City Research Online

Abstract

Despite their effectiveness, linear models for realized variance neglect measurement errors on integrated variance and exhibit several forms of misspecification due to the inherent nonlinear dynamics of volatility. We propose new extensions of the popular approximate long-memory heterogeneous autoregressive (HAR) model apt to disentangle these effects and quantify their separate impact on volatility forecasts. By combining the asymptotic theory of the realized variance estimator with the Kalman filter and by introducing time-varying HAR parameters, we build new models that account for: (i) measurement errors (HARK), (ii) nonlinear dependencies (SHAR) and (iii) both measurement errors and nonlinearities (SHARK). The proposed models are simply estimated through standard maximum likelihood methods and are shown, both on simulated and real data, to provide better out-of-sample forecasts compared to standard HAR specifications and other competing approaches.

Publication Type:	Article
Additional Information:	This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of Financial Econometrics following peer review. The version of record Giuseppe Buccheri, Fulvio Corsi, HARK the SHARK: Realized Volatility Modeling with Measurement Errors and Nonlinear Dependencies, Journal of Financial Econometrics, is available online at: https://doi.org/10.1093/jjfinec/nbz025
Publisher Keywords:	Realized Volatility, HAR, Measurement Errors, Nonlinear Time Series, Score Driven Models, Kalman Filter
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HG Finance
Departments:	School of Policy & Global Affairs > Economics
SWORD Depositor:	Symplectic Administrator

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