Forecasting S&amp;P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models

Liu, Hung‐Chun; Hung, Jui‐Cheng

doi:10.1016/j.eswa.2009.12.022

Cited by 88 publications

(44 citation statements)

References 51 publications

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“…Empirical results vary on which of these models provides the best volatility forecasts. According to the research done by [25] GJR-GARCH achieves the most accurate volatility forecasts with EGARCH just slightly behind. Asymmetric GARCH model attracted extensive research on the volatility transmission in the context of the Asian financial crisis and the 2007-9 subprime mortgage crisis (see [17], [6], and [28] among others).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Asymmetric GARCH model attracted extensive research on the volatility transmission in the context of the Asian financial crisis and the 2007-9 subprime mortgage crisis (see [17], [6], and [28] among others). Reference [25] mentioned that when asymmetries are ignored GARCH model with normality assumption is preferable to the usual error distribution models. Modelling asymmetric components is vital than specifying error distribution to improve volatility forecasts of financial returns with heavy tails, lepto-kurtic and skewed leverage effects.…”

Section: Literature Reviewmentioning

confidence: 99%

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Modelling Co-movement of Different Sectors in Dhaka Stock Exchange (DSE) Using Asymmetric BVAR-GARCH Models

Chowdhury¹,

Arefin²

2017

JFE

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This paper tries to investigate the financial shock transmission dynamics using daily return data under different sectors traded in Dhaka Stock Exchange (DSE). Bayesian VAR model was used as conditional mean in GJR-GARCH, scalar-diagonal VECH and BEKK GARCH models to test return and volatility spillover effects. Lagged squared residuals and lagged conditional variances were used as variance regressors in conditional variance of GJR-GARCH to test the spillover effects. Finding reveals a highly significant memory effect on all sector returns except one. Asymmetric versions of GARCH, VECH and BEKK identified significant effect of news about volatility, past memory and differential effect of bad news on conditional volatility of almost all series. GJR-GARCH results identified pharmaceutical sector spillover free yet all other pairs were found to be unidirectional or in some cases bidirectional. Co-volatility among almost all sectors is also observed from VECH and BEKK output.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Modelling Co-movement of Different Sectors in Dhaka Stock Exchange (DSE) Using Asymmetric BVAR-GARCH Models

Chowdhury¹,

Arefin²

2017

JFE

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“…For emerging stock market data, Ng & McAleer (2004) suggested that GARCH and GJR-GARCH models are superior to the RiskMetrics (Morgan, 1996) model in forecasting stock market volatility; however, neither GARCH nor GJR-GARCH dominates the other. Despite extensive literature on volatility model evaluation, no consensus exists suggesting the most appropriate model for providing which model has the optimal performance in forecasting volatility (Liu & Hung, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…The GARCH model estimates jointly a conditional mean and conditional variance equation, and it is characterized by a fat tail and excess of kurtosis, regularly used in studying the daily returns of stock market data (Han & Park, 2008). 1 Despite the success of GARCH model, it has been criticized for failing to capture the asymmetric volatility (Liu & Hung, 2010), since for stock prices, negative shocks to returns generally have large impacts on their volatility than positive shocks. To overcome these limitations, extensions of the GARCH model have been proposed, comprising a class of asymmetric GARCH models.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

Maciel¹

2013

Advances in Financial Risk Management

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Forecasting stock market returns volatility is a challenging task that has attracted the attention of market practitioners, regulators and academics in recent years. This paper proposes a Fuzzy GJR-GARCH model to forecast the volatility of S&P 500 and Ibovespa indexes. The model comprises both the concept of fuzzy inference systems and GJR-GARCH modeling approach in order to consider the principles of time-varying volatility, leverage effects and volatility clustering, in which changes are cataloged by similarity. Moreover, a differential evolution (DE) algorithm is suggested to solve the problem of Fuzzy GJR-GARCH parameters estimation. The results indicate that the proposed method offers significant improvements in volatility forecasting performance in comparison with GARCH-type models and with a current Fuzzy-GARCH model reported in the literature. Furthermore, the DEbased algorithm aims to achieve an optimal solution with a rapid convergence rate.Keywords: volatility; GARCH models; fuzzy systems; differential evolution. JEL codes: C53; C61; G17. ResumoA previsão da volatilidade dos retornos de ativos financeirosé uma abordagem desafiadora e tem atraído a atenção de participantes do mercado, reguladores e acadêmicos nos anos recentes. Este artigo propõe um modelo GJR-GARCH nebuloso para a previsão da volatilidade dosíndices S&P 500 e Ibovespa. O modelo combina os conceitos de sistemas de inferência nebulosos e a abordagem GJR-GARCH para considerar os príncipios de variância condicional, efeitos de alavancagem e agrupamentos de volatilidade, em que as flutuações são associadas por similaridade. Além disso, um algoritmo de evolução diferencial (ED)é sugerido para solucionar o problema de estimação dos parâmetros do modelo GJR-GARCH nebuloso. Os resultados indicaram que o método proposto permitiu uma melhor capacidade de previsão da volatilidade em comparação com modelos da família GARCH e com um modelo GARCH nebuloso recentemente proposto na literatura. Além disso, o algoritmo de ED considerado obteve soluçõesótimas com uma rápida taxa de convergência.

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“…To the best of our knowledge, a large volume of recent studies have been investigated and written about the value-at-risk (VaR) issue for various financial markets by using GARCH techniques, such as Angelidis et al (2004), Huang and Lin (2004), Liu and Hung (2010), Orhan and Köksal (2012) and So and Yu (2006) for stock markets, Al Janabi (2006), Bams et al (2005) and So and Yu (2006) for foreign exchange rate markets, Chan and Gray (2006), Sadeghi and Shavvalpour (2006) and Sadorsky (2006) for energy markets. However, despite the importance of VaR on financial risk management and the popularity of ETFs for common investors, there seems to have been relatively little work endeavored on ETFs.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Downside Risk of Exchange-Traded Funds: Do the Volatility Estimators Matter?

Wang¹,

Chen²,

Liu³

et al. 2015

IJEF

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This paper aims to propose the augmented GJR-GARCH (GJR-GARCH M ) model that extends the GJR-GARCH model by comprising overnight returns volatility (ONV), daily high-low prices range (PK), and fear index (VIX) as explanatory variables for the GJR's variance equation, respectively. The proposed models are used to estimate the daily value-at-risk values and evaluate their downside risk management performance for the SPDRs covering the period from 2009 to 2014. Empirical results show that the GJR-GARCH M model outperforms the GJR-GARCH model for most cases, suggesting that the GJR-GARCH-based VaR forecasts can be moderately improved with the additional information embodied in the ONV, PK and VIX volatility estimators. In addition, daily high-low prices range and VIX are far more informative than the overnight volatility estimator for improving the GJR-GARCH-based VaR forecasts. Risk managers can employ the proposed models for estimating and controling the potential loss of ETFs in the face of financial catastrophes.

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Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models

Cited by 88 publications

References 51 publications

Modelling Co-movement of Different Sectors in Dhaka Stock Exchange (DSE) Using Asymmetric BVAR-GARCH Models

Modelling Co-movement of Different Sectors in Dhaka Stock Exchange (DSE) Using Asymmetric BVAR-GARCH Models

A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

Analyzing the Downside Risk of Exchange-Traded Funds: Do the Volatility Estimators Matter?

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