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http://dx.doi.org/10.13106/jafeb.2022.vol9.no10.0001

Forecasting Volatility of Stocks Return: A Smooth Transition Combining Forecasts  

HO, Jen Sim (School of Business and Economics, Universiti Putra Malaysia)
CHOO, Wei Chong (School of Business and Economics, Universiti Putra Malaysia, Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Universiti Putra Malaysia)
LAU, Wei Theng (School of Business and Economics, Universiti Putra Malaysia)
YEE, Choy Leng (School of Business and Economics, Universiti Putra Malaysia)
ZHANG, Yuruixian (School of Business and Economics, Universiti Putra Malaysia)
WAN, Cheong Kin (Faculty of Business and Management, UCSI University)
Publication Information
The Journal of Asian Finance, Economics and Business / v.9, no.10, 2022 , pp. 1-13 More about this Journal
Abstract
This paper empirically explores the predicting ability of the newly proposed smooth transition (ST) time-varying combining forecast methods. The proposed method allows the "weight" of combining forecasts to change gradually over time through its unique feature of transition variables. Stock market returns from 7 countries were applied to Ad Hoc models, the well-known Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family models, and the Smooth Transition Exponential Smoothing (STES) models. Of the individual models, GJRGARCH and STES-E&AE emerged as the best models and thereby were chosen for constructing the combined forecast models where a total of nine ST combining methods were developed. The robustness of the ST combining forecasts is also validated by the Diebold-Mariano (DM) test. The post-sample forecasting performance shows that ST combining forecast methods outperformed all the individual models and fixed weight combining models. This study contributes in two ways: 1) the ST combining methods statistically outperformed all the individual forecast methods and the existing traditional combining methods using simple averaging and Bates & Granger method. 2) trading volume as a transition variable in ST methods was superior to other individual models as well as the ST models with single sign or size of past shocks as transition variables.
Keywords
Stocks Volatility Forecasts; Combining Forecasts; Smooth Transition;
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Times Cited By KSCI : 5  (Citation Analysis)
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