Free CFA Institute CFA-Level-II Exam Practice Questions & Explanations
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Jonathan Weil, CFA, is the managing director of Weil Capital Management (WCM). Weil has decided to reevaluate the asset backed security (ABS) positions in his investors' portfolios since a recent, and severe, credit problem in the subprime lending industry has had ripple effects on other fixed-income markets. Weil expects the interest rate term structure to remain flat, but shift upward as credit terms tighten. Weil has asked one of his analysts, Vanessa Ordon, to assist him in analyzing potential ABS investments, including some products he is unfamiliar with.
Ordon provides an assessment of ABS backed by auto loans, credit card receivables, and closed end home equity loans. Ordon has assembled the following information on the collateral, credit enhancements, and prepayment risk associated with each type of ABS.
Ordon has also assembled the following information on the structure of a closed end home equity loan (HEL) asset backed security. Ordon has presented the security to Weil as a potential investment opportunity, but Weil is concerned about its default and prepayment risks. The HEL ABS does not have a shifting interest mechanism.
Ordon has also developed an assessment of mortgage backed securities (MBS). Ordon has assembled the following information related to a mortgage backed security.
Which of the following best describes the initial reaction of the home equity loan backed ABS Ordon presented to Weil if there is a collateral default of $30 million?
The home equity loan ABS has an internal credit enhancement known as over-collateralization. The principal of the collateral equals $475 million. The principal of the tranches, however, totals only $450 million (180 + 112.5 + 67.5 + 36 + 54). The over-collatcralization feature is included in ABS to protect the tranches from collateral defaults. Thus $25 million of the $30 million default stated in the question would first be absorbed by the extra collateral. This leaves a $5 million default that must be absorbed by one of the tranches. Tranche E has been subordinated to Tranches A - D, a structure known as credit tranching. Losses resulting from defaults (a credit event) are absorbed by Tranche E first until the principal backing the tranche is exhausted. The remaining $5 million default gets absorbed by Tranche E, reducing the principal to $49 million (54 - 5). (Study Session 15, LOS 56.b,e)
Ryan Hendricks serves as a security analyst for Investment Management, Inc. (IMI), which employs the Treynor-Black model to evaluate securities and to make portfolio recommendations. IMI uses the capital asset pricing model (CAPM) to determine the degree to which securities may be mispriced relative to IMFs forecasts.
Hendricks evaluates the common shares of Computer Software Associates (CSA), a small company specializing in a unique computer software market niche. Hendricks obtains the following market model results for CSA, using monthly returns for the past 60 months:
Hendricks uses the adjusted beta method to derive his forecasts for companies' future betas. In deriving his forecast for any company beta, Hendricks uses the following first-order autoregressive formula:
forecast beta = 0.33 + 0.67 x (historical beta) (2)
Hendricks derives required returns for individual securities using the CAPM after making appropriate adjustments using his adjusted beta formula in equation (2).
IMI provides Hendricks with the following capital market forecasts to use as inputs for the CAPM.
IMI asks Hendricks to make decisions to take long and short positions in individual securities for IMl's actively managed portfolio, IMI-Active. Specifically, Hendricks is asked to examine CSA and Millennium Drilling (MD), an oil and gas drilling company specializing in deep sea drilling. After a thorough examination of the prospects for each company, Hendricks derives the following alpha forecasts for CSA and MD.
Hendricks forecasts that the unsystematic variance (the variance of the market model regression error) for MD will be more than double that of CSA .
After determining the appropriate allocations across securities within the IMI-Active portfolio, Hendricks derives the portfolio predictions shown in Exhibit 3.
IMI forecasts that the total standard deviation for the S&P500 returns will equal 20%. After examining the historical forecasting abilities of Hendricks, IMI determines that Hendricks has demonstrated perfect forecasting ability in regards to CSA stock, but imperfect forecasting abilities in regards to MD stock. IMI finds that the correlation between the realized alphas for MD and the forecast MD alphas provided by Hendricks equals 0.50.
Referring to the Treynor-Black model, Hendricks makes the following statements:
Statement 1: All else equal, the Treynor-Black model increases the weight to the active portfolio as its unsystematic risk increases.
Statement 2: The Treynor-Black model is based on the premise that only a limited number of stocks should be included in the actively managed portfolio.
