Averaging Heterogeneous Autoregression Models with Heteroskedastic Errors: Theory and an Application to Cryptocurrency Volatility Forecasting

Ziwen Gao, Steven Lehrer (), Tian Xie and Xinyu Zhang

A chapter in Essays in Honor of Subal Kumbhakar, 2024, vol. 46, pp 99-131 from Emerald Group Publishing Limited

Abstract: Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Keywords: Model uncertainty; model averaging; asymptotic optimality; heterogeneous autoregression; cryptocurrency; volatility forecasting; C31; C32; G12; G17 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320240000046006

DOI: 10.1108/S0731-905320240000046006

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