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Flexible covariance dynamics, high‐frequency data, and optimal futures hedging
Author(s) -
Lai YuSheng
Publication year - 2019
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.22054
Subject(s) - futures contract , covariance , econometrics , computer science , covariance matrix , class (philosophy) , sample (material) , sample mean and sample covariance , empirical research , economics , mathematics , algorithm , financial economics , statistics , artificial intelligence , chemistry , chromatography , estimator
This paper investigates the out‐of‐sample performance of hedged portfolios constructed using a novel rotated ARCH (RARCH) model class, which enables flexible covariance dynamics for spot and futures returns. The model's empirical fit can be significantly improved when it incorporates rotated realized covariance matrix measures. The empirical results suggest that a highly risk‐averse hedger implementing the restricted RARCH model would be willing to pay substantial switching fees to capture the incremental gains generated by the flexible and informative alternative; this thus supports the economic importance of incorporating high‐frequency data into flexible RARCH modeling processes for the construction of optimal hedged portfolios.