Heterogeneous autoregressive model with structural break using nearest neighbor truncation volatility estimators for DAX

High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural b...

Full description

Saved in:
Bibliographic Details
Published in:SpringerPlus 2016-11, Vol.5 (1), p.1883-1883, Article 1883
Main Authors: Chin, Wen Cheong, Lee, Min Cherng, Yap, Grace Lee Ching
Format: Article
Language:English
Subjects:
Business and Economics
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai–Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.
ISSN:2193-1801
2193-1801
DOI:10.1186/s40064-016-3465-x