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-step out-of-sample forecasting, in particular in the post-2003 crisis and 2007 global financial crisis (GFC) periods, during which financial market volatilities were unduly high.
| Original language | English |
|---|---|
| Pages (from-to) | 88-100 |
| Number of pages | 13 |
| Journal | Journal of Business and Economic Statistics |
| Volume | 36 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2018 |
Cite this
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Nonparametric estimation and forecasting for time-varying coefficient realized volatility models. / Chen, Xiangjin B.; Gao, Jiti; Li, Degui; Silvapulle, Param.
In: Journal of Business and Economic Statistics, Vol. 36, No. 1, 2018, p. 88-100.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Nonparametric estimation and forecasting for time-varying coefficient realized volatility models
AU - Chen, Xiangjin B.
AU - Gao, Jiti
AU - Li, Degui
AU - Silvapulle, Param
PY - 2018
Y1 - 2018
N2 - 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-step out-of-sample forecasting, in particular in the post-2003 crisis and 2007 global financial crisis (GFC) periods, during which financial market volatilities were unduly high.
AB - 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-step out-of-sample forecasting, in particular in the post-2003 crisis and 2007 global financial crisis (GFC) periods, during which financial market volatilities were unduly high.
UR - http://www.scopus.com/inward/record.url?scp=85018176012&partnerID=8YFLogxK
U2 - 10.1080/07350015.2016.1138118
DO - 10.1080/07350015.2016.1138118
M3 - Article
VL - 36
SP - 88
EP - 100
JO - Journal of Business and Economic Statistics
JF - Journal of Business and Economic Statistics
SN - 0735-0015
IS - 1
ER -