Comparing volatility forecasting models during the global financial crisis. Issue 7 (9th August 2017)

• Record Type: Journal Article
• Authors: De Pinho, Frank M.; Couto, Ricardo F.
• Is Part Of: Communications in statistics. Volume 46:Issue 7(2017)
• Page Start: 5257
• Page End: 5270
• Publication Date: 2017-08-09
• Subjects: APARCH models -- Classical and Bayesian inferences -- Heavy-tail distributions -- Non-Gaussian State Space Models -- Volatility models; 62M10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1152363
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Volatility estimation in financial markets has always been a challenge especially in time of crisis. Once asset prices and investment decisions are highly sensitive to such variable, many different models have been proposed in literature. This article estimates the volatility from a new family of stochastic volatility models called non-Gaussian State Space Models, a subclass of state space models where it is possible to compute exact likelihood. Volatilities of important Asian and Oceanian stock market indexes have been estimated and compared to APARCH model estimates. Results showed that non-Gaussian State Space Models outperformed significantly in both in-sample and forecasting cases.