is to determine the values of

### CFA Examination PORTFOLIO MANAGEMENT Page 1 of 6

PORTFOLIO MANAGEMENT A. INTRODUCTION RETURN AS A RANDOM VARIABLE E(R) = the return around which the probability distribution is centered: the expected value or mean of the probability distribution of possible

### CHAPTER 11: ARBITRAGE PRICING THEORY

CHAPTER 11: ARBITRAGE PRICING THEORY 1. The revised estimate of the expected rate of return on the stock would be the old estimate plus the sum of the products of the unexpected change in each factor times

### CAPM, Arbitrage, and Linear Factor Models

CAPM, Arbitrage, and Linear Factor Models CAPM, Arbitrage, Linear Factor Models 1/ 41 Introduction We now assume all investors actually choose mean-variance e cient portfolios. By equating these investors

### The Not-Formula Book for C1

Not The Not-Formula Book for C1 Everything you need to know for Core 1 that won t be in the formula book Examination Board: AQA Brief This document is intended as an aid for revision. Although it includes

### SAMPLE MID-TERM QUESTIONS

SAMPLE MID-TERM QUESTIONS William L. Silber HOW TO PREPARE FOR THE MID- TERM: 1. Study in a group 2. Review the concept questions in the Before and After book 3. When you review the questions listed below,

### The Classical Linear Regression Model

The Classical Linear Regression Model 1 September 2004 A. A brief review of some basic concepts associated with vector random variables Let y denote an n x l vector of random variables, i.e., y = (y 1,

### DO NOT COPY PORTFOLIO SELECTION AND THE CAPITAL ASSET PRICING MODEL

UVA-F-1604 Rev. Mar. 4, 011 PORTFOLIO SELECTION AND THE CAPITAL ASSET PRICING MODEL What portfolio would you recommend to a 8-year-old who has just been promoted to a management position, and what portfolio

### D&D, sect : CAPM without risk free asset Main points:

D&D, sect 74 77: CAPM without risk free asset Main points: Consider N risky assets, N > 2, no risk free asset Then the frontier portfolio set is an hyperbola (Mentioned without proof on p 11 of 1 September)

### Review for Exam 2. Instructions: Please read carefully

Review for Exam 2 Instructions: Please read carefully The exam will have 25 multiple choice questions and 5 work problems You are not responsible for any topics that are not covered in the lecture note

### Portfolio Optimization Part 1 Unconstrained Portfolios

Portfolio Optimization Part 1 Unconstrained Portfolios John Norstad j-norstad@northwestern.edu http://www.norstad.org September 11, 2002 Updated: November 3, 2011 Abstract We recapitulate the single-period

### Mean-Variance Portfolio Analysis and the Capital Asset Pricing Model

Mean-Variance Portfolio Analysis and the Capital Asset Pricing Model 1 Introduction In this handout we develop a model that can be used to determine how a risk-averse investor can choose an optimal asset

### 3.3. Solving Polynomial Equations. Introduction. Prerequisites. Learning Outcomes

Solving Polynomial Equations 3.3 Introduction Linear and quadratic equations, dealt within Sections 3.1 and 3.2, are members of a class of equations, called polynomial equations. These have the general

### FE670 Algorithmic Trading Strategies. Stevens Institute of Technology

FE670 Algorithmic Trading Strategies Lecture 6. Portfolio Optimization: Basic Theory and Practice Steve Yang Stevens Institute of Technology 10/03/2013 Outline 1 Mean-Variance Analysis: Overview 2 Classical

### AFM 472. Midterm Examination. Monday Oct. 24, 2011. A. Huang

AFM 472 Midterm Examination Monday Oct. 24, 2011 A. Huang Name: Answer Key Student Number: Section (circle one): 10:00am 1:00pm 2:30pm Instructions: 1. Answer all questions in the space provided. If space

### 11.1. Objectives. Component Form of a Vector. Component Form of a Vector. Component Form of a Vector. Vectors and the Geometry of Space

11 Vectors and the Geometry of Space 11.1 Vectors in the Plane Copyright Cengage Learning. All rights reserved. Copyright Cengage Learning. All rights reserved. 2 Objectives! Write the component form of

### December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS

December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation

### Lecture 2. Marginal Functions, Average Functions, Elasticity, the Marginal Principle, and Constrained Optimization

Lecture 2. Marginal Functions, Average Functions, Elasticity, the Marginal Principle, and Constrained Optimization 2.1. Introduction Suppose that an economic relationship can be described by a real-valued

### High School Mathematics Algebra

High School Mathematics Algebra This course is designed to give students the foundation of understanding algebra at a moderate pace. Essential material will be covered to prepare the students for Geometry.

