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INVESTMENTS | BODIE, KANE, MARCUS Chapter Five Risk, Return, and the Historical Record Copyright © 2014 McGraw-Hill Education. All rights reserved. No.

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Presentation on theme: "INVESTMENTS | BODIE, KANE, MARCUS Chapter Five Risk, Return, and the Historical Record Copyright © 2014 McGraw-Hill Education. All rights reserved. No."— Presentation transcript:

1 INVESTMENTS | BODIE, KANE, MARCUS Chapter Five Risk, Return, and the Historical Record Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

2 INVESTMENTS | BODIE, KANE, MARCUS 5-2 Interest rate determinants Rates of return for different holding periods Risk and risk premiums Estimations of return and risk Normal distribution Deviation from normality and risk estimation Historic returns on risky portfolios Chapter Overview

3 INVESTMENTS | BODIE, KANE, MARCUS 5-3 Supply Households Demand Businesses Government’s net demand Federal Reserve actions Interest Rate Determinants

4 INVESTMENTS | BODIE, KANE, MARCUS 5-4 Nominal interest rate (rn): Growth rate of your money Real interest rate (rr): Growth rate of your purchasing power Where i is the rate of inflation Real and Nominal Rates of Interest

5 INVESTMENTS | BODIE, KANE, MARCUS 5-5 Figure 5.1 Determination of the Equilibrium Real Rate of Interest

6 INVESTMENTS | BODIE, KANE, MARCUS 5-6 As the inflation rate increases, investors will demand higher nominal rates of return If E(i) denotes current expectations of inflation, then we get the Fisher Equation: Equilibrium Nominal Rate of Interest

7 INVESTMENTS | BODIE, KANE, MARCUS 5-7 Tax liabilities are based on nominal income Given a tax rate (t) and nominal interest rate (rn), the real after-tax rate is: The after-tax real rate of return falls as the inflation rate rises Taxes and the Real Rate of Interest

8 INVESTMENTS | BODIE, KANE, MARCUS 5-8 Zero Coupon Bond: Par = $100 Maturity = T Price = P Total risk free return Rates of Return for Different Holding Periods

9 INVESTMENTS | BODIE, KANE, MARCUS 5-9 Example 5.2 Annualized Rates of Return

10 INVESTMENTS | BODIE, KANE, MARCUS 5-10 EAR: Percentage increase in funds invested over a 1-year horizon Effective Annual Rate (EAR)

11 INVESTMENTS | BODIE, KANE, MARCUS 5-11 APR: Annualizing using simple interest Annual Percentage Rate (APR)

12 INVESTMENTS | BODIE, KANE, MARCUS 5-12 Table 5.1 APR vs. EAR

13 INVESTMENTS | BODIE, KANE, MARCUS 5-13 Table 5.2 T-Bill Rates, Inflation Rates, and Real Rates,

14 INVESTMENTS | BODIE, KANE, MARCUS 5-14 Moderate inflation can offset most of the nominal gains on low-risk investments A dollar invested in T-bills from 1926–2012 grew to $20.25 but with a real value of only $1.55 Negative correlation between real rate and inflation rate means the nominal rate doesn’t fully compensate investors for increased in inflation Bills and Inflation,

15 INVESTMENTS | BODIE, KANE, MARCUS 5-15 Figure 5.3 Interest Rates and Inflation,

16 INVESTMENTS | BODIE, KANE, MARCUS 5-16 Rates of return: Single period HPR = Holding period return P 0 = Beginning price P 1 = Ending price D 1 = Dividend during period one Risk and Risk Premiums

17 INVESTMENTS | BODIE, KANE, MARCUS 5-17 Ending Price =$110 Beginning Price = $100 Dividend =$4 Rates of Return: Single Period Example

18 INVESTMENTS | BODIE, KANE, MARCUS 5-18 Expected returns p(s) = Probability of a state r(s) = Return if a state occurs s = State Expected Return and Standard Deviation

19 INVESTMENTS | BODIE, KANE, MARCUS 5-19 State Prob. of Stater in State Excellent Good Poor Crash E(r) = (.25)(.31) + (.45)(.14) + (.25)(−.0675) + (0.05)(− 0.52) E(r) =.0976 or 9.76% Scenario Returns: Example

20 INVESTMENTS | BODIE, KANE, MARCUS 5-20 Variance (VAR): Standard Deviation (STD): Expected Return and Standard Deviation

