Is there any evidence of nonlinearity in the relationships

Reference no: EM131040765

The assignment requires collecting data from Yahoo finance and running the data in STATA to answer the question. Please send the result (screenshot, tables, chart, etc) you have from running the STATA to support your answer.

In this examination, we will further explore a stock return and an index return.

I have given individual ticker symbols. Your stock is ___GM___________. (General Motors Company (GM)

You will use the S&P 500 Index.

Additionally, you will use one of the following sector ETFs. Circle the sector your stock belongs.

Consumer Discretionary (XLY)

Consumer Staples (XLP)

Energy (XLE)

Financials (XLF)

Health Care (XLV)

Industrials (XLI)

Materials (XLB)

Technology (XLK)

Utilities (XLU)

Download the daily stock price data for your stock, your industry ETF and the S&P 500 from Yahoo Finance. Take the last three years of data (April 1, 2013 to March 31, 2016). Use the adjusted closing price. You should already have the data from midterm.

1. We will explore the presence of non-linearity in your stock returns. Your dependent variable will be your stock return. Run three regressions. First, with your industry return and industry return squared as independent variables. Second, with S&P 500 return and S&P 500 return squared as independent variables. Third, with the four independent variables entered at the same time. Explain the results. What am I trying to make you do here? What do the estimated coefficients mean? Is there any evidence of nonlinearity in the relationships? Why or why not?

Next, create two indicator variables. First, one where your industry return is >0. Second, one where S&P 500 return >0. (Hint: the Stata code for generating indicator variable is: gen variablename = (indexreturn> 0) if !missing(indexreturn)). Next, generate two interaction terms between the indicator variable and the industry return (Hint: the Stata code for generating interaction terms is: gen interactionterm = variablename * indexreturn) or S&P 500 return. The dependent variable is your stock return. Now, run three regressions. First, with the independent variables (a) industry return, (b) the indicator variable and (c) the interaction term. Second, repeat with S&P 500 returns. Third, include all six independent variables at the same time. Explain the results. What am I trying to make you do here? What do the estimated coefficients mean? Is there any evidence of nonlinearity in the relationships? Why or why not?

2. We will explore the presence of autoregression, integration, and moving average in the data.First, create a time variable (Stata code: gen variablename = _n). Second, tell Stata that your data is a time series (Stata code: tsetvariablename).

Generate the Correlogram, ACF, and PACF for the three variables - your stock price, you industry priceand S&P 500index value. (Stata code for correlogram is: corrgram).Explain what they mean.

Run the most appropriate ARIMA model. Explain what the coefficients mean.

Do you find evidence of autoregression, integration, or moving average in these three variables? Why or why not? Explain.

Reference no: EM131040765

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