High Frequency vs. Daily Resolution: The Economic Value of Forecasting Volatility Models

Quaderni - Working Paper DSE N° 1084

35 Pages Posted: 17 Nov 2016

See all articles by Francesca Lilla

Francesca Lilla

University of Bologna - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: November 16, 2016

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 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.

Keywords: GARCH, DCS, jumps, leverage effect, high frequency data, realized variation, range estimator, VaR

JEL Classification: C58, C53, C22, C01, C13

Suggested Citation

Lilla, Francesca, High Frequency vs. Daily Resolution: The Economic Value of Forecasting Volatility Models (November 16, 2016). Quaderni - Working Paper DSE N° 1084, Available at SSRN: https://ssrn.com/abstract=2870521 or http://dx.doi.org/10.2139/ssrn.2870521

Francesca Lilla (Contact Author)

University of Bologna - Department of Economics ( email )

Strada Maggiore 45
Bologna, 40125
Italy

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