Forecasting Volatility Stock Return: Evidence from the Nordic Stock Exchanges

Abstract

The purpose of this study is to explore the volatility and secondary effects in the four Nordic stock exchanges of Norway: Oslo Børs Linked all-share index AXLT Denmark: OMX Copenhagen 20, Sweden: OMX Stockholm 30 and Finland: OMX Helsinki 25. Keeping in mind that there is an ARCH effect in the returns of the four stock exchanges, we move on to the evaluation to the evaluation of models ARCH (q), GARCH (p, q) και GARCH-M (p, q). Evaluating the parameters became possible through the use of the maximum likelihood method using the BHHH algorithm of (Berndt et al., 1974) and the three distributions (normal, t-Student, and the Generalized normal distribution GED). The results of this study indicate model ARMA(0,1)-GARCH-Μ(1,1) with t-student distribution as the appropriate one to describe the returns of the all Nordic stock exchanges except that of Sweden, where model ARMA(0,3)-GARCH-Μ(1,1) describes it best. Lastly, for forecasting the models ARMA(0,1)-GARCH-Μ(1,1) and ARMA(0,3)-GARCH-Μ(1,1) of the current stock exchanges we use both the dynamic and static process. The results of this study indicate that the static process forecasts better than the corresponding dynamic.

Modeling the Volatility of Daily Listed Real Estate Returns during Economic Crises: Evidence from Generalized Autoregressive Conditional Heteroscedasticity Models

“…We compare the test results to previous studies which adopted similar empirical strategies on Scandinavian stock markets, and find that the volatility pattern of the Swedish real estate sector-leverage effect and long-range dependence specifically-follow similar movement of the stock market as a whole [34][35][36][37]. Further, the Swedish Real Estate Sector Index's average mean-reverting half-life period is 49 days, which is longer than other developed markets such as NYSE Composite and Nikkei [63], but faster than emerging markets such as the Saudi Stock Exchange (30 months) [62].…”

“…Empirical volatility studies of the Scandinavian region are limited. Dritsakis and Savvas [34] explored the volatility of four Nordic stock exchanges in Norway, Denmark, Sweden, and Finland using ARCH (q), GARCH (p, q), and GARCH-M(p, q) models, which were evaluated through MSE, MAE, RMSE, and MAPE. They found that ARMA (0, 1)-GARCH-M (1, 1) and ARMA (0, 3)-GARCH (1, 1) models are the most accurate in terms of Student's-t distribution.…”

Pendugaan Imbal Hasil Saham dengan Model Autoregressive Moving Average

“…Imbal hasil saham merupakan salah satu topik penting dalam dunia ekonomi. Evaluasi terhadap suatu aset dapat dilakukan dengan menggunakan imbal hasil saham tersebut, yang secara konvensional didefinisikan sebagai fungsi logaritma natural terhadap perubahan harga [1]. Namun, apakah imbal hasil saham di masa depan dapat diduga menjadi pertanyaan yang krusial dikemukakan oleh Ding, et.al [2].…”