| Autor: |
Chin WC; Faculty of Management, SIG Quantitative Economics and Finance, Multimedia University, 63100 Cyberjaya, Selangor Malaysia., Lee MC; Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43300 Kajang, Selangor Malaysia., Yap GL; Faculty of Engineering, University of Nottingham (Malaysia Campus), 43500 Semenyih, Selangor Malaysia. |
| Jazyk: |
angličtina |
| Zdroj: |
SpringerPlus [Springerplus] 2016 Nov 06; Vol. 5 (1), pp. 1883. Date of Electronic Publication: 2016 Nov 06 (Print Publication: 2016). |
| DOI: |
10.1186/s40064-016-3465-x |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
