Browsing by Author "Emenogu, N.G."

Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemOpen Access
    The Impact of Global Financial Crisis and COVID-19 Pandemic on Crude Oil Futures Returns
    (Department of Statistics, Nasarawa Sate University Keffi., 2023-01-09) Adenomon, Monday Osagie; Emenogu, N.G.; Idowu, Richard A.
    This study investigates the impact of the global financial crisis and of the present COVID-19 pandemic on daily and weekly Crude oil futures using four variants of ARMA-CARCH models: ARMA- sCARCH, ARMA-eGARCH, ARMA-TGARCH and ARMA- aPARCH with dummy variables We also investigated the persistence, half-life and backtesting of the models. This study therefore seeks to contribute to the body of literature on the impact of the global financial crisis and the present COVID- 19 pandemic on the crude oil futures market. ٦he Impact of the global financial crisis and the COVID- 19 on the crude oil futures has not been investigated at present. We obtained and analyzed the daily and weekly crude oil futures from secondary sources. ٦he daily crude oil futures used in this study cover the period from 4th January 2000 to 27th April 2020 while the weekly crude oil futures covered the period from 2nd January 2000 to 26th April 2020. ٦he global financial crisis period covered the period from 2nd 2007 لاالال to 3151 March 2009 and the current COVID-19 pandemic covered the period from 1st January 2020 to 27th April, 2020. ٦he study used both Student t and skewed Student t innovations with AIC, goodness-of-test fit and backtesting to select the best model. Most of the estimated ARMA-GARCH models are supported by skewed Student t distribution while most of the ARMAGARCH models exhibited high persistence values in the presence of the global financial crisis and the COVID-19 pandemic. In the overall, the estimated ARMA(1,0)-eGARCH(2,1) and ARMA(1,0)_ eGARCH(2,2) model for dally crude oil futures and weekly crude oil futures respectively have been significantly Impacted by the global financial crisis and the Present COVID-19 pandemic while the preferred estimated models also passed the goodness-of-test fit and backtesting. This study recommends shareholders and investors should think outside the box as crude oil futures tend to be affected by the global financial crisis and COVID-19 pandemic while countries also that depend mostly on crude oil are encouraged to diversify their economy in order to survive and be sustained during the financial and health crisis.
  • Thumbnail Image
    ItemOpen Access
    MODELING AND FORECASTING DAILY STOCK RETURNS OF GUARANTY TRUST BANK NIGERIA PLC USING ARMA-GARCH MODELS, PERSISTENCE, HALF-LIFE VOLATILITY AND BACKTESTING
    (Department of Statistics, Nasarawa Sate University Keffi., 2019-06-05) Emenogu, N.G.; Adenomon, Monday Osagie; Nweze, N.O.
    This study investigated the forecasting ability of GARCH family models, and to achieve superior and more reliable models for volatility persistence, half-life volatility and backtesting, the study combined the ARMA and GARCH models. The study modeled and forecasted the Guaranty Trust Bank (GTB) daily stock returns using data from January 2, 2001 to May 8, 2017 obtained from a secondary source. The ARMA-GARCH models, persistence, half- life and backtesting were used to analyse the data using student t and skewed student t distributions, and the analyses were carried out in R environment using rugarch and performanceAnaytics Packages. The study revealed that using the lowest information criteria values alone could be misleading so backtesing was also carried out. The ARMA(1,1)-GARCH(1,1) models fitted exhibited high persistency in the daily stock returns while it took about 6 days for mean-reverting of the models, but failed backtesting. However, backtesting showed that ARMA(1,1)-eGARCH(2,2) model with student t distribution passed the test and was suitable for evaluating the GTB stock returns, and required about 16 days for the persistence volatility to return to its average value of the stock returns. The study recommended addition of backtesting approach in evaluating the performance of GARCH model in order to avoid misleading results. Also, the GTB stocks can be predicted since most of the estimated models were stable.
  • Thumbnail Image
    ItemOpen Access
    On the performance of GARCH family models using the root mean square error and the mean absolute error
    (Department of Statistics, Nasarawa Sate University Keffi., 2018-02-02) Emenogu, N.G.; Adenomon, Monday Osagie; Nweze, N.O.
    It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). In a simulation iteration of 1000 times in R environment using rugarch, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH was superior, for medium size of outliers, it was sGARCH and gjrGARCH that were superior irrespective of the time series length, and for a large size of outliers, irrespective of the time series length, gjrGARCH was superior. The study leveled that in the presence of additive outliers, both RMSE and MAE values would increase as the time series length is increased.
  • Thumbnail Image
    ItemOpen Access
    On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting
    (Department of Statistics, Nasarawa Sate University Keffi., 2020-08-06) Emenogu, N.G.; Adenomon, Monday Osagie; Nweze, N.O.
    This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models: sGARCH, girGARCH, eGARCH, iGARCH, aGARCH, TGARCH, NGARCH, NAGARCH, and AVGARCH along with value at risk estimation and backtesting. We use daily data for Total Nigeria Plc returns for the period January 2, 2001 to May 8, 2017, and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations. This investigation of the volatility, VaR, and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach. We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable. Additionally, for student t innovation, the sGARCH and girGARCH models failed to converge; the mean reverting number of days for returns differed from model to model. From the analysis of VaR and its backtesting, this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices. Furthermore, risk was reflected by significant up and down movement in the stock price at a 99% confidence level, suggesting that high risk brings a high return.
  • Thumbnail Image
    ItemOpen Access
    Robustness of GARCH family models to high positive autocorrelation
    (Department of Statistics, Nasarawa Sate University Keffi., 2020-08-08) Emenogu, N.G.; Adenomon, Monday Osagie
    This study compared the performance of five Family Generalized Auto-Regressive Conditional Heteroscedastic (fGARCH) models (sGARCH, gjrGARCH, iGARCH, TGARCH and NGARCH) in the presence of high positive autocorrelation. To achieve this, financial time series was simulated with autocorrelated coefficients as ρ = (0.8, 0.85, 0.9, 0.95, 0.99), at different time series lengths (as 250, 500, 750, 1000, 1250, 1500) and each trial was repeated 1000 times carried out in R environment using rugarch package. The performance of the preferred model was judged using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results from the simulation revealed that these GARCH models performances varies with the different autocorrelation values and at different time series lengths. But in the overall, NGARCH model dominates with 62.5% and 59.3% using RMSE and MAE respectively. We therefore recommended that investors, financial analysts and researchers interested in stock prices and asset return should adopt NGARCH model when there is high positive autocorrelation in the financial time series data.