Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models

Lama, Achal; Jha, Girish Kumar; Paul, Ranjit Kumar; Gurung, Bishal

doi:10.5958/0974-0279.2015.00005.1

Cited by 41 publications

(32 citation statements)

References 10 publications

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“…Accordingly, using a model which can help to measure the volatility of price indexes (Engle 1982, Zakoian 1994, Bollerslev 1986, Zakoian 1994 serves as an insight to predict volatility. In line with this, Lama et al (2015), Le Roux (2018), and Adugh ( 2019) are pioneers who model volatility specifically on agricultural commodity prices.…”

Section: Statement Of the Problemmentioning

confidence: 95%

“…According to Lama et al (2015), most of the agricultural price series can be modeled as time series data where the information is collected over time at equal time-epochs. In this framework, Lama et al (2015) studied the autoregressive integrated moving average (ARIMA) model, generalized autoregressive conditional Heteroskedastic (GARCH) model and exponential GARCH (EGARCH) model along with their estimation procedures for modeling and forecasting of three price series, specifically domestic and international edible oils price indices and the international cotton price 'Cotlook A' index. Their study revealed that the EGARCH model outperformed the ARIMA and the GARCH models in forecasting the international cotton price series primarily due to its ability to capture asymmetric volatility patterns.…”

Section: Statement Of the Problemmentioning

confidence: 99%

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Modeling Price Volatility for Selected Agricultural Commodities in Ethiopia: The Application of GARCH Models

Tirngo

2021

Preprint

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The purpose of this study was to model and forecast volatility of returns for selected agricultural commodities prices using generalized autoregressive conditional heteroskedasticity (GARCH) models in Ethiopia. GARCH family models, specifically GARCH, threshold generalized autoregressive conditional heteroskedasticity (TGARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) were employed to analyze the time varying volatility of selected agricultural commodities prices from 2011to 2021. The data analysis results revealed that, out of the GARCH specifications, TGARCH model with Normal distributional assumption of residuals was a better fit model for the price volatility of Teff and Red Pepper in which their return series reacted differently to the good and the bad news. The study indicated the presence of leverage effect which implied that the bad news could have a larger effect on volatility than the good news of the same magnitude, and the asymmetric term was found to be significant. Also, TGARCH model was found to be the accurate model for forecasting price return volatility of the same commodities, namely Teff and Red Pepper. In short, the study concludes that TGARCH was to be the best fit to model and forecast price return volatility of Teff and Red Pepper in the Ethiopian context.

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Section: Statement Of the Problemmentioning

confidence: 95%

Section: Statement Of the Problemmentioning

confidence: 99%

Modeling Price Volatility for Selected Agricultural Commodities in Ethiopia: The Application of GARCH Models

Tirngo

2021

Preprint

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“…First, unlike the relatively few previous studies done in Tanzania (see for example, Mutaju and Dickson (2019)), we apply both GARCH and the EGARCH models to capture both symmetry and asymmetry effects, and determine key characteristics of DSE stock returns. Secondly, unlike Mutaju and Dickson (2019) we divide our data set into three periods, namely; the period between 2014 and 2019, the period before "General Election" (2014-2015) and after "General Election" of 2015-2019. We believe this categorization of period is important because change of power by those in government may influence investor's participation in the stock market through the adoption of "wait and see" attitude (Nancy, 2016) and this might have remarkable consequences on the behavior of stock prices.…”

Section: Asian Journal Of Economic Modellingmentioning

confidence: 99%

“…The motivation for undertaking this exercise is two folds. First, although much has been documented on the volatility of stock prices elsewhere in the world, relatively little is known in the context of Tanzania (see for example, (Achal, Girish, Ranjit, & Bishal, 2015;Ajaya & Swagatika, 2018;Akhtar & Khan, 2016;Mathur, Chotia, & Rao, 2016)). Existing studies that have attempted to examine volatility within the context of GARCH models in Tanzania have mainly focused on other macroeconomic variables such as inflation (Edward, Eliab, & Estomih, 2004) exchange rate (Carolyn, Betuel, & Pitos, 2018;Epaphra, 2016) tax revenues (Chimilila, 2017).…”

Section: Introductionmentioning

confidence: 99%

Volatility of Stock Prices in Tanzania: Application of Garch Models to Dar Es Salaam Stock Exchange

Kazungu

Mboya²

2021

Asian Journal of Economic Modelling

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We use Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to examine volatility of stock prices for firms listed in the Dar es Salaam Stock Exchange (DSE). In doing so, both symmetric and asymmetric GARCH models are used in this study. The descriptive analysis of the data shows that standard deviation of the series returns is high, indicating a high level of daily fluctuations, and the log value of the mean is close to zero. Our empirical results clearly exhibit evidence of volatility and volatility clustering, a typical feature of financial time series. Moreover, our results indicate that the series are highly leptokurtic, flat tailed and asymmetric consistent with characteristics of financial time series data. Out of all models examined, EGARCH (1,1) and GARCH (1,1) seem to perform plausibly better than others.Contribution/ Originality: This study contributes to the existing literature through the application of both GARCH and the EGARCH models in order to capture both symmetry and asymmetry effects, and determines key characteristics of stock returns at Dar es salaam Stock Exchange.

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“…An EGARCH model was chosen in view of certain advantages it has. In addition to the GARCH model of Bollerslev (1986) to estimate conditional volatility, the EGARCH model allows for the asymmetric effects between positive and negative shocks on the conditional variance (Lama, Jha, Paul, & Gurung, 2015). Nelson and Cao (1992) suggest that whereas there are non-negativity constraints on the parameters of the process in the GARCH model of Bollerslev (1986), there are no restrictions on the parameters in the EGARCH model.…”

Section: Asian Economic and Financial Reviewmentioning

confidence: 99%

Risk Threshold for Sustainable Current Account Balance of Payments: An Indonesian Case

Hakim

Sriyana

2020

Asian Economic and Financial Review

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The objective of this study was to model the behavior of the Current Account Balance of Payments (CAB) for Indonesia. It also calculated the conditional Value at Risk (VaR) as a measure of the risk level of the CAB. An ARDL (Autoregressive Distributed Lag) model and an EGARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity) model were used to estimate the CAB behavior for the annual data 1985-2018. The research found that exchange rates, growth of gross domestic product, inflation, total reserves, and unemployment are essential in determining the behavior and volatility of the CAB. The VaR calculated based on the conditional standard deviation that resulted from the EGARCH estimation shows that most of the time the Indonesian CAB is in safe conditions. However, the VaR has been violated by the actual CAB several times, and the violations coincide with various macroeconomic shocks. The Central Bank of Indonesia could calculate the VaR threshold using this method to evaluate the risky nature of the current account deficit. This study provides an alternative procedure to analyze and assess the current account balance risk to mitigate the impact of macroeconomic shocks.Contribution/ Originality: This study contributes to the existing literature in the area of international economics, specifically in the issue of the current account balance of payments. The study is one of very few to have investigated the estimation of the current account risk to avoid macroeconomic instability.

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Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models

Cited by 41 publications

References 10 publications

Modeling Price Volatility for Selected Agricultural Commodities in Ethiopia: The Application of GARCH Models

Modeling Price Volatility for Selected Agricultural Commodities in Ethiopia: The Application of GARCH Models

Volatility of Stock Prices in Tanzania: Application of Garch Models to Dar Es Salaam Stock Exchange

Risk Threshold for Sustainable Current Account Balance of Payments: An Indonesian Case

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