Modeling the S&P 500 Index using the Kalman Filter and the LagLasso
published: Aug. 21, 2009, recorded: July 2009, views: 1859
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
This video introduces a method to predict upward and downward monthly variations of the S&P 500 index by using a pool of macro-economic and financial explicative variables. The method is based on the combination of a denoising step, performed by Kalman filtering, with a variable selection step, performed by a Lasso-type procedure. In particular, we propose an implementation of the Lasso method called LagLasso which includes selection of lags for individual factors. We provide promising backtesting results of the prediction model based on a naive trading rule.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !