Forecasting realized volatility in a changing world: A dynamic model averaging approach

In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volati...

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Vydáno v:Journal of banking & finance Ročník 64; s. 136 - 149
Hlavní autoři: Wang, Yudong, Ma, Feng, Wei, Yu, Wu, Chongfeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.03.2016
Elsevier Sequoia S.A
Témata:
Averaging
Dynamic model averaging
Econometrics
Economic models
Forecasting
Forecasting techniques
Portfolio
Realized volatility
S&P 500 index
Securities markets
Studies
Time-varying parameters
Volatility
S&P 500 index
C22
Realized volatility
Time-varying parameters
C58
Portfolio
G13
G14
Dynamic model averaging
ISSN:0378-4266, 1872-6372
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Shrnutí:In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting.
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ISSN:0378-4266
1872-6372
DOI:10.1016/j.jbankfin.2015.12.010