|Title:||Volatility and risk estimation with linear and nonlinear methods based on high frequency data|
|Authors :||Dettling, Marcel|
|Published in :||Applied Financial Economics|
|Publisher / Ed. Institution :||Routlege|
|Publisher / Ed. Institution:||London|
|License (according to publishing contract) :||Licence according to publishing contract|
|Type of review:||Peer review (Publication)|
|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|
|Appears in Collections:||Publikationen School of Engineering|
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