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.
Highlights
Stock price is the highest or the lowest amount that a stock can be bought
Since the problem of identifying the best statistical model in modelling volatility has been an issue, this study shows the capability of combined models and the use of artificial neural network together with asymmetric models in modelling the volatility
The asymmetry GARCH models address the challenge of heavy tails in financial data and can be further used to forecast the volatility, the main aim of this work is to comprehend the major strengths and limitations of the GARCH models and Artificial Neural Network in estimating the volatility
Summary
Stock price is the highest or the lowest amount that a stock can be bought. The growth of economy draws attention in the field of technology. There had been a major concern about which methods can be used to measure the price volatility. There are basically two broad methods of modelling volatility namely; analytical and historical approach. The asymmetry GARCH models address the challenge of heavy tails in financial data and can be further used to forecast the volatility, the main aim of this work is to comprehend the major strengths and limitations of the GARCH models and Artificial Neural Network in estimating the volatility. The remainder concerns of this paper comprises part 2 explores past works that exist, part 3 explores the GARCH family models and Artificial Neural Network and outlines basic calculation on how to do forecasting of the stock price return volatility based on the asymmetry GARCH models and Artificial-Asymmetry GARCH models. Part 7 provides a conclusion and recommendations for further studies
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