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A Bivariate High‐Frequency‐Based Volatility Model for Optimal Futures Hedging
Author(s) -
Lai YuSheng,
Lien Donald
Publication year - 2017
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.21841
Subject(s) - econometrics , futures contract , autoregressive conditional heteroskedasticity , bivariate analysis , autoregressive model , volatility (finance) , economics , hedge , equity (law) , mathematics , financial economics , statistics , ecology , political science , law , biology
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. © 2017 Wiley Periodicals, Inc. Jrl Fut Mark 37:913–929, 2017