You will use one market index and four companies’ daily data and these companies should be from two different sectors. (The required data can be downloaded from Yahoo finance: http://uk.finance.yahoo.com/). The sample period should be latest and at least 2 years in length (for example, from August 2021 to August 2023). For the volatility forecast, the required FX data can be downloaded from the course Moodle page. For the empirical analysis, you can use statistical software such as EViews, STATA, or SPSS etc., which has to specify in the report.
A. Panel Data Analysis
1. Discuss on:
i) the differences between time-series data, cross-sectional data, and panel data;
ii) what are the advantages of panel data.
(9 Marks)
2. Construct a panel data set using the latest 100 days of the four stock prices. Transfer the stock prices, market index, and risk-free rate into log returns. Show the observations in a table.
(6 Marks)
3. Report the R-squares, t-statistics of the beta coefficient, and the p-values of the alpha coefficient using OLS, FE and RE estimators.
(9 Marks)
4. Verify the CAPM theory using OLS, FE, and RE estimators. Comment which method should be applied.
(6 Marks)
B. Time-Series Data Analysis
1. Please provide the explanations on ACF and PACF. Discuss on why ACF and PACF are used in time-series data analysis.
(6 Marks)
2. Choose one of your stock price series, compute ACF and PACF for the log returns and show the graphs in the report.
(6 Marks)
3. Estimate the log returns with ARMA(3,2) model and comment on the estimations (R-squares, significance of the AR and MA components).
(8 Marks)
4. Forecast with ARMA(3,2) model and verify the forecasting accuracy by considering the last 6 months of the data as out-of-sample.
(5 Marks)
C. Volatility Analysis
1. Choose one of your stock price series, plot the stock price and log returns of this stock.
(4 Marks)
2. Explain what is the ARCH effect, and why ARCH effect should be verified in volatility analysis.
(4 Marks)
3. Verify the ARCH effect using the chosen stock and estimate GARCH(1, 1) model.
(8 Marks)
2. Using the chosen stock carry out a GARCH(1, 1) volatility forecasting by considering the last 6 months of the data as out-of-sample. Discuss the results.
(4 Marks)
D. Machine Learning Application
1. Discuss the following concepts.
a) Machine Learning
b) Supervised Learning
c) Differentiate between test set and training set
(9 Marks)
2. Explain your understanding on neural network in machine learning. Provide one possible application of neural network in the financial practice and explain the processes.