On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting

Abstract 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 Pl...

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Main Authors: Ngozi G. Emenogu, Monday Osagie Adenomon, Nwaze Obini Nweze
Format: Article
Language:English
Published: SpringerOpen 2020-03-01
Series:Financial Innovation
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40854-020-00178-1
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spelling doaj-bb51ad7e387a426c96dd7720f523ecbd2020-11-25T03:35:37ZengSpringerOpenFinancial Innovation2199-47302020-03-016112510.1186/s40854-020-00178-1On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtestingNgozi G. Emenogu0Monday Osagie Adenomon1Nwaze Obini Nweze2Department of Statistics, Federal PolytechnicDepartment of Statistics, Nasarawa State Univ ersityDepartment of Statistics, Nasarawa State Univ ersityAbstract 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.http://link.springer.com/article/10.1186/s40854-020-00178-1VolatilityReturnsStocksTotal petroleumAkaike information criterion (AIC)GARCH
collection DOAJ
language English
format Article
sources DOAJ
author Ngozi G. Emenogu
Monday Osagie Adenomon
Nwaze Obini Nweze
spellingShingle Ngozi G. Emenogu
Monday Osagie Adenomon
Nwaze Obini Nweze
On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting
Financial Innovation
Volatility
Returns
Stocks
Total petroleum
Akaike information criterion (AIC)
GARCH
author_facet Ngozi G. Emenogu
Monday Osagie Adenomon
Nwaze Obini Nweze
author_sort Ngozi G. Emenogu
title On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting
title_short On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting
title_full On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting
title_fullStr On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting
title_full_unstemmed On the volatility of daily stock returns of Total Nigeria Plc: evidence from GARCH models, value-at-risk and backtesting
title_sort on the volatility of daily stock returns of total nigeria plc: evidence from garch models, value-at-risk and backtesting
publisher SpringerOpen
series Financial Innovation
issn 2199-4730
publishDate 2020-03-01
description Abstract 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.
topic Volatility
Returns
Stocks
Total petroleum
Akaike information criterion (AIC)
GARCH
url http://link.springer.com/article/10.1186/s40854-020-00178-1
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