21 INVESTMENTS | BODIE, KANE, MARCUS 5-21 Example VAR calculation: σ 2 =.25(.31 − ) (.14 −.0976) (− − ) (−.52 −.0976) 2 =.038 Example STD calculation: Scenario VAR and STD: Example

22 INVESTMENTS | BODIE, KANE, MARCUS 5-22 True means and variances are unobservable because we don’t actually know possible scenarios like the one in the examples So we must estimate them (the means and variances, not the scenarios) Time Series Analysis of Past Rates of Return

23 INVESTMENTS | BODIE, KANE, MARCUS 5-23 Arithmetic Average Geometric (Time-Weighted) Average = Terminal value of the investment Returns Using Arithmetic and Geometric Averaging

24 INVESTMENTS | BODIE, KANE, MARCUS 5-24 Estimated Variance Expected value of squared deviations Unbiased estimated standard deviation Estimating Variance and Standard Deviation

25 INVESTMENTS | BODIE, KANE, MARCUS 5-25 Excess Return The difference in any particular period between the actual rate of return on a risky asset and the actual risk-free rate Risk Premium The difference between the expected HPR on a risky asset and the risk-free rate Sharpe Ratio The Reward-to-Volatility (Sharpe) Ratio

26 INVESTMENTS | BODIE, KANE, MARCUS 5-26 Investment management is easier when returns are normal Standard deviation is a good measure of risk when returns are symmetric If security returns are symmetric, portfolio returns will be as well Future scenarios can be estimated using only the mean and the standard deviation The dependence of returns across securities can be summarized using only the pairwise correlation coefficients The Normal Distribution

27 INVESTMENTS | BODIE, KANE, MARCUS 5-27 Figure 5.4 The Normal Distribution Mean = 10%, SD = 20%

28 INVESTMENTS | BODIE, KANE, MARCUS 5-28 What if excess returns are not normally distributed? Standard deviation is no longer a complete measure of risk Sharpe ratio is not a complete measure of portfolio performance Need to consider skewness and kurtosis Normality and Risk Measures

29 INVESTMENTS | BODIE, KANE, MARCUS 5-29 Figure 5.5A Normal and Skewed Distributions Mean = 6%, SD = 17%

30 INVESTMENTS | BODIE, KANE, MARCUS 5-30 Figure 5.5B Normal and Fat-Tailed Distributions Mean =.1, SD =.2

31 INVESTMENTS | BODIE, KANE, MARCUS 5-31 Value at Risk (VaR) Loss corresponding to a very low percentile of the entire return distribution, such as the fifth or first percentile return Expected Shortfall (ES) Also called conditional tail expectation (CTE), focuses on the expected loss in the worst-case scenario (left tail of the distribution) More conservative measure of downside risk than VaR Normality and Risk Measures

32 INVESTMENTS | BODIE, KANE, MARCUS 5-32 Lower Partial Standard Deviation (LPSD) and the Sortino Ratio Similar to usual standard deviation, but uses only negative deviations from the risk-free return, thus, addressing the asymmetry in returns issue Sortino Ratio (replaces Sharpe Ratio) The ratio of average excess returns to LPSD Normality and Risk Measures

33 INVESTMENTS | BODIE, KANE, MARCUS 5-33 The second half of the 20 th century, politically and economically the most stable sub-period, offered the highest average returns Firm capitalization is highly skewed to the right: Many small but a few gigantic firms Average realized returns have generally been higher for stocks of small rather than large capitalization firms Historic Returns on Risky Portfolios

34 INVESTMENTS | BODIE, KANE, MARCUS 5-34 Normal distribution is generally a good approximation of portfolio returns VaR indicates no greater tail risk than is characteristic of the equivalent normal The ES does not exceed 0.41 of the monthly SD, presenting no evidence against the normality However Negative skew is present in some of the portfolios some of the time, and positive kurtosis is present in all portfolios all the time Historic Returns on Risky Portfolios

35 INVESTMENTS | BODIE, KANE, MARCUS 5-35 Figure 5.7 Nominal and Real Equity Returns Around the World,

36 INVESTMENTS | BODIE, KANE, MARCUS 5-36 Figure 5.8 SD of Real Equity & Bond Returns Around the World

37 INVESTMENTS | BODIE, KANE, MARCUS 5-37 Figure 5.9 Probability of Investment with a Lognormal Distribution

38 INVESTMENTS | BODIE, KANE, MARCUS 5-38 When the continuously compounded rate of return on an asset is normally distributed, the effective rate of return will be lognormally distributed Terminal Value with Continuous Compounding


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