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 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.

COVID-19 and instability of stock market performance: evidence from the U.S.

“…In terms of stock volatility, academic researchers used to make the forecasts by traditional GARCH models using indicators based on the past behavior of stock price and volatility (Gokcan 2000 ; Emenogu et al 2020 ). More recent studies become aware of issues such as parametric assumptions, leverage and asymmetric effects, and power transformations and long memory (e.g., Brooks 2007 ; Bandi and Reno 2012 ; Hou 2013 ).…”

Double-Edged Sword of Global Financial Crisis and COVID-19 Pandemic on Crude Oil Stock Returns

“…At 99% Value-at-Risk, the model passed the backtesting test using the Unconditional and conditional coverage while 95% Value-at-Risk the model also passed using the unconditional coverage and passed using the conditional coverage. On the overall the estimated ARMA(1,0)-eGARCH(2,2) model is reliable (Nieppola, 2009).…”

“…At 99% Value-at-Risk, the model passed the backtesting test using the Unconditional and conditional coverage while 95% Value-at-Risk the model failed using the unconditional coverage and passed using the conditional coverage. On the average the model is reliable (Nieppola, 2009. The table 14 above presents the selection criteria values for weekly crude oil futures based on the student t and skewed student t distributions. The ARMA model favoured the ARMA(1,0) model while the competing GARCH models (that is sGARCH, eGARCH, TGARCH and apARCH) used a maximum lag of 2.…”

Distribution-Adjusted Risk Estimates: An Application to Domestic Assets Investment Portfolios.

“…Por otra parte, Naimy y Hayek (2018) utilizan los modelos EWMA, GARCH (1,1) y EGARCH para pronosticar la volatilidad de una moneda virtual como la criptomoneda bitcoin; destacan la superioridad del modelo EGARCH que no solamente captura de mejor manera el efecto apalancamiento y los resultados MAE Y RMSE que tienden a presentar un pronóstico presumiblemente más efectivo. Emenogu et al (2020), en su estudio, investigan la volatilidad en los rendimientos diarios de activos del mercado de acciones petroleras nigeriano utilizando nueve variantes de la familia de modelos GARCH aplicando modelos VaR y backtesting, sobre el cual se concentra el estudio en éste último; de esta manera, para seleccionar al mejor modelo, ellos concluyen que los enfoques GARCH exponencial (eGARCH) y GARCH estándar (sGARCH) son los mejores al utilizar innovaciones normales, en tanto que el enfoque NGARCH es el mejor para el caso de inovaciones t-student con AIC.…”