I need an R programmer
Budget: $30 – $250 USD
To provide the aforementioned, you are to create an R-Markdown file that generates a PDF as output for your report. As a guide for the data you are to acquire, feel free to use the following steps:
Gather daily stock price data covering the last 5 years for 5 stocks, or indices, of your choosing (again, feel free to use the same from Assignment #1).
Gather the Fama-French 3 Factor data from the following site: Current US Research Returns Data (Links to an external site.).
You do not need to download data directly from R—teams who are able to perform such direct download get an extra 10 points.
You can download data in CSV format, then read it into R.
Create summary statistics, e.g. means, variances, correlations, etc, on the returns associated with each data item.
Plot individual return patterns for each asset.
Plot price trends for all assets.
Calculate sensitivities to the F-F factors, using simple OLS techniques on the realized data. (Hint: use the "lm()" function in R.)
Feel free to separate the regression analyses of each stock on separate report pages.
Teams able to consolidate sensitivities and present regression results in a tabular format on a single page for all assets receive 10 Bonus Points.
Present and explain your regression results.
Perform Monte Carlo simulation on the "F-F" factors (assume the factors generate "normally" distributed characteristics as determined by Step 3), and generate expected returns using the 3-Factor model for each "stock" chosen in Step 1.
Perform Historical Simulation on the "F-F" factors and generated expected returns using the 3-Factor model for each "stock" chosen in Step 2.
Replicate each simulation at least 500 times and determine an expected range of VaR determined in each simulation
Present your results in a summary statement that compares the VaR's generated through point estimates, e.g. traditional approaches, vs. simulated approaches.
Gather daily stock price data covering the last 5 years for 5 stocks, or indices, of your choosing (again, feel free to use the same from Assignment #1).
Gather the Fama-French 3 Factor data from the following site: Current US Research Returns Data (Links to an external site.).
You do not need to download data directly from R—teams who are able to perform such direct download get an extra 10 points.
You can download data in CSV format, then read it into R.
Create summary statistics, e.g. means, variances, correlations, etc, on the returns associated with each data item.
Plot individual return patterns for each asset.
Plot price trends for all assets.
Calculate sensitivities to the F-F factors, using simple OLS techniques on the realized data. (Hint: use the "lm()" function in R.)
Feel free to separate the regression analyses of each stock on separate report pages.
Teams able to consolidate sensitivities and present regression results in a tabular format on a single page for all assets receive 10 Bonus Points.
Present and explain your regression results.
Perform Monte Carlo simulation on the "F-F" factors (assume the factors generate "normally" distributed characteristics as determined by Step 3), and generate expected returns using the 3-Factor model for each "stock" chosen in Step 1.
Perform Historical Simulation on the "F-F" factors and generated expected returns using the 3-Factor model for each "stock" chosen in Step 2.
Replicate each simulation at least 500 times and determine an expected range of VaR determined in each simulation
Present your results in a summary statement that compares the VaR's generated through point estimates, e.g. traditional approaches, vs. simulated approaches.