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You are working on U.S. stock market performances. You have been provided weekly adjusted close prices for the NASDAQ (National Association of Securities Dealers Automated

You are working on U.S. stock market performances. You have been provided weekly adjusted close prices for the NASDAQ (National Association of Securities Dealers Automated Quotations) Composite index, Microsoft Corporation (MSFT), and Intel Corporation (INTC) from January 2014 to December 2023.

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Please complete the following questions: 1. Calculate weekly discrete returns for the NASDAQ index, Intel Corporation (INTC), and Microsoft Corporation (MSFT) stock prices, respectively. Please answer the following questions using the returns data obtained in Q.1. 2. Compute and interpret the descriptive statistics of NASDAQ index returns, Intel Corporation (INTC), and Microsoft Corporation (MSFT) stock prices returns, respectively. 3. Which one has the highest relative risk among the weekly returns of the NASDAQ index, INTC and MSFT? Provide evidence. 4. Assuming normality, what is the probability of the INTC returns above 3%? What is the probability of the MSFT returns being between -2% and 2%? 5. Test whether the mean return of the NASDAQ index is significantly different from zero at the 1% level of significance. Test whether the mean return of INTC is significantly different from 0.5% at the 5% level of significance. 6. What is the covariance of NASDAQ index returns and INTC returns? What is the correlation coefficient between the MSFT stock returns and INTC stock returns? 7. To predict the weekly Intel Corporation (INTC) returns, your supervisor advised two linear regression models: Model A: Assuming a linear relationship between INTC returns and MSFT returns. Model B: Assuming a linear relationship between INTC returns and NASDAQ index returns. 7.1: For model A, is the intercept coefficient significant at the 5% level? Is the slope coefficient significant at the 1% level? 7.2: For model B, Construct the 99% confidence interval for the intercept coefficient, and interpret it. 7.3: Which model you would recommend to your supervisor, and why

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