Title: Volatility and risk estimation with linear and nonlinear methods based on high frequency data
Authors : Dettling, Marcel
Bühlmann, Peter
Published in : Applied Financial Economics
Volume(Issue) : 14
Issue : 10
Pages : 717
Pages to: 729
Publisher / Ed. Institution : Routlege
Publisher / Ed. Institution: London
Issue Date: 2004
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : IDP
Subject (DDC) : 332: Financial economics
Abstract: Accurate volatility predictions are crucial for the successful implementation of risk management. The use of high frequency data approximately renders volatility from a latent to an observable quantity, and opens new directions to forecast future volatilities. The goals in this paper are: (i) to select an accurate forecasting procedure for predicting volatilities based on high frequency data from various standard models and modern prediction tools; (ii) to evaluate the predictive potential of those volatility forecasts for both the realized and the true latent volatility; and (iii) to quantify the differences using volatility forecasts based on high frequency data and using a GARCH model for low frequency (e.g. daily) data, and study its implication in risk management for two widely used risk measures. The pay-off using high frequency data for the true latent volatility is empirically found to be still present, but magnitudes smaller than suggested by simple analysis.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Article in scientific Journal
DOI : 10.1080/0960310042000243556
ISSN: 0960-3107
URI: https://digitalcollection.zhaw.ch/handle/11475/4667
Appears in Collections:Publikationen School of Engineering

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