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FORECASTING THE END-OF-THE-DAY REALIZED VARIANCE

100%

Doman M., Doman R.

Metody Ilościowe w Badaniach Ekonomicznych

2009

vol. 10

issue 1

67-75

A large package of information is being reflected in stock prices during a short period after opening. Moreover, the start-of-the-day (morning) volatility has a strong impact on the price variability during all the day. In this connection, the question is whether the morning realized variance calculated as the sum of morning squared intraday returns can be useful in forecasting the daily realized variance (end-of-the-day volatility). In the paper, we apply three different methods of forecasting the daily realized variance for stocks quoted on the Warsaw Stock Exchange Our findings show that the morning realized variance provides valuable information that can be used in forecasting the daily realized variance.

Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach

75%

AlـGounmeein R. S., Ismail M. T.

Statistics in Transition new series

2021

vol. 22

issue 1

29-54

The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which is the primary objective of this study. The other goal of this paper is to expand on the researchers' previous work by examining long memory and volatilities simultaneously, by using the ARFIMA-sGARCH hybrid model and comparing it against the ARFIMA-fGARCH hybrid model. Consequently, the hybrid models were configured with the monthly Brent crude oil price series for the period from January 1979 to July 2019. These datasets were considered as the global economy is currently facing significant challenges resulting from noticeable volatilities, especially in terms of the Brent crude prices, due to the outbreak of COVID-19. To achieve these goals, an R/S analysis was performed and the aggregated variance and the Higuchi methods were applied to test for the presence of long memory in the dataset. Furthermore, four breaks have been detected: in 1986, 1999, 2005, and 2013 using the Bayes information criterion. In the further section of the paper, the Hurst Exponent and Geweke-Porter-Hudak (GPH) methods were used to estimate the values of fractional differences. Thus, some ARFIMA models were identified using AIC (Akaike Information Criterion), BIC (Schwartz Bayesian Information Criterion), AICc (corrected AIC), and the RMSE (Root Mean Squared Error). In result, the following conclusions were reached: the ARFIMA(2,0.3589648,2)-sGARCH(1,1) model and the ARFIMA(2,0.3589648,2)-fGARCH(1,1) model under normal distribution proved to be the best models, demonstrating the smallest values for these criteria. The calculations conducted herein show that the two models are of the same accuracy level in terms of the RMSE value, which equals 0.08808882, and it is this result that distinguishes our study. In conclusion, these models can be used to predict oil prices more accurately than others.

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1 Metody Ilościowe w Badaniach Ekonomicznych

1 Statistics in Transition new series

1 AlـGounmeein R. S.

1 Doman M.

1 Doman R.

1 Ismail M. T.

1 2021

1 2009

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FORECASTING THE END-OF-THE-DAY REALIZED VARIANCE

Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach