Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2014. The project is a required component of every master program.
Realized Volatility Estimation
Miquel Masoliver, Guillem Roig, Shikhar Singla
The main purpose of this study is to try to find the optimal volatility estimator in a non-parametric framework. In particular, this study focuses on the estimation of the daily integrated variance-covariance matrix of stock returns using simulated and high-frequency data in the presence of market microstructure noise, jumps, and non-synchronous trading. This work is structured in three building blocks: (i) price processes are simulated in the presence of jumps and market microstructure noise. This allows us to obtain some insight about the estimators’ performance. (ii) The aforementioned realized volatility estimators are applied to high-frequency data of the S&P 100 stocks of October 27th 2010 using 5-second, 10-second, 30-second, 1-minute and 2-minute time intervals. (iii) We use the estimated covariance matrices to construct the global minimum variance portfolio for each sampling frequency. These global minimum variance portfolios are used to build 30 day ex-post portfolio’s returns and we use the variance of these returns to compare between the performance of the estimators.
Read the full paper or view slides below: