Forecasting Volatility of Dhaka Stock Exchange: Linear Vs Non-linear models

Islam, Mohammed A.; Ali, Lasker Ershad; Afroz, Nahida

doi:10.12777/ijse.3.2.4-8

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Based on the forecasting evaluation of different forecasting error criteria between ARMA (1,1) and GARCH (2,1), Huq et al (2013) discovered that GARCH (2,1) had the best forecasting performance in the case of DSE. While (Islam, Ali, & Afroz, 2012) obtained exceptional results using the RMSE and MAPE of all linear and nonlinear models, the moving average model outperformed all other models in the case of DSE. As a result, in our study, asymmetric models with generalized error distributions (GED) had better out-ofsample forecasting performances, whereas in the majority of other similar studies, particularly those focused on two Bangladeshi markets, DSE and CSE, asymmetric models with student"s t distributions were found to be the best performers.…”

Section: Volatility Forecastingmentioning

confidence: 96%

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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Section: Volatility Forecastingmentioning

confidence: 96%

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

Rahman¹,

Haq²

2022

JBS

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“…The realistic significance of modelling and forecasting volatility in various finance applications represent that the accomplishment or failure of volatility models depend upon the features of experimental data which they attempt to capture and forecast. Volatility of share market is a crucial issue for the government's policy makers, market analysts, corporate and financial managers, since a remarkable volatility in a share market leads to an adverse impact for a country's economy (Islam et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Volatility of Stock Indices with ARCH Model

Alam¹,

Siddikee²,

Masukujjaman³

2013

International Journal of Financial Research

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The main motive of this study is to investigate the use of ARCH model for forecasting volatility of the DSE20 and DSE general indices by using the daily data. GARCH, EGARCH, PARCH, and TARCH models are used as benchmark models for the study purpose. This study covers from December 1, 2001 to August 14, 2008 and from August 18, 2008 to September 10, 2011 as in-sample and out-of-sample set sets respectively. The study finds the past volatility of both the DSE20 and DSE general indices returns series are significantly, influenced current volatility. Based on in-sample statistical performance, both the ARCH and PARCH models are considered as the best performing model jointly for DSE20 index returns, whereas for DSE general index returns series, ARCH model outperforms other models. According to the out – of- sample statistical performance, all models except GARCH and TARCH models are regarded as the best model jointly for DSE20 index returns series, while for DSE general index returns series, no model is nominated as the best model individually. Based on the in-sample trading performance, all models except GARCH are considered as the best model jointly for DSE20 index returns series, while ARCH model is selected as the best model for DSE general index returns series. A per outputs of out-of-sample trading performance, the EGARCH model is the best performing model for DSE20 index returns series, whereas the GARCH and ARCH models are considered as the best performing model jointly for DSE general index returns series

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“…GARCH and TARCH models are regarded as the best model jointly for DSE20 index returns series, while for DSE general index returns series, no model is nominated as the best model individually. Islam et al[10] examined the relative ability of various linear and nonlinear models to forecast…”

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confidence: 99%

Which Model Performs Better While Forecasting Stock Market Volatility? Answer for Dhaka Stock Exchange (DSE)

Abdullah¹,

Kabir²,

Jahan³

et al. 2018

TEL

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An efficient and well behaved capital market can be regarded as a prerequisite for the sustainable financial development for an economy. For making the stock market efficient and reducing uncertainty, volatility measure is necessary for the policy makers. The main objective of this paper is to examine relative ability of various models to forecast future volatility and to devise appropriate volatility model for capturing variability in stock returns of Dhaka Stock Exchange (DSE). By exploiting daily data spanning from 27 th November, 2001 to 31 st July, 2013, it was found that, from volatility persistency perspective MA(2) − GARCH(2, 1) is better due to both in sample and out of sample accuracy. In contrast, from capturing asymmetric effect perspective MA(2) − EGARCH(1, 3) is better. Thus, there was no clear winner and hence the decision should depend on the purpose of the concerned people.

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Forecasting Volatility of Dhaka Stock Exchange: Linear Vs Non-linear models

Cited by 4 publications

References 12 publications

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

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

Forecasting Volatility of Stock Indices with ARCH Model

Which Model Performs Better While Forecasting Stock Market Volatility? Answer for Dhaka Stock Exchange (DSE)

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