### OPRE 6201 : 2. Simplex Method

OPRE 6201 : 2. Simplex Method 1 The Graphical Method: An Example Consider the following linear program: Max 4x 1 +3x 2 Subject to: 2x 1 +3x 2 6 (1) 3x 1 +2x 2 3 (2) 2x 2 5 (3) 2x 1 +x 2 4 (4) x 1, x 2

### Module 7 Asset pricing models

1. Overview Module 7 Asset pricing models Prepared by Pamela Peterson Drake, Ph.D., CFA Asset pricing models are different ways of interpreting how investors value investments. Most models are based on

### AP Physics 1 and 2 Lab Investigations

AP Physics 1 and 2 Lab Investigations Student Guide to Data Analysis New York, NY. College Board, Advanced Placement, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks

### v 1. v n R n we have for each 1 j n that v j v n max 1 j n v j. i=1

1. Limits and Continuity It is often the case that a non-linear function of n-variables x = (x 1,..., x n ) is not really defined on all of R n. For instance f(x 1, x 2 ) = x 1x 2 is not defined when x

### Concepts in Investments Risks and Returns (Relevant to PBE Paper II Management Accounting and Finance)

Concepts in Investments Risks and Returns (Relevant to PBE Paper II Management Accounting and Finance) Mr. Eric Y.W. Leung, CUHK Business School, The Chinese University of Hong Kong In PBE Paper II, students

### WEB APPENDIX. Calculating Beta Coefficients. b Beta Rise Run Y 7.1 1 8.92 X 10.0 0.0 16.0 10.0 1.6

WEB APPENDIX 8A Calculating Beta Coefficients The CAPM is an ex ante model, which means that all of the variables represent before-thefact, expected values. In particular, the beta coefficient used in

### Section 1.1. Introduction to R n

The Calculus of Functions of Several Variables Section. Introduction to R n Calculus is the study of functional relationships and how related quantities change with each other. In your first exposure to

### Regression III: Advanced Methods

Lecture 5: Linear least-squares Regression III: Advanced Methods William G. Jacoby Department of Political Science Michigan State University http://polisci.msu.edu/jacoby/icpsr/regress3 Simple Linear Regression

### Week 7 - Game Theory and Industrial Organisation

Week 7 - Game Theory and Industrial Organisation The Cournot and Bertrand models are the two basic templates for models of oligopoly; industry structures with a small number of firms. There are a number

### Chapter 5. Risk and Return. Copyright 2009 Pearson Prentice Hall. All rights reserved.

Chapter 5 Risk and Return Learning Goals 1. Understand the meaning and fundamentals of risk, return, and risk aversion. 2. Describe procedures for assessing and measuring the risk of a single asset. 3.

### Midterm Exam:Answer Sheet

Econ 497 Barry W. Ickes Spring 2007 Midterm Exam:Answer Sheet 1. (25%) Consider a portfolio, c, comprised of a risk-free and risky asset, with returns given by r f and E(r p ), respectively. Let y be the

### 15.401 Finance Theory

Finance Theory MIT Sloan MBA Program Andrew W. Lo Harris & Harris Group Professor, MIT Sloan School Lecture 13 14 14: : Risk Analytics and Critical Concepts Motivation Measuring Risk and Reward Mean-Variance

### 22.1. Markowitz Portfolio Theory

NPTEL Course Course Title: Security Analysis and Portfolio Management Course Coordinator: Dr. Jitendra Mahakud Module-11 Session-22 Markowitz Portfolio Theory 22.1. Markowitz Portfolio Theory The pioneering

