Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)-type models? Empirical evidence from the stock markets
JOURNAL OF FORECASTING, cilt.37, sa.2, ss.133-150, 2018 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 37 Sayı: 2
- Basım Tarihi: 2018
- Doi Numarası: 10.1002/for.2478
- Dergi Adı: JOURNAL OF FORECASTING
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus
- Sayfa Sayıları: ss.133-150
- Anahtar Kelimeler: ARCH, GARCH models, financial time series, forecasting, forecasting performance measures, NoVaS, volatility
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
In this paper, we present a comparison between the forecasting performances of the normalization and variance stabilization method (NoVaS) and the GARCH(1,1), EGARCH(1,1) and GJR-GARCH(1,1) models. Hence the aim of this study is to compare the out-of-sample forecasting performances of the models used throughout the study and to show that the NoVaS method is better than GARCH(1,1)-type models in the context of out-of sample forecasting performance. We study the out-of-sample forecasting performances of GARCH(1,1)-type models and NoVaS method based on generalized error distribution, unlike normal and Student's t-distribution. Also, what makes the study different is the use of the return series, calculated logarithmically and arithmetically in terms of forecasting performance. For comparing the out-of-sample forecasting performances, we focused on different datasets, such as S&P 500, logarithmic and arithmetic BST 100 return series. The key result of our analysis is that the NoVaS method performs better out-of-sample forecasting performance than GARCH(1,1)-type models. The result can offer useful guidance in model building for out-of-sample forecasting purposes, aimed at improving forecasting accuracy.