Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data

Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data

Background: Since high-frequency data have become available, an unbiased volatility estimator, i.e. realized variance (RV) can be computed. Commonly used models for RV forecasting suffer from strong persistence with a high sensitivity to the returns distribution assumption and they use only daily re...

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Bibliographic Details
Main Authors: Arnerić Josip, Poklepović Tea, Teai Juin Wen
Format: Article
Language:English
Published: Sciendo 2018-07-01
Series:Business Systems Research
Subjects:
high-frequency data
realized variance
nonlinearity
long memory
jumps
leverage
feedforward neural networks
heterogeneous autoregressive model
Online Access:https://doi.org/10.2478/bsrj-2018-0016

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