### 2. Mean-variance portfolio theory

2. Mean-variance portfolio theory (2.1) Markowitz s mean-variance formulation (2.2) Two-fund theorem (2.3) Inclusion of the riskfree asset 1 2.1 Markowitz mean-variance formulation Suppose there are N

### Chapter 1. Introduction to Portfolio Theory. 1.1 Portfolios of Two Risky Assets

Chapter 1 Introduction to Portfolio Theory Updated: August 9, 2013. This chapter introduces modern portfolio theory in a simplified setting where there are only two risky assets and a single risk-free

### THE FUNDAMENTAL THEOREM OF ARBITRAGE PRICING

THE FUNDAMENTAL THEOREM OF ARBITRAGE PRICING 1. Introduction The Black-Scholes theory, which is the main subject of this course and its sequel, is based on the Efficient Market Hypothesis, that arbitrages

### Basic Financial Tools: A Review. 3 n 1 n. PV FV 1 FV 2 FV 3 FV n 1 FV n 1 (1 i)

Chapter 28 Basic Financial Tools: A Review The building blocks of finance include the time value of money, risk and its relationship with rates of return, and stock and bond valuation models. These topics

### Numerical Analysis Lecture Notes

Numerical Analysis Lecture Notes Peter J. Olver 5. Inner Products and Norms The norm of a vector is a measure of its size. Besides the familiar Euclidean norm based on the dot product, there are a number

### In recent years, Federal Reserve (Fed) policymakers have come to rely

Long-Term Interest Rates and Inflation: A Fisherian Approach Peter N. Ireland In recent years, Federal Reserve (Fed) policymakers have come to rely on long-term bond yields to measure the public s long-term

### 1111: Linear Algebra I

1111: Linear Algebra I Dr. Vladimir Dotsenko (Vlad) Lecture 3 Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Lecture 3 1 / 12 Vector product and volumes Theorem. For three 3D vectors u, v, and w,

### 6 EXTENDING ALGEBRA. 6.0 Introduction. 6.1 The cubic equation. Objectives

6 EXTENDING ALGEBRA Chapter 6 Extending Algebra Objectives After studying this chapter you should understand techniques whereby equations of cubic degree and higher can be solved; be able to factorise

### Chapter 5. Risk and Return. Learning Goals. Learning Goals (cont.)

Chapter 5 Risk and Return Learning Goals 1. Understand the meaning and fundamentals of risk, return, and risk aversion. 2. Describe procedures for assessing and measuring the risk of a single asset. 3.

### Tangent and normal lines to conics

4.B. Tangent and normal lines to conics Apollonius work on conics includes a study of tangent and normal lines to these curves. The purpose of this document is to relate his approaches to the modern viewpoints

### Some Notes on Taylor Polynomials and Taylor Series

Some Notes on Taylor Polynomials and Taylor Series Mark MacLean October 3, 27 UBC s courses MATH /8 and MATH introduce students to the ideas of Taylor polynomials and Taylor series in a fairly limited

### Lecture 6: Arbitrage Pricing Theory

Lecture 6: Arbitrage Pricing Theory Investments FIN460-Papanikolaou APT 1/ 48 Overview 1. Introduction 2. Multi-Factor Models 3. The Arbitrage Pricing Theory FIN460-Papanikolaou APT 2/ 48 Introduction

### What are the place values to the left of the decimal point and their associated powers of ten?

The verbal answers to all of the following questions should be memorized before completion of algebra. Answers that are not memorized will hinder your ability to succeed in geometry and algebra. (Everything

### The Characteristic Polynomial

Physics 116A Winter 2011 The Characteristic Polynomial 1 Coefficients of the characteristic polynomial Consider the eigenvalue problem for an n n matrix A, A v = λ v, v 0 (1) The solution to this problem

### 5 =5. Since 5 > 0 Since 4 7 < 0 Since 0 0

a p p e n d i x e ABSOLUTE VALUE ABSOLUTE VALUE E.1 definition. The absolute value or magnitude of a real number a is denoted by a and is defined by { a if a 0 a = a if a

### Factoring Patterns in the Gaussian Plane

Factoring Patterns in the Gaussian Plane Steve Phelps Introduction This paper describes discoveries made at the Park City Mathematics Institute, 00, as well as some proofs. Before the summer I understood

