Arbeitspapier
High frequency vs. daily resolution: The economic value of forecasting volatility models 2nd ed
Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem, such as the discreteness of the data, the properties of the trading mechanism and the existence of bid-ask spread. Moreover, these data are not always available and, even if they are, the asset's liquidity may be not sufficient to allow for frequent transactions. This paper considers different variants of these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumps in prices and leverage effects for volatility. Findings suggest that daily-data models are preferred to HF-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing fat-tails) and leverage effects translates in more accurate VaR measure.
- Sprache
-
Englisch
- Erschienen in
-
Series: Quaderni - Working Paper DSE ; No. 1099
- Klassifikation
-
Wirtschaft
Financial Econometrics
Forecasting Models; Simulation Methods
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Econometrics
Estimation: General
- Thema
-
GARCH
DCS
jumps
leverage effect
high frequency data
realized variation
range estimator
VaR
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Lilla, Francesca
- Ereignis
-
Veröffentlichung
- (wer)
-
Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE)
- (wo)
-
Bologna
- (wann)
-
2017
- DOI
-
doi:10.6092/unibo/amsacta/5541
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Lilla, Francesca
- Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE)
Entstanden
- 2017
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