Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models

Henry Njagi; Anthony Gichuhi Waititu; Anthony Wanjoya

doi:doi:10.11648/j.ijdsa.20180404.11

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Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models

Henry Njagi, Anthony Gichuhi Waititu, Anthony Wanjoya

Published in International Journal of Data Science and Analysis (Volume 4, Issue 4)

Received: 19 September 2018 Accepted: 9 October 2018 Published: 23 October 2018

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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.

Published in	International Journal of Data Science and Analysis (Volume 4, Issue 4)
DOI	10.11648/j.ijdsa.20180404.11
Page(s)	46-52
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Volatility, Rmse, Ann and Asymmetry Garch Models

References

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Henry Njagi, Anthony Gichuhi Waititu, Anthony Wanjoya. (2018). Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models. International Journal of Data Science and Analysis, 4(4), 46-52. https://doi.org/10.11648/j.ijdsa.20180404.11

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ACS Style

Henry Njagi; Anthony Gichuhi Waititu; Anthony Wanjoya. Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models. Int. J. Data Sci. Anal. 2018, 4(4), 46-52. doi: 10.11648/j.ijdsa.20180404.11

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AMA Style

Henry Njagi, Anthony Gichuhi Waititu, Anthony Wanjoya. Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models. Int J Data Sci Anal. 2018;4(4):46-52. doi: 10.11648/j.ijdsa.20180404.11

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@article{10.11648/j.ijdsa.20180404.11,
  author = {Henry Njagi and Anthony Gichuhi Waititu and Anthony Wanjoya},
  title = {Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models},
  journal = {International Journal of Data Science and Analysis},
  volume = {4},
  number = {4},
  pages = {46-52},
  doi = {10.11648/j.ijdsa.20180404.11},
  url = {https://doi.org/10.11648/j.ijdsa.20180404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20180404.11},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models
AU  - Henry Njagi
AU  - Anthony Gichuhi Waititu
AU  - Anthony Wanjoya
Y1  - 2018/10/23
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdsa.20180404.11
DO  - 10.11648/j.ijdsa.20180404.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 46
EP  - 52
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20180404.11
AB  - 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.
VL  - 4
IS  - 4
ER  -

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Author Information

Henry Njagi

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Gichuhi Waititu

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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APA Style

Henry Njagi, Anthony Gichuhi Waititu, Anthony Wanjoya. (2018). Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models. International Journal of Data Science and Analysis, 4(4), 46-52. https://doi.org/10.11648/j.ijdsa.20180404.11

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ACS Style

Henry Njagi; Anthony Gichuhi Waititu; Anthony Wanjoya. Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models. Int. J. Data Sci. Anal. 2018, 4(4), 46-52. doi: 10.11648/j.ijdsa.20180404.11

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AMA Style

Henry Njagi, Anthony Gichuhi Waititu, Anthony Wanjoya. Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models. Int J Data Sci Anal. 2018;4(4):46-52. doi: 10.11648/j.ijdsa.20180404.11

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@article{10.11648/j.ijdsa.20180404.11,
  author = {Henry Njagi and Anthony Gichuhi Waititu and Anthony Wanjoya},
  title = {Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models},
  journal = {International Journal of Data Science and Analysis},
  volume = {4},
  number = {4},
  pages = {46-52},
  doi = {10.11648/j.ijdsa.20180404.11},
  url = {https://doi.org/10.11648/j.ijdsa.20180404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20180404.11},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models
AU  - Henry Njagi
AU  - Anthony Gichuhi Waititu
AU  - Anthony Wanjoya
Y1  - 2018/10/23
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdsa.20180404.11
DO  - 10.11648/j.ijdsa.20180404.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 46
EP  - 52
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20180404.11
AB  - 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.
VL  - 4
IS  - 4
ER  -

Copy | Download