Instructions:The assignment has to be submitted as a single pdf file (Python output (tables and graphs) followed by your comments and interpretations, merged with the Python script exported as a pdf output file),together with datasets(as a
separate single Excel file).
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PART A: Descriptive Statistics, SML and Four-Factor Model. (5 pts.)
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Problem A1.
Download data for any three stocks and corresponding market index for any available period of your choice from
(YahooFinance). The data set should include at least 252 observation, but there is no upper limit for the number
of observation. Also, your data set (the period and the market index) should remain confidential. Use Python
(exclusively) to: (a) calculate the descriptive statistics (mean, median, variance, standard deviation, skewness,
kurtosis, coefficient of variation) for both the stocks and the index, and comment on the observed dynamics; (b)
develop and interpret the SML for each stock (plot all three on the same plot, comment on the intercept, the slope,
their meaning, etc.). The Python output and your comments/interpretation should be included in the main pdf file.
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Problem A2.
Download data for 5 industry portfolios from Keneth R. French data library (Online Data Library), and corre-
sponding factors and risk-free rate (you will need to download all necessary factors for this assignment). Data
set should include at least 252 observation, but there is no upper limit for the number of observation. Pick any
two industry portfolios from the data set (out of those 5 portfolios), and estimate the four-factor model for each
industry portfolio (two models). Comment on the estimated coefficients, and the goodness-of-fit for each model.
The Python output and your comments/interpretation should be included in the main pdf file.
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PART B: ARIMA Forecasting & Market Risk. (15 pts.)
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Problem B1.
Download data for S&P 500, Russell 2000, and Bitcoin for any available period of your choice from YahooFinance
and CoinMarketCap. The data set should include at least 252 observation, but there is no upper limit for the num-
ber of observation. Also, your data set should remain confidential. Use Python (exclusively) to plot the distribution
of log returns for these three assets. Comment on the difference in the distribution of the returns, having in mind
different risk-preferences (risk aversion vs. risk loving investors). The Python output and your comments/interpre-
tation should be included in the main pdf file.
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Problem B2.
Specify an appropriate ARIMA model (your specification must be justified by different statistical tests) for each
time series and forecast the log returns for these three assets 15 days ahead. Plot the forecasted returns for each
asset and comment on the ARIMA forecasting output. The Python output and your comments/interpretation
should be included in the main pdf file.
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Problem B3.
Use Python and the log returns for these three assets to calculate: (a) the VaR (90%, 95% and 99% C.L.), (b)
backtesting estimations of these VaR values; (c) the CVaR (90%, 95% and 99% C.L.). Comment on the meaning
and the dynamics of these estimated losses for each asset and across the assets. What would be your investment
advice for different type of portfolio investors?
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PART C: Volatility (Historical Volatility & GARCH). (15 pts.)
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Problem C1.
Download data for any two stocks and any two cryptocurrencies for any available period of your choice from Yahoo-
Finance and CoinMarketCap. Use Python to: (a) calculate and plot on the same chart the log returns for each of
these assets; (b) plot the distribution of these log returns and comment on possible asymmetries and their meaning
from the investment perspective.
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Problem C2.
Calculate and plot any two measures of historical volatility for each asset (four plots, one for each asset). Comment
on the dynamics of historical volatility by comparing the trends (within and across the markets).
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Problem C3.
Use Python to estimate GARCH (1,1) model for both stocks separately. Comment on the estimated GARCH
coefficients, and compare the estimated results obtained from these two models.
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Problem C4.
Use Python to estimate GARCH (1,1) model for both cryptos separately. Comment on the estimated GARCH
coefficients, and compare the estimated results obtained from these two models. Also, compare and comment the
estimated GARCH coefficients for all four GARCH models.
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• Use Python (exclusively) for each problem, export all tables, figures, and computational results to
a docx file.
• Interpret and comment all the results according to the requirements written in each problem.
•Export Python script as a pdf file (it has to be organized to follow the problems in the assignment),
and merge it with the main pdf file (the main file followed by the Python script) to make a single pdf file.
• Submit the single pdf file, together with a separate Excel file (that includes all data sets in different
spreadsheets) exclusively through Canvas (so, you are supposed to submit only two files).
Requirements: