Measuring the degree of co-movement between stock prices is of utmost importance when dealing with portfolio selection, risk measurement and multivariate derivative pricing/hedging. Daily stock price information is easily accessible for a large number of stocks and indices and allows to calibrate multivariate stock price models. Such a model can then be employed to investigate the extent to which stocks will move together in the future. However, intraday movements of stock prices are behaving differently from longer-term (daily, weekly or monthly) movements. Our aim in this project is to use high-frequency data of stocks to investigate how stock prices move together during a trading day. The intra-day co-movement may change rapidly, giving rise to changes in diversification when composing a portfolio.
The project is based on the order book data of the Chinese stock market. An order book is a list of orders for a certain security placed by the public, which contains detailed information about the order time, direction, depth, amount, etc. The observation interval is extended from 1 day to 3s. High-frequency raw data is associated with not only massive information but also massive noise, calling for different approaches to extract information about volatilities and co-movements. The size of the dataset to be used is up to 100G, which makes efficient code necessary.
Students: Neer Bhardwaj, Changyue Hu, Runshi Li, Yirui Luo
Supervisor: Daniël Linders
Graduate Supervisor: Yong Xie