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Enhancing Efficiency of Local Projections Estimation with Volatility Clustering in High-Frequency Data

Author

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  • Chew Lian Chua
  • David Gunawan
  • Sandy Suardi

Abstract

This paper advances the local projections (LP) method by addressing its inefficiency in high-frequency economic and financial data with volatility clustering. We incorporate a generalized autoregressive conditional heteroskedasticity (GARCH) process to resolve serial correlation issues and extend the model with GARCH-X and GARCH-HAR structures. Monte Carlo simulations show that exploiting serial dependence in LP error structures improves efficiency across forecast horizons, remains robust to persistent volatility, and yields greater gains as sample size increases. Our findings contribute to refining LP estimation, enhancing its applicability in analyzing economic interventions and financial market dynamics.

Suggested Citation

  • Chew Lian Chua & David Gunawan & Sandy Suardi, 2025. "Enhancing Efficiency of Local Projections Estimation with Volatility Clustering in High-Frequency Data," Papers 2503.02217, arXiv.org.
    • Handle: RePEc:arx:papers:2503.02217
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    References listed on IDEAS

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    1. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
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