Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models
Henry Njagi,
Anthony Gichuhi Waititu,
Anthony Wanjoya
Issue:
Volume 4, Issue 4, August 2018
Pages:
46-52
Received:
19 September 2018
Accepted:
9 October 2018
Published:
23 October 2018
Abstract: The stock price in developing countries, especially in Kenya, has become one of the market that supports the economy growth of a country. Due to the political instabilities in the Kenyan contest, stock price markets have been affected. As a consequence of the instabilities in the financial markets, this paper model the volatility associated with the stock price for a one day ahead volatility forecast which will help in risk control in the market. This is accomplished by using the asymmetry GARCH and ANN-asymmetry GARCH models. The residuals obtained from artificial neural network are used when fitting ANN- asymmetry GARCH models. It was found that returns on the selected companies in NSE are categorized by volatility clustering, leptokurtosis and asymmetry. In the modelling, we further examine the performance of the leading alternatives with the daily log returns residuals of the leading companies in Kenyan stock market (PAFR, PORT and EGAD) from the period January 2006 to November 2017 for trading days excluding weekends and holidays. The root mean squared error indicated that among the available models i.e. ANN-EGARCH model, GJR-GARCH and EGARCH model, ANN-GJR-GARCH model performed better in modelling and forecasting the stock price volatility in Kenyan contest. The paper demonstrates that combined machine learning and statistical models can effectively model stock price volatility and make reliable forecasts.
Abstract: The stock price in developing countries, especially in Kenya, has become one of the market that supports the economy growth of a country. Due to the political instabilities in the Kenyan contest, stock price markets have been affected. As a consequence of the instabilities in the financial markets, this paper model the volatility associated with th...
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Robust Estimation of Finite Population Totals Using a Model Based Approach in the Presence of Two Auxiliary Variables
Damaris Felistus Mulwa,
George Otieno Orwa,
Romanus Odhiambo
Issue:
Volume 4, Issue 4, August 2018
Pages:
53-57
Received:
16 October 2018
Accepted:
31 October 2018
Published:
14 November 2018
Abstract: The utilization of auxiliary information during surveys increases the accuracy of estimators, thereby giving more reliable estimates of the population parameters of interest. It has been established that the presence of more than one auxiliary variables, some more robust estimators can be formed by combining different estimators like product, ratio or even regression estimators and in each case the individual estimators uses its own random variable. One of the most commonly used methods is the ratio method of estimating finite totals which is the foundation of all the other methods that use auxiliary information. In this paper, an estimator of the ratio-exponential class that uses two auxiliary variables has been proposed and its variance derived. After deriving the proposed estimator the coverage probabilities were estimated. Results showed that the interval length of the proposed estimator was narrower and tighter than that of the known Horwitz-Thompson’s estimator. Two datasets from the agricultural and environmental sectors were used in order to investigate the properties of the estimator and they gave satisfactory results. Mean squared error criteria was used to investigate the performance of the proposed estimator and in both cases it had the minimum squared error values. The analysis in these paper is of very great importance in understanding environmental and agricultural data.
Abstract: The utilization of auxiliary information during surveys increases the accuracy of estimators, thereby giving more reliable estimates of the population parameters of interest. It has been established that the presence of more than one auxiliary variables, some more robust estimators can be formed by combining different estimators like product, ratio...
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