Artikel
Volatility forecasting: Downside risk, jumps and leverage effect
We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we estimate the size of positive and negative jumps and propose a methodology to estimate the size of jumps in the quadratic variation. The leverage effect is separated into continuous and discontinuous effects, and past volatility is separated into "good" and "bad", as well as into continuous and discontinuous risks. Using a long history of the S & P500 price index, we find that the continuous leverage effect lasts about one week, while the discontinuous leverage effect disappears after one day. "Good" and "bad" continuous risks both characterize the volatility persistence, while "bad" jump risk is much more informative than "good" jump risk in forecasting future volatility. The volatility forecasting model proposed is able to capture many empirical stylized facts while still remaining parsimonious in terms of the number of parameters to be estimated.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 4 ; Year: 2016 ; Issue: 1 ; Pages: 1-24 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Estimation: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
Forecasting Models; Simulation Methods
Financial Econometrics
- Thema
-
high frequency data
realized volatility forecasting
downside risk
leverage effect
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Audrino, Francesco
Hu, Yujia
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2016
- DOI
-
doi:10.3390/econometrics4010008
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Audrino, Francesco
- Hu, Yujia
- MDPI
Entstanden
- 2016
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