Peer Review for midterm report

[yangchang0]

The project aims to predict/forecase realized volatility of the S&P 500 index over different market regimes. The data used is 1 minute tick data for S&P 500 from Kaggle. For prediction of volatility, this group consider not only market variables but also marcroeconomic variables and lagged volatility terms which help increase accuracy of the prediction.

Advantages:
In the exploratory data analysis part, this group covers every feature and makes a large number of plot based on different features. They also include description of how they deal with missing data. They even make a comparison of between China and the US.
Beside complte data visualization this group did a great job doing feature engineering. For the preliminary model they normalize 6 features and input missing value for 2 features before training their model.
While fitting the model, a preliminary model is built first and then revised with reduced multicollinearity as well as more significant features.

Potential improvement:
Could potentially add interaction term to increase the accuracy of the model
Could potentially try on different method such as bagging or boosting. Some algorithms do not require a lot of manual feature selection so it would be easier
Could spend less time on data visualization for feature like US dividend indicator because they are not quite explanatory with visualization