Comprehensive CFA Level II Mock Exam: 60 Challenging Questions with Detailed Explanations (2025 Edition)

Question #1 of 60 Question ID: 692270 Prepare with confidence using this Comprehensive CFA Level II Mock Exam, featuring 60 carefully designed, exam-style questions and detailed answer explanations. Built to match the CFA Institute’s difficulty and format, this mock covers all core topics including Ethics, Quantitative Methods, Financial Reporting & Analysis, Equity, Fixed Income, Corporate Finance, Portfolio Management, Economics, Derivatives, and Alternative Investments. Each question is accompanied by clear, insightful rationales to reinforce key concepts and test your understanding under timed conditions. Ideal for pre-exam simulation or targeted review, this resource helps you identify weak areas, improve test-taking strategies, and build confidence ahead of the official CFA Level II examQuestions 1­6 relate to Goldensand Jewelry, Ltd. Introduction Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a London­based retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1. Exhibit 1: 1979­2009 Annual Data (31 Observations) Exhibit 2: Partial Student's t­distribution Table Level of Significance for One­Tailed Test df 0.100 0.050 0.025 0.010 0.005 0.0005 Level of Significance for Two­Tailed Test df 0.200 0.100 0.050 0.020 0.010 0.001 29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636 Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000. Regression Concerns Overall Concerns Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement: Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years. Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following: Statement 2: We fail to reject the null hypothesis that the slope coefficient is equal to 4.0 at the 5% level of significance. Testing for Heteroskedasticity Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a Breusch­Pagan chi­square test for heteroskedasticity by calculating the following test statistic: n × R2 with k degrees of freedom where: n = number of observations R2 = R2 of the regression of jewelry prices on gold prices k = number of independent variables Model Misspecification Biscayne and Singh have various views on the potential for model misspecification and the effect of any such misspecification. Biscayne worries that the regression model is misspecified because it does not include a variable to measure the cost of the highly specialized labor used by manufacturing jewelers. She points out that the effect of omitting an important variable in a regression analysis is that the regression coefficients will be unbiased and inconsistent. Singh adds that another common consequence of misspecifying a regression analysis is creating undesired stationarity. Multiple Regression Hara conducts a series of regression analyses using all possible combinations of the suggested independent variables based on their average quarterly values. He returns with the following regression results as shown in Exhibit 3 for the equation which uses all suggested independent variables. Exhibit 3: 1999­2009 Quarterly Data (44 Observations) Independent Variables Coefficient t­Statistic Intercept −3.9 3.7 Gold price 4.7 14.5 Silver price 1.2 7.8 Platinum price 3.5 3.1 Labor costs 0.82 2.4 GDP (EU) 0.000274 5.7 GDP (Middle East) 0.000049 3.6 Personal income (EU) 0.000314 2.1 Personal income (Middle East) 0.009876 2.2 R2: 0.55 Durbin­Watson: 3.89 Hara is concerned about the equation described in Exhibit 3. He makes the following statement: Statement 3: The model appears to suffer from multicollinearity. Dropping one or more independent variables will increase the coefficient of determination. Biscayne responds with the following statement: Statement 4: An autocorrelation problem can be addressed by using the Hansen method to adjust the R2. Exhibit 4: Partial Durbin­Watson Table Critical Values for the Durbin­Watson Statistic ( = 0.05) K = 3 K = 4 K = 5 n d1 39 1.33 40 1.34 45 1.38 du 1.66 1.66 1.67 d1 1.27 1.29 1.34 du 1.72 1.72 1.72 d1 1.22 1.23 1.29 du 1.79 1.79 1.78 ..................................................................................................................................................................................................... The per ounce price of gold that corresponds to the $2,000 cost of finished jewelry is closest to: A) $687. B) $712. C) $3,240. Question #2 of 60 Question ID: 692269 Introduction Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a London­based retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1. Exhibit 1: 1979­2009 Annual Data (31 Observations) Exhibit 2: Partial Student's t­distribution Table Level of Significance for One­Tailed Test df 0.100 0.050 0.025 0.010 0.005 0.0005 Level of Significance for Two­Tailed Test df 0.200 0.100 0.050 0.020 0.010 0.001 29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636 Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000. Regression Concerns Overall Concerns Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement: Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years. Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following: Statement 2: We fail to reject the null hypothesis that the slope coefficient is equal to 4.0 at the 5% level of significance. Testing for Heteroskedasticity Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a Breusch­Pagan chi­square test for heteroskedasticity by calculating the following test statistic: n × R2 with k degrees of freedom where: n = number of observations R2 = R2 of the regression of jewelry prices on gold prices k = number of independent variables Model Misspecification Biscayne and Singh have various views on the potential for model misspecification and the effect of any such misspecification. Biscayne worries that the regression model is misspecified because it does not include a variable to measure the cost of the highly specialized labor used by manufacturing jewelers. She points out that the effect of omitting an important variable in a regression analysis is that the regression coefficients will be unbiased and inconsistent. Singh adds that another common consequence of misspecifying a regression analysis is creating undesired stationarity. Multiple Regression Hara conducts a series of regression analyses using all possible combinations of the suggested independent variables based on their average quarterly values. He returns with the following regression results as shown in Exhibit 3 for the equation which uses all suggested independent variables. Exhibit 3: 1999­2009 Quarterly Data (44 Observations) Independent Variables Coefficient t­Statistic Intercept −3.9 3.7 Gold price 4.7 14.5 Silver price 1.2 7.8 Platinum price 3.5 3.1 Labor costs 0.82 2.4 GDP (EU) 0.000274 5.7 GDP (Middle East) 0.000049 3.6 Personal income (EU) 0.000314 2.1 Personal income (Middle East) 0.009876 2.2 R2: 0.55 Durbin­Watson: 3.89 Hara is concerned about the equation described in Exhibit 3. He makes the following statement: Statement 3: The model appears to suffer from multicollinearity. Dropping one or more independent variables will increase the coefficient of determination. Biscayne responds with the following statement: Statement 4: An autocorrelation problem can be addressed by using the Hansen method to adjust the R2. Exhibit 4: Partial Durbin­Watson Table Critical Values for the Durbin­Watson Statistic ( = 0.05) K = 3 K = 4 K = 5 n d1 39 1.33 40 1.34 45 1.38 du 1.66 1.66 1.67 d1 1.27 1.29 1.34 du 1.72 1.72 1.72 d1 1.22 1.23 1.29 du 1.79 1.79 1.78 ..................................................................................................................................................................................................... Are Singh (Statement 1) and Hara (Statement 2) correct or incorrect regarding the usefulness of regression results described in Exhibit 1 and the value of the slope coefficient? A) Both are correct. B) One is correct, the other is incorrect. C) Both are incorrect. Question #3 of 60 Question ID: 692272 Introduction Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a London­based retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1. Exhibit 2: Partial Student's t­distribution Table Level of Significance for One­Tailed Test df 0.100 0.050 0.025 0.010 0.005 0.0005 Level of Significance for Two­Tailed Test df 0.200 0.100 0.050 0.020 0.010 0.001 29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636 Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000. Regression Concerns Overall Concerns Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement: Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years. Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following: Statement 2: We fail to reject the null hypothesis that the slope coefficient is equal to 4.0 at the 5% level of significance. Testing for Heteroskedasticity Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a Breusch­Pagan chi­square test for heteroskedasticity by calculating the following test statistic: n × R2 with k degrees of freedom where: n = number of observations R2 = R2 of the regression of jewelry prices on gold prices k = number of independent variables Model Misspecification