Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)-type models? Empirical evidence from the stock markets

GÜLAY E., EMEÇ H.

JOURNAL OF FORECASTING, cilt.37, sa.2, ss.133-150, 2018 (SSCI) identifier identifier

  • 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, VOLATILITY MODELS, GARCH MODELS, PRICES
  • 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.