### THE CAPITAL ASSET PRICING MODEL

THE CAPITAL ASSET PRICING MODEL Szabolcs Sebestyén szabolcs.sebestyen@iscte.pt Master in Finance INVESTMENTS Sebestyén (ISCTE-IUL) CAPM Investments 1 / 30 Outline 1 Introduction 2 The Traditional Approach

### Enhancing the Teaching of Statistics: Portfolio Theory, an Application of Statistics in Finance

Page 1 of 11 Enhancing the Teaching of Statistics: Portfolio Theory, an Application of Statistics in Finance Nicolas Christou University of California, Los Angeles Journal of Statistics Education Volume

### Simple Regression Theory II 2010 Samuel L. Baker

SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the

### FINANCIAL ECONOMICS OPTION PRICING

OPTION PRICING Options are contingency contracts that specify payoffs if stock prices reach specified levels. A call option is the right to buy a stock at a specified price, X, called the strike price.

### DERIVATIVES AS MATRICES; CHAIN RULE

DERIVATIVES AS MATRICES; CHAIN RULE 1. Derivatives of Real-valued Functions Let s first consider functions f : R 2 R. Recall that if the partial derivatives of f exist at the point (x 0, y 0 ), then we

### 5.4 The Quadratic Formula

Section 5.4 The Quadratic Formula 481 5.4 The Quadratic Formula Consider the general quadratic function f(x) = ax + bx + c. In the previous section, we learned that we can find the zeros of this function

### 2.2. Instantaneous Velocity

2.2. Instantaneous Velocity toc Assuming that your are not familiar with the technical aspects of this section, when you think about it, your knowledge of velocity is limited. In terms of your own mathematical

### Linear Programming I

Linear Programming I November 30, 2003 1 Introduction In the VCR/guns/nuclear bombs/napkins/star wars/professors/butter/mice problem, the benevolent dictator, Bigus Piguinus, of south Antarctica penguins

### Mathematics Course 111: Algebra I Part IV: Vector Spaces

Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are

### Copyrighted Material. Chapter 1 DEGREE OF A CURVE

Chapter 1 DEGREE OF A CURVE Road Map The idea of degree is a fundamental concept, which will take us several chapters to explore in depth. We begin by explaining what an algebraic curve is, and offer two

### Polynomials. Dr. philippe B. laval Kennesaw State University. April 3, 2005

Polynomials Dr. philippe B. laval Kennesaw State University April 3, 2005 Abstract Handout on polynomials. The following topics are covered: Polynomial Functions End behavior Extrema Polynomial Division

### Mathematics 31 Pre-calculus and Limits

Mathematics 31 Pre-calculus and Limits Overview After completing this section, students will be epected to have acquired reliability and fluency in the algebraic skills of factoring, operations with radicals

### An Introduction to Utility Theory

An Introduction to Utility Theory John Norstad j-norstad@northwestern.edu http://www.norstad.org March 29, 1999 Updated: November 3, 2011 Abstract A gentle but reasonably rigorous introduction to utility

### An Introduction to Portfolio Theory

An Introduction to Portfolio Theory John Norstad j-norstad@northwestern.edu http://www.norstad.org April 10, 1999 Updated: November 3, 2011 Abstract We introduce the basic concepts of portfolio theory,

### Instructor s Manual Chapter 12 Page 144

Chapter 12 1. Suppose that your 58-year-old father works for the Ruffy Stuffed Toy Company and has contributed regularly to his company-matched savings plan for the past 15 years. Ruffy contributes \$0.50

### 2. Simple Linear Regression

Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according

### 15.062 Data Mining: Algorithms and Applications Matrix Math Review

.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop

### The Simple Linear Regression Model: Specification and Estimation

Chapter 3 The Simple Linear Regression Model: Specification and Estimation 3.1 An Economic Model Suppose that we are interested in studying the relationship between household income and expenditure on

### Chapter 7 Risk and Return: Portfolio Theory and Asset Pricing Models ANSWERS TO END-OF-CHAPTER QUESTIONS

Chapter 7 Risk and Return: Portfolio Theory and Asset Pricing odels ANSWERS TO END-OF-CHAPTER QUESTIONS 7-1 a. A portfolio is made up of a group of individual assets held in combination. An asset that

