A Bivariate High-Frequency-Based Volatility Model for Optimal Futures Hedging

Yu Sheng Lai, Donald Lien

Research output: Research - peer-reviewArticle

Abstract

This study examines the usefulness of high-frequency data for estimating hedge ratios for different hedging horizons. By jointly modeling the returns and conditional expectation of the covariation, the multivariate high-frequency-based volatility (HEAVY) model generates spot-futures distributions over longer horizons. Using the data on international equity index futures, performance comparisons between HEAVY and generalized autoregressive conditional heteroskedasticity (GARCH) hedge ratios indicate that HEAVY hedge ratios perform more effectively than GARCH hedge ratios at shorter hedging horizons. This implies that the distinct properties of short-time response and short-run momentum effects revealed in the HEAVY model are vital for hedge ratio estimation.

LanguageEnglish (US)
JournalJournal of Futures Markets
DOIs
StateAccepted/In press - 2017

Fingerprint

Hedge ratio
Volatility models
Hedging
Autoregressive conditional heteroskedasticity
High-frequency data
Usefulness
Equity
Conditional expectation
Momentum effect
Response time
Modeling
Short-run

ASJC Scopus subject areas

  • Accounting
  • Business, Management and Accounting(all)
  • Finance
  • Economics and Econometrics

Cite this

A Bivariate High-Frequency-Based Volatility Model for Optimal Futures Hedging. / Lai, Yu Sheng; Lien, Donald.

In: Journal of Futures Markets, 2017.

Research output: Research - peer-reviewArticle

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