High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed

F. Lilla

Working Papers from Dipartimento Scienze Economiche, Universita' di Bologna

Abstract: 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.

JEL-codes: C01 C13 C22 C53 C58 (search for similar items in EconPapers)
Date: 2017-04
New Economics Papers: this item is included in nep-ets, nep-for and nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://amsacta.unibo.it/5541/1/WP1099.pdf (application/pdf)

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: https://EconPapers.repec.org/RePEc:bol:bodewp:wp1099

Access Statistics for this paper

More papers in Working Papers from Dipartimento Scienze Economiche, Universita' di Bologna Contact information at EDIRC.
Bibliographic data for series maintained by Dipartimento Scienze Economiche, Universita' di Bologna ().