Author: Wu, Kejin; Karmakar, Sayar
Title: Boosting Model-free predictions for econometric datasets Cord-id: abp83r7c Document date: 2021_1_6
ID: abp83r7c
Snippet: This article explores the existing normalizing and variance-stabilizing (NoVaS) method on predicting financial volatility. First, we explore the robustness of the existing NoVaS method for long-term predictions. Then we develop a more parsimonious variant of the existing method. With systematic justification and extensive data analysis, our new method shows better performance than current NoVaS and standard GARCH(1,1) methods on both short- and long-term predictions.
Document: This article explores the existing normalizing and variance-stabilizing (NoVaS) method on predicting financial volatility. First, we explore the robustness of the existing NoVaS method for long-term predictions. Then we develop a more parsimonious variant of the existing method. With systematic justification and extensive data analysis, our new method shows better performance than current NoVaS and standard GARCH(1,1) methods on both short- and long-term predictions.
Search related documents:
Co phrase search for related documents, hyperlinks ordered by date