Asymmetric effects and long memory in the volatility of Dow Jones stocks

Marcel Scharth and Marcelo C. Medeiros

International Journal of Forecasting, 2009, vol. 25, issue 2, pages 304-327

Abstract: Does volatility reflect a continuous reaction to past shocks or do changes in the markets induce shifts in the volatility dynamics? In this paper, we provide empirical evidence that cumulated price variations convey meaningful information about multiple regimes in the realized volatility of stocks, where large falls (rises) in prices are linked to persistent regimes of high (low) variance in stock returns. Incorporating past cumulated daily returns as an explanatory variable in a flexible and systematic nonlinear framework, we estimate that falls of different magnitudes over less than two months are associated with volatility levels 20% and 60% higher than the average of periods with stable or rising prices. We show that this effect accounts for large empirical values of long memory parameter estimates. Finally, we show that, while introducing more realistic dynamics for volatility, the model is able to overall improve or at least retain out-of-sample performance in forecasting when compared to standard methods. Most importantly, the model is more robust to periods of financial crises, when it attains significantly better forecasts.

Keywords: Realized; volatility; Long; memory; Asymmetric; effects; Regime; switching; Regression; trees; Smooth; transition; Forecasting; Empirical; finance (search for similar items in EconPapers)
Date: 2009

Downloads: (external link)
http://www.sciencedirect.com/science/article/B6V92 ... 829ab1710338bb7408e0
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:eee:intfor:v:25:y:2009:i:2:p:304-327

Access Statistics for this article

International Journal of Forecasting is edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Series data maintained by Heidi Boesdal ().