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    Tytuł:
    Evaluation of Forecasts Performance of ARIMA-GARCH-type Models in the Light of Outliers
    Autorzy:
    Akpan, Emmanuel Alphonsus
    Lasisi, K. E.
    Adamu, Ali
    Rann, Haruna Bakari
    Powiązania:
    https://bibliotekanauki.pl/articles/1075685.pdf
    Data publikacji:
    2019
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    ARIMA Model
    Forecast
    GARCH Model
    Heteroscedasticity
    Outlier
    Volatility
    Opis:
    The carry-over effect of biased estimates of ARIMA-GARCH-type models parameters on forecasting accuracy is investigated in the presence of outliers by exploring the daily returns of share price series of three major banks in Nigerian. The banks considered are Diamond, United bank for Africa and Union. The data were collected from the Nigerian Stock Exchange and spanned from January 3, 2006 to December 30, 2016, comprises 2713 observations and were divided into two portions. The first portion which ranges from January 3, 2006 to November 24, 2016, comprises 2690 observations was used for model formulation and the second portion which ranges from November 25, 2016 to December 30, 2016, consisting of 23 observations was used for out-of-sample forecasting performance evaluation. The parametric bootstrap technique was used in computing the forecasts while Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Error (ME) were the methods of forecast evaluation considered. The findings of this study showed that in the presence of outliers, the forecasts were found to be biased as indicated by ME and the accuracy reduced as shown by MSE, RMSE and MAE. However, adjusting for the outliers, only marginal improvement on the forecasts was observed, reason being that all the outliers were treated as innovations and they occurred before the forecasts origin.
    Źródło:
    World Scientific News; 2019, 119; 68-84
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application out-of-sample forecasting in model selection on Nigeria exchange rate
    Autorzy:
    Henry, Akpensuen Shiaondo
    Lasisi, K. E.
    Akpan, E. A.
    Gwani, A. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1062858.pdf
    Data publikacji:
    2019
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    ARMA model
    Exchange Rate
    In-sample forecasting
    Model selection and evaluation
    Out-sample forecasting
    Opis:
    In time series, several competing models may adequately fit a given set of data. At times choosing the best model may be easy or difficult. However, there are two major model selection criteria; it could be either in-sample or out-of-sample forecasts. This study was necessitated because Empirical evidence based on out-of-sample model forecast performance is generally considered more trustworthy than evidence based on in-sample model performance which can be more sensitive to outliers and data mining. And also the fact that Out-of-sample forecasts also better reflect the information available to the forecaster in real time was also an added motivation. Hence this study considered data from Nigeria exchange rate (Naira to US Dollar) from January 2002 to December 2018 comprising 204 observations. The first 192 observations were used for model identification and estimation while the remaining 12 observations were holdout for forecast validation. Three ARIMA models; ARIMA (0, 1, 1), ARIMA (1, 1, 2) and ARIMA (2, 1, 0) were fitted tentatively. Base on in-sample information criteria ARIMA (0, 1, 1) was the best model with minimum AIC, SIC and HQ information criteria. However, on the basics of out-of-sample forecast evaluation using RMSE, MSE, MAE, and MAPE, ARIMA (2, 1, 0) perform better than ARIMA (0, 1, 1). The implication of this study is that, a model that is best in the in-sample fitting may not necessary give a genuine forecasts since it is the same data that is used in model identification and estimation that is also use in forecast evaluation.
    Źródło:
    World Scientific News; 2019, 127, 3; 225-247
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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