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• W2330475988 abstract "This article introduces a new specification for the heterogenous autoregressive (HAR) model for the realized volatility of S&P 500 index returns. In this modeling framework, the coefficients of the HAR are allowed to be time-varying with unspecified functional forms. The local linear method with the cross-validation (CV) bandwidth selection is applied to estimate the time-varying coefficient HAR (TVC-HAR) model, and a bootstrap method is used to construct the point-wise confidence bands for the coefficient functions. Furthermore, the asymptotic distribution of the proposed local linear estimators of the TVC-HAR model is established under some mild conditions. The results of the simulation study show that the local linear estimator with CV bandwidth selection has favorable finite sample properties. The outcomes of the conditional predictive ability test indicate that the proposed nonparametric TVC-HAR model outperforms the parametric HAR and its extension to HAR with jumps and/or GARCH in terms of multi-st..." @default.
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• W2330475988 date "2017-04-27" @default.
• W2330475988 modified "2023-10-03" @default.
• W2330475988 title "Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models" @default.
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• W2330475988 doi "https://doi.org/10.1080/07350015.2016.1138118" @default.
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