Abstract Details
Activity Number:
|
396
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Business and Economic Statistics Section
|
Abstract #312346
|
View Presentation
|
Title:
|
Forecasting Financial Volatility: An Exogenous Log-GARCH Model
|
Author(s):
|
Ming Chen*+ and Qiongxia Song
|
Companies:
|
University of Texas at Dallas and University of Texas at Dallas
|
Keywords:
|
financial volatility ;
log-GARCH ;
exogenous variable ;
semi-parametric regression ;
spline ;
quasi likelihood estimation
|
Abstract:
|
In this article, we develop a new model for nancial volatility estimation and forecasting by including exogenous variables in a semi-parametric log-GARCH model.With additional information, we expect to gain an increased prediction power. We propose a quasi maximum likelihood procedure via spline smoothing technique. Consistent estimators and asymptotic normality are obtained under mild regularity conditions.Simulation experiments provide strong evidence that corroborates the asymptotic theories. Additionally, an application to S&P 500 index data demonstrates strong competitive advantage of our model comparing with GARCH(1,1) and log-GARCH(1,1)models.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.