### Continuous Functions, Smooth Functions and the Derivative

UCSC AMS/ECON 11A Supplemental Notes # 4 Continuous Functions, Smooth Functions and the Derivative c 2004 Yonatan Katznelson 1. Continuous functions One of the things that economists like to do with mathematical

### DIVERSIFICATION, REBALANCING, AND THE GEOMETRIC MEAN FRONTIER. William J. Bernstein. David Wilkinson

DIVERSIFICATION, REBALANCING, AND THE GEOMETRIC MEAN FRONTIER William J. Bernstein wbern@mail.coos.or.us and David Wilkinson DaveWilk@worldnet.att.net November 24, 1997 Abstract The effective (geometric

### CHAPTER 9: THE CAPITAL ASSET PRICING MODEL

CHAPTER 9: THE CAPITAL ASSET PRICING MODEL PROBLEM SETS 1. E(r P ) = r f + β P [E(r M ) r f ] 18 = 6 + β P(14 6) β P = 12/8 = 1.5 2. If the security s correlation coefficient with the market portfolio

### Senior Secondary Australian Curriculum

Senior Secondary Australian Curriculum Mathematical Methods Glossary Unit 1 Functions and graphs Asymptote A line is an asymptote to a curve if the distance between the line and the curve approaches zero

### By W.E. Diewert. July, Linear programming problems are important for a number of reasons:

APPLIED ECONOMICS By W.E. Diewert. July, 3. Chapter : Linear Programming. Introduction The theory of linear programming provides a good introduction to the study of constrained maximization (and minimization)

### MATHEMATICS (CLASSES XI XII)

MATHEMATICS (CLASSES XI XII) General Guidelines (i) All concepts/identities must be illustrated by situational examples. (ii) The language of word problems must be clear, simple and unambiguous. (iii)

### Continued Fractions and the Euclidean Algorithm

Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction

### NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )

Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates

### 2. Capital Asset pricing Model

2. Capital Asset pricing Model Dr. Youchang Wu WS 2007 Asset Management Youchang Wu 1 Efficient frontier in the presence of a risk-free asset Asset Management Youchang Wu 2 Capital market line When a risk-free

### CORRELATED TO THE SOUTH CAROLINA COLLEGE AND CAREER-READY FOUNDATIONS IN ALGEBRA

We Can Early Learning Curriculum PreK Grades 8 12 INSIDE ALGEBRA, GRADES 8 12 CORRELATED TO THE SOUTH CAROLINA COLLEGE AND CAREER-READY FOUNDATIONS IN ALGEBRA April 2016 www.voyagersopris.com Mathematical

### Linear Least Squares

Linear Least Squares Suppose we are given a set of data points {(x i,f i )}, i = 1,...,n. These could be measurements from an experiment or obtained simply by evaluating a function at some points. One

### CURVE FITTING LEAST SQUARES APPROXIMATION

CURVE FITTING LEAST SQUARES APPROXIMATION Data analysis and curve fitting: Imagine that we are studying a physical system involving two quantities: x and y Also suppose that we expect a linear relationship

### A Review of Cross Sectional Regression for Financial Data You should already know this material from previous study

A Review of Cross Sectional Regression for Financial Data You should already know this material from previous study But I will offer a review, with a focus on issues which arise in finance 1 TYPES OF FINANCIAL

### Review of Fundamental Mathematics

Review of Fundamental Mathematics As explained in the Preface and in Chapter 1 of your textbook, managerial economics applies microeconomic theory to business decision making. The decision-making tools

### Florida Math for College Readiness

Core Florida Math for College Readiness Florida Math for College Readiness provides a fourth-year math curriculum focused on developing the mastery of skills identified as critical to postsecondary readiness

### 5. Linear Regression

5. Linear Regression Outline.................................................................... 2 Simple linear regression 3 Linear model............................................................. 4

### Current Standard: Mathematical Concepts and Applications Shape, Space, and Measurement- Primary

Shape, Space, and Measurement- Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two- and three-dimensional shapes by demonstrating an understanding of: