Modelling and Estimation of Volatility Using ARCH/GARCH Models in Jordan’s Stock Market

Al-Najjar, Dana

doi:10.5296/ajfa.v8i1.9129

Cited by 32 publications

(13 citation statements)

References 36 publications

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“…Consistent with many previous studies (see for example, Franses & Van Dijk [43], Gokcan [44] and AL-Najjar [45], the study applies the GARCH (1,1). This …”

Section: The Garch (11) Estimation Resultssupporting

confidence: 65%

Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models

Epaphra¹

2017

JMF

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Policy makers need accurate forecasts about future values of exchange rates. This is due to the fact that exchange rate volatility is a useful measure of uncertainty about the economic environment of a country. This paper applies univariate nonlinear time series analysis to the daily (TZS/USD) exchange rate data spanning from January 4, 2009 to July 27, 2015 to examine the behavior of exchange rate in Tanzania. To capture the symmetry effect in exchange rate data, the paper applies both ARCH and GARCH models. Also, the paper employs exponential GARCH (EGARCH) model to capture the asymmetry in volatility clustering and the leverage effect in exchange rate. The paper reveals that exchange rate series exhibits the empirical regularities such as clustering volatility, nonstationarity, non-normality and serial correlation that justify the application of the ARCH methodology. The results also suggest that exchange rate behavior is generally influenced by previous information about exchange rate. This also implies that previous day's volatility in exchange rate can affect current volatility of exchange rate. In addition, the estimate for asymmetric volatility suggests that positive shocks imply a higher next period conditional variance than negative shocks of the same sign. The main policy implication of these results is that since exchange rate volatility (exchange-rate risk) may increase transaction costs and reduce the gains to international trade, knowledge of exchange rate volatility estimation and forecasting is important for asset pricing and risk management.

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“…Consistent with many previous studies (see for example, Franses & Van Dijk [43], Gokcan [44] and AL-Najjar [45], the study applies the GARCH (1,1). This …”

Section: The Garch (11) Estimation Resultssupporting

confidence: 65%

Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models

Epaphra¹

2017

JMF

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“…Dana Alnajjar (2016) attempted to forecast the volatility of Jordan's capital market using GARCH family models. The study comprises of three models i.e ARCH, GARCH and EGARCH to find out volatility clustering, leptokurtosis and leverage effect on Amman stock exchange which showed that ARCH GARCH gives more evidence for both volatility clustering and leptokurtic.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Garch Models for Volatility Modelling of Stock Market Returns: Evidences From Bse India

Mathur¹,

Mathur²

2020

Proceedings of the 11th Business &Amp; Management Conference, Dubai

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The National Stock Exchange and Bombay Stock Exchange are the two major stock exchanges in India. The Bombay Stock Exchange is the first stock exchange of Asia and 10th largest stock exchange in the world in the terms of market capitalisation. Stock markets significantly contributes in the economic development of India. The stock markets have volatile character which results into the uncertainty of the returns, volatility is caused by the variability in speculative market prices and the instability of business performance. Volatility plays a significant role in financial decisions of the investors, managers, policy makers and the researchers as it can assess the risk exposures in their investments and the uncertainty in stocks returns. The risk averse investor avoid investment in highly volatile market. The stock return forecasting leads to volatility forecasting. This paper has made an attempt to analyse the volatility with reference to Bombay Stock Exchange. The daily data of S&P Sensex 30 has been collected and used to calculate the volatility of stock market in India for last 3 years (April 2016 to March 2019). The preliminary analysis is done on the basis of descriptive statistics Stationery test, Normality test and serial correlation test. Volatility modelling is done by the ARCH and GARCH family models. The findings of the study will help investors in taking good investment decisions in Indian stock market in the presence of its volatile character.

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“…The choice of lower GARCH models stems from the fact that GARCH (1,1) model is sufficient for capturing all volatilities present in any financial data and also producing the desired relationship between risk and expected returns. For evidence see the works by many researchers [31,32,33,34,35,36,37,38,39] among others.…”

Section: Model Specificationmentioning

confidence: 99%

Estimating Stock Returns Volatility and the Risk-Return Nexus in the Nigerian Stock Market in the Presence of Shift Dummies

Kuhe

Chiawa

Nwaosu

et al. 2019

ARJOM

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Volatility and the trade-off between risk and return in stock markets is an important subject in financial theory which play significant role in investment decision making, portfolio selection, options pricing, financial stability, hedging and pair trading strategy among others. This study estimates stock return volatility and analyses the risk-return trade-off in the Nigerian stock market using symmetric GARCH (1,1)-in-mean, asymmetric CGARCH (1,1)-in-mean and EGARCH (1,1)-in-mean models with Generalized Error Distribution and Student-t innovation. Data on daily closing all share prices of the Nigerian stock exchange for the period 2nd January, 1998 to 9th January, 2018 are utilised. The data is further divided into three sub-periods of pre-crisis, global financial crisis and post crisis periods to allow volatility behaviour and the risk-return trade-off to be investigated across the sub-periods. Results showed evidence of volatility clustering, leptokurtosis, high persistence of shocks to volatility and asymmetry without leverage effects across the study periods. The persistence of shocks to volatility increased during the global financial crisis period with delayed reactions of volatility to market changes. However, by incorporating the exogenous breaks into the volatility models for the full study period, the shock persistence drastically reduced with faster reactions of volatility to market changes. The results of this study also found supportive evidence for a significant positive risk-return relationship in Nigerian stock market across various study sub-periods and model specifications meaning that investors in Nigerian stock market should be compensated for holding risky assets. The empirical findings of this study further suggest that the recent global financial crisis have not altered the market dynamics to distort the risk-return trade-off in Nigerian stock market indicating that expected returns are not driven by changes in the stock market volatility. The study provides some policy recommendations for investors and policy makers in the Nigerian stock market.

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Modelling and Estimation of Volatility Using ARCH/GARCH Models in Jordan’s Stock Market

Cited by 32 publications

References 36 publications

Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models

Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models

Application of Garch Models for Volatility Modelling of Stock Market Returns: Evidences From Bse India

Estimating Stock Returns Volatility and the Risk-Return Nexus in the Nigerian Stock Market in the Presence of Shift Dummies

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