Long Memory and Nonlinearities in Realized Volatility: A Markov Switching Approach

40 Pages Posted: 24 Sep 2010

See all articles by Silvano Bordignon

Silvano Bordignon

University of Padua - Department of Statistical Sciences

Davide Raggi

University of Bologna - Department of Economics

Date Written: February 6, 2010

Abstract

Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics.

We consider an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters, latent process and predictive densities. The insample results show that both long memory and nonlinearities are significant and improve the description of the data. The out-sample resultsat several forecast horizons, show that introducing these nonlinearities produces superior forecasts over those obtained from nested models.

Keywords: Realized volatility, Switching-regime, Long memory, MCMC, Forecasting

Suggested Citation

Bordignon, Silvano and Raggi, Davide, Long Memory and Nonlinearities in Realized Volatility: A Markov Switching Approach (February 6, 2010). Available at SSRN: https://ssrn.com/abstract=1681344 or http://dx.doi.org/10.2139/ssrn.1681344

Silvano Bordignon (Contact Author)

University of Padua - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

Davide Raggi

University of Bologna - Department of Economics ( email )

Piazza Scaravilli 2
Bologna, 40126
Italy

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
95
Abstract Views
590
Rank
565,763
PlumX Metrics