Modelling &amp; Forecasting Volatility of Daily Stock Returns Using GARCH Models: Evidence from Dhaka Stock Exchange

Ahmed, Md. Tuhin; Naher, Nurun

doi:10.31014/aior.1992.04.03.371

Cited by 3 publications

(3 citation statements)

References 8 publications

(21 reference statements)

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“…Sudah banyak penelitian volatilitas saham yang menggunakan GARCH seperti perbandingan kinerja model GARCH untuk menangkap volatilitas pasar saham di Malaysia (Lim & Sek, 2013); pemodelan volatilitas pasar saham Bulgaria, Chechnya, Polandia, Hungaria dan Turkey (Ugurlu et al, 2014); Sudan dan Mesir (Abdala et al, 2014); Uganda (Namugaya et al, 2014); Kenya (Koima et al, 2015); Bangladesh (Miah & Rahman, 2016); Afrika Selatan dan Tiongkok (Cheteni, 2016); Tanzania (Kazungu & Mboya, 2021). Untuk prediksi volatilitas harga saham menggunakan GARCH sudah ada beberapa penelitian yang dilakukan seperti penelitian terhadap indeks saham S&P-500 (Awartani & Corradi, 2005); akurasi peramalan volatilitas di pasar saham Swedia (Grek, 2014); pemodelan dan peramalan volatilitas di Dhaka Stock Exchange (Ahmed & Naher, 2021); peramalan 21 indeks saham dunia (Sharma, 2015). Penelitian ini bertujuan untuk memprediksi volatilitas IHSG menggunakan GARCH dan berusaha menjelaskan peristiwa yang menyebabkan volatilitas IHSG sepanjang tahun 2022.…”

Section: Pendahuluanunclassified

Prediksi Volatilitas Indeks Harga Saham Gabungan Menggunakan GARCH

Jange

2023

arbitrase

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This study aims to determine the Volatility Prediction of the Indonesia Composite Index using GARCH. GARCH is a model for generating forecasts based on historical data using a function of its own past lag plus past innovations. This study uses quantitative methods. The data in this study are JKSE data for 11 (eleven) years, from 01-01-2012 to 31-12-2022. The volatility prediction is carried out for a year, namely in 2022. The results of this study indicate that the prediction accuracy is quite good with a MAPE of 17.26 percent. It was also found that volatility was quite pronounced, which rose significantly in May 2022 due to the effect of the absence of restrictions on the Eid holiday and decreased significantly in July 2022 due to the government extending the implementation of restrictions on community activities (PPKM) throughout Indonesia and dropping even more sharply due to the government increasing prices of Pertalite in September 2022.

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Section: Pendahuluanunclassified

Prediksi Volatilitas Indeks Harga Saham Gabungan Menggunakan GARCH

Jange

2023

arbitrase

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“…However, a study on DSE by Abdullah et al (2018) revealed that MA(2)-EGARCH (1,3) offers the most accurate results outside of the sample among its counterparts. According to the results of the student's t distribution and the normal distribution, ARMA (1,1)-IGARCH (1,1) has the best out-of-sample forecasting performance for DSE (Ahmed, Naher, & Ahmed, 2021). Huq and Ali (2018) investigated the fact that the GJR-GARCH (1, 1) under the Gaussian error distribution model outperformed other DSE models.…”

Section: Volatility Forecastingmentioning

confidence: 99%

Forecasting Index Return Volatility of The Chittagong Stock Exchange of Bangladesh using GARCH Models

Rahman¹,

Haq²

2022

JBS

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Purpose: The aim of this research is to identify the best-fitted model(s) for estimating and forecasting the return volatility of the Chittagong Stock Exchange (CSE) in Bangladesh. Methodology: The study analyzes the returns of the Chittagong Stock Exchange's (CSE) daily Selective Categories Index (CSCX) from February 4, 2013 to December 31, 2021 (as a full sample) and from July 1, 2021 to December 30, 2021 (for forecasting). The researcher used GARCH family approaches considering different error distributions, to find the well-suited model(s) for the CSCX index. The researchers used ARMA to develop the mean equation based on two popular model selection criteria: Schwarz's (1978) Bayesian information criterion (SBIC) and Akaike's (1974) information criterion (AIC). The data has been analyzed using the application software E-Views 10. Findings: The ARMA (0,1) has been adopted as the mean equation for GARCH specifications. Under all three types of error distributions, the ARCH and GARCH terms, along with the leverage terms of asymmetric models, were found to be statistically significant in all the accepted combinations of the model. The models GARCH (1,2), TGARCH (1,2), and PARCH (1,2) under generalized error distributions and EGARCH (2,1) under Student’s t error distributions have been selected as the best-fitted models for estimation. Whereas, based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), and theil inequality (TI), EGARCH (1, 2), TGARCH (1, 2), and PARCH (1, 2) under generalized error distributions, and GARCH (1, 2) under student’s t error distributions and normal error distributions are found to have superior out-of-sample forecasting abilities. Practical implications and originality: This is an original research work that will help the investors and other stakeholders of the Bangladeshi stock market to estimate and forecast market volatility more efficiently. Limitations: Due to its extensive features, this study was unable to incorporate a few additional ARCH and GARCH models.

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“…Ahmed & Suliman, 2011), examined modelling stock market volatility using GARCH models evidence from Sudan. The symmetric and asymmetric behavior of the stock was analyzed and the result revealed that the conditional variance process was highly persistent and as such provided evidence of risk premium for the KSE index stock series which showed that the asymmetric model provided a better fit than the symmetric model, which confirmed the presence of leverage effect (Maqsood et al, 2017).…”

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confidence: 99%

Forecasting Interest Rate Volatility In Nigeria In The Arch-Garch Family Models

Ebenezer Chukwuma

2022

jrssem

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Modeling the volatility of interest rates is essential for many areas in finance. However, it is well known that interest rate series exhibit non-normal characteristics that may not be captured with the standard GARCH model with a normal error distribution. But which GARCH model and error distribution to use is still open to question, especially where the model that best fits the in-sample data may not give the most effective out-of-sample volatility forecasting ability, which we use as the criterion for the selection of the most effective model from among the alternatives. In this work, the GARCH family models were employed in modeling interest rate volatility in Nigeria. A time series of data spanning January 2000 to December 2018 (in-sample data) was used to fit the models and out-of-sample data running from January to December 2019 to determine the best conditional volatility forecast. Twenty-four symmetric and eighteen asymmetric models were estimated and compared using three distribution errors; the normal, student's t, and the generalized error distributions; while four error loss functions, namely, RMSE, MAE, MAPE, and TIC, were adopted to determine the best fit and conditional volatility forecast. The result shows that the symmetric GED-GARCH (1,1) model was considered the overall best fit in both the symmetric and asymmetric models. The best-fitted GED-GARCH (1,1) model exhibited volatility persistence. The in-sample and out-of-sample volatility forecast of the GED-GARCH (1,1) model reveals that unconditional mean and variance will be achieved in the third month of 2019. Some transmission spillover effects running from the exchange and inflation rates to interest rates were also detected.

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Modelling & Forecasting Volatility of Daily Stock Returns Using GARCH Models: Evidence from Dhaka Stock Exchange

Cited by 3 publications

References 8 publications

Prediksi Volatilitas Indeks Harga Saham Gabungan Menggunakan GARCH

Prediksi Volatilitas Indeks Harga Saham Gabungan Menggunakan GARCH

Forecasting Index Return Volatility of The Chittagong Stock Exchange of Bangladesh using GARCH Models

Forecasting Interest Rate Volatility In Nigeria In The Arch-Garch Family Models

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