HARK the SHARK: Realized Volatility Modeling with Measurement Errors and Nonlinear Dependencies*

Giuseppe Buccheri and Fulvio Corsi

Journal of Financial Econometrics, 2021, vol. 19, issue 4, 614-649

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.

Keywords: realized volatility; HAR; measurement errors; nonlinear time series; score-driven models; Kalman filter (search for similar items in EconPapers)
JEL-codes: C22 C53 C58 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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