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

Wen Cheong Chin1, Min Cherng Lee2, Grace Lee Ching Yap3

  • 1Faculty of Management, SIG Quantitative Economics and Finance, Multimedia University, 63100 Cyberjaya, Selangor Malaysia.

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|November 23, 2016
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Summary

This study introduces a new econometric model for financial time series, improving volatility forecasting by addressing structural breaks and jumps. The enhanced model offers superior accuracy for risk management and investment analysis.

Keywords:
Heterogeneous autoregressive modelNearest neighbor truncation estimatorStructural break

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Area of Science:

  • Financial econometrics
  • Time series analysis
  • Volatility modeling

Background:

  • High frequency financial data modeling is crucial but challenged by structural breaks causing volatility estimation inconsistencies.
  • Existing models struggle with the volatility of financial time series, impacting risk management.

Purpose of the Study:

  • To propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility model.
  • To incorporate jump-robust estimators for enhanced accuracy in financial volatility forecasting.

Main Methods:

  • Utilized Bai-Perron sequential multi breakpoints procedure to detect volatility breakpoints.
  • Employed nearest neighbor truncation (minimum and median realized volatility) for jump-robust estimators.
  • Developed a modified HAR model incorporating structural break dummy variables and jump-robust estimators.

Main Results:

  • The modified HAR model demonstrated superior in-sample and out-of-sample forecast performance compared to standard HAR models.
  • Improvements in both model structure and volatility estimators led to more accurate forecasts.
  • The enhanced model effectively handles structural breaks and abrupt jumps in financial volatility.

Conclusions:

  • The proposed structural break heavy-tailed HAR model with jump-robust estimators significantly improves financial volatility forecasting.
  • Accurate volatility forecasts are essential for effective risk management and investment portfolio analysis.
  • This research provides a more robust econometric tool for analyzing volatile financial markets.