Hendricks' forecasts for the CSA beta and future return are closest to:
We are told that IMI uses the CAPM to determine the degree to which securities are mispriced. The equation for the CAPM (using the S&P500 market index and treasury bill to proxy for the market index and risk-free asset) is:
According to Exhibit 2, Hendricks forecasts that the CSA alpha will equal 4%. The alpha equals the difference between Hendricks* forecast return and the required return:
Alpha for CSA = forecast return - CAPM required return
or
Forecast return = alpha + CAPM required return = 0.04 + 0.16 = 0.20 = 20%
(Study Session 10, LOS 33.e and Study Session 18, LOS 64.e,h)
William Shears, CFA, has been assigned the task of predicting sales for the specialty retail industry. Shears finds that sales have been increasing at a fairly constant rate over time and decides to estimate the linear trend in sales for the industry using quarterly data over the past 15 years, starting with Quarter 1 of 1994 and ending with Quarter 4 of 2008. On January 1, 2009, Shears estimates the following model:
Shears also estimates an autoregressive model of order one, AR(1), using the changes in quarterly sales data for the industry from the first quarter of 1994 through the fourth quarter of 2008. He obtains the following results for his AR(1) model:
Shears also derives a regression using the residuals from the AR(1) model. He regresses the squared residuals (or estimated errors) against the lagged squared residuals. The results of this regression are reported in Exhibit 4.
Quarterly sales for the Specially Retail Industry during 2008 were:
Shears's supervisor, Sam Kite, expresses concern that equation (1) might be misspecified. Specifically, Kite refers to the finding that "sales have been increasing at a fairly constant rate over time.
Using the results for the linear trend equation in Exhibit 1, the specialty retail industry sales forecast for Quarter 1 of 2009 is closest to:
Quarter 1 of 2009 is the 61st quarter (starting with Quarter 1 of 1994): sales = 10 + 16(61) = $986 million. (Study Session 3, LOS 13.a)
Joan Fisher and Kim Weatherford are economists responsible for modeling security returns for Quincy Portfolio Managers, which is located in the southwestern United States. Fisher is the firm's chief economist and Weatherford is her assistant.
Fisher has been busy over the past week modeling the macroeconomic data of an emerging market. The data for the past 24 months is shown in Exhibit 1.
Fisher ponders how she can run a regression that will model the data for this country in the most appropriate way. She decides to regress the macroeconomic values against a time variable. The resulting plot of the residuals is shown in Exhibit 2.
In addition to financial assets, Quincy Portfolio Managers also recommends the use of commodities as a portfolio diversifier. Weatherford has been examining price indices for silver in an attempt to determine whether silver returns are predictable. As an initial step, she uses an autoregressive first-order regression model on daily price data for silver over the past two years. The plot of the raw data and the results of the regression are shown in Exhibits 3 and 4.
Fisher and Weatherford later discuss fluctuations in gold prices. Although the arithmetic and geometric mean returns for gold were negative for much of the 1980s and 1990s, Fisher and Weatherford believe that gold should perform better in the future due to higher expected inflation. After appropriate transformation of the data, they use an autoregressive first-order regression model to examine the characteristics of gold returns, the results of which are shown in Exhibit 5.
The most likely problem in Fisher's regression of the emerging market data and the most appropriate test for it in the regression are:
Problem Test
The most likely problem in Fisher's regression of the emerging market data is that the error terms appear to be positively correlated in Exhibit 2. The first few error terms are positive, then negative, and then positive. This indicates serial correlation, which is common in trend models. As Fisher regresses the macroeconomic data against a time variable, she is using a trend model. In a trend model, the Durbin Watson statistic can be used to detect serial correlation. (Study Session 3, LOS 12.g)
Stan Loper is unfamiliar with the Black-Scholes-Merton (BSM) option pricing model and plans to use a two-period binomial model to value some call options. The stock of Arbor Industries pays no dividends and currently trades for $45. The up-move factor for the stock is 1.15, and the risk-free rate is 4%. He is considering buying two-period European style options on Arbor Industries with a strike price of S40. The delta of these options over the first period is 0.83.
Loper is curious about the effect of time on the value of the calls in the binomial model, so he also calculates the value of a one-period European style call option with a strike price of 40.
Loper is also interested in using the BSM model to price European and American call and put options. He is concerned, however, whether the assumptions necessary to derive the model are realistic. The assumptions he is particularly concerned about are:
* The volatility of the option value is known and constant.
* Stock prices are lognormally distributed.
* The continuous risk-free rate is known and constant.
Loper would also like to value options on Rapid Repair, Inc., common stock, but Rapid pays dividends, so Loper is uncertain what the effect will be on the value of the options. Loper uses the two-period model to value long positions in the Rapid Repair call and put options without accounting for the fact that Rapid Repair pays common dividends.
The position in calls necessary to hedge a long position in 1,000 shares of stock over the first period is closest to:
