Modeling the stock price returns volatility using GARCH(1,1) in some Indonesia stock prices

Awalludin, Subhan Ajiz; Ulfah, Syafika; Soro, Slamet

doi:10.1088/1742-6596/948/1/012068

Cited by 5 publications

(9 citation statements)

References 2 publications

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“…With the lower persistence in volatility, the ESTR-GARCHn(1,1) model The results of the unconditional volatility and halflife of volatility are presented in Table 6. Next, this study focuses on the use of the Student-t distribution for the GARCH (1,1), ESTR-GARCH (1,1), and ST(1)-GARCH (1,1) models, respectively then called GARCHt(1,1), ESTR-GARCHt(1,1), and ST(1)-GARCHt(1,1) models, which are labeled as Model (4), (5), and (6) in Table 5. Overall, the empirical results showed that Excel Solver and Matlab provide similar estimation values, except the estimate of degrees of freedom in the ESTR-GARCH (1,1) model adopting the FTSE100 and TOPIX data.…”

Section: Simulation Studymentioning

confidence: 99%

“…The GARCH (1,1)-type models was applied by [20] to Indonesian commodity market, [28] to Indonesian foreign exchange market, [5] to Indonesian stock market, and [15] to Indonesian capital market. [20] examined the predictability of five GARCH-type models, namely ARCH, GARCH, GARCH-M, EGARCH, and TGARCH, for seven primary agricultural commodities in Indonesian export and found that the predictability of the considered models is different for each commodity.…”

Section: Introductionmentioning

confidence: 99%

“…In that case, they used the Adaptive Random Walk Metropolis (ARWM) method in the MCMC algorithm to estimate the models. Meanwhile, [5] applied the GARCH (1,1) model to the daily data prices of seven Indonesian stocks for the period from July 2007 to September 2015. They estimate the model using the maximum likelihood estimation method.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

Nugroho

Susanto

Prasetia

et al. 2019

jak

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This study proposed two new classes of GARCH(1,1) model by applying the Tukeytransformations to the returns and to the lagged variance. The behavior of return volatility was investigated on the basis of models with normal and Student-t distributions for return error. The competing models were estimated by using the Excel Solver and Matlab tools. The empirical analysis is based on simulated data, daily exchange rates of the IDR/USD, and daily stock indices of FTSE100 and TOPIX. This study recommends the use of Excel Solver for finance academics and practitioners working on volatility using GARCH(1,1) models. Our empirical findings conclude that GARCH(1,1) models under Tukey transformations should be considered in risk management decisions since the models are more appropriate than standard for describing returns and volatility of financial time series and its stylized facts including fat tails and mean reverting. The Tukey transformed returns imply a shorter volatility half-life, and thus this study suggests that investors should invest the observed assets in a shorter time period to obtain higher returns.

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Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

Nugroho

Susanto

Prasetia

et al. 2019

jak

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“…The first GARCH that we estimated comes from Bollerslev (1986). We specified a GARCH(1,1) process because researchers found the GARCH(1,1) performed well during the 2008 global financial crisis in Malaysia (see, e.g., Angabini & Wasiuzzaman, 2011;Awalludin et al, 2018;Kingsley & Peter, 2019;Namugaya, Weke, & Charles, 2014;Tripathy & Rahman, 2013). Equation 3 is the mean equation with a stock market return (rt), intercept (c), and residual (ut), while Equation 4 represents the conditional variance equation with a variance ( ).…”

Section: Methodsmentioning

confidence: 99%

“…Accordingly, the volatility plays an important role in portfolio selection, derivative pricing and risk management. Although volatility plays an informative and vital role in financial markets, it is not directly observable, varies over time and is highly sensitive to financial market changes (Awalludin, Ulfah, & Soro, 2018). Therefore, econometricians have Asian Economic and Financial Review developed various volatility models, since volatility estimation with the best precision is crucial in determining what is happening in the stock markets and the economy.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

Asian Economic and Financial Review

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In this study, the volatility of two Asian stock markets, Bursa Malaysia and Singapore Exchange, is estimated. The analysis used data on daily closing prices of the indices of the respective stock markets between July 1, 2019 and August 31, 2020. The sample is split into two subsample periods: Pre-COVID-19 pandemic and during the COVID-19 pandemic. We estimated a standard GARCH, GARCH-M, TGARCH, EGARCH and PGARCH model for each subsample. We chose the best GARCH that yielded the lowest Schwarz information criterion for the normal, skewed normal, Student's tdistribution, skewed Student's t-distribution, generalized error distribution (GED) and skewed GED. The results show that both stock market returns are quite persistent, and the persistence decreases for both stock market returns during the pandemic. Furthermore, the normal distribution performed well for Malaysian and Singaporean stock markets before the pandemic and switched to a Student's t (skewed normal) during the pandemic. The standard GARCH(1,1), GARCH-M(1,1), and EGARCH(1,1) performed well for both stock market returns, and the EGARCH indicates the presence of the leverage effect when stock market returns are negatively correlated to its volatility. Contribution/Originality: In this study, the volatility of the Malaysian and Singaporean stock market returns is examined and estimated with various GARCH models with different probability distributions in the loglikelihood function. The COVID-19 pandemic has altered the distributional properties of the GARCH models.

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Stock Market Volatility in Zimbabwe Stock Exchange During Pandemic Period

Bonga¹

2022

EJEF

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Volatility is essential to consider uncertainty surrounding investments in financial assets. For this reason, financial industry regulators, mutual fund managers, individual and institutional investors, and policymakers are concerned about volatility. Against this background, this paper investigates the volatility of returns on the Zimbabwean stock market between January 2020 and January 2022. We use the All Shares Index for Zimbabwe Stock Exchange (ZSE) for volatility analysis and perform the quantitative investigation using GARCH family models. According to the AIC and SIC criterion, we use the GARCH (1,1) model and perform a complete analysis considering the results obtained from EGARCH (1,1) and IGARCH (1,1) regressions. Results report persistence in volatility, showing that it takes time for the market to digest information into the prices fully, and the shocks to conditional variance take longer to die out. Also, an asymmetry exists, implying that bad news and good news impact differently on the stock market, and the magnitude of volatility due to the good news is higher than bad news. Therefore, we conclude that positive news of the same magnitude impacts more than bad news. Investors rely more on the good news for effective decisions during the pandemic to earn more. Considering the results, any policy aimed at reducing the impact of the pandemic is favorable for investment.

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Modeling the stock price returns volatility using GARCH(1,1) in some Indonesia stock prices

Cited by 5 publications

References 2 publications

Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

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Stock Market Volatility in Zimbabwe Stock Exchange During Pandemic Period

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