Arbeitspapier
High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models
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 of 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 jumping prices and leverage effects for volatility. Findings suggest that GARJI model provides more accurate VaR measures for the S&P 500 index than RV models. Furthermore, the assumption of conditional normality is shown to be not sufficient to obtain accurate risk measures even if jump contribution is provided. More sophisticated models might address this issue, improving VaR results.
- Language
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Englisch
- Bibliographic citation
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Series: Quaderni - Working Paper DSE ; No. 1084
- Classification
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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
- Event
-
Geistige Schöpfung
- (who)
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Lilla, Francesca
- Event
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Veröffentlichung
- (who)
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Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE)
- (where)
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Bologna
- (when)
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2016
- DOI
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doi:10.6092/unibo/amsacta/5444
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Lilla, Francesca
- Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE)
Time of origin
- 2016
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