GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns

Guo, Zi-Yi

doi:10.5539/ijbm.v12n9p28

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…The estimated risk premium coefficients ( ) in the GARCH (2,1)-M models are also positive for both asset and volume of trade returns indicating that the conditional variances used as proxies for risk of returns are positively related to the levels of returns. This result corroborates the empirical findings of several authors [42,43,25,44,45] but contrary to the findings of several authors [46,47,48,49]. Tables 8 and 9, we observe also that by incorporating the structural break points in the volatility models, there are significant decreases in the values of shock persistence parameters ( ) in all the estimated asymmetric GARCH-type models.…”

Section: Parameter Estimates Of Symmetric and Asymmetric Volatility Msupporting

confidence: 88%

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

Kuhe

Chiawa

Nwaosu

et al. 2019

AJPAS

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This study investigated the impact of volatility shock persistence on the conditional variance in the Nigerian stock returns using symmetric and asymmetric higher order GARCH family models in the presence of random level shifts and non-Gaussian errors. The study utilised Bai and Perron methodology to detect structural breakpoints in the conditional variance of daily stock and volume of trade returns in the Nigerian stock market from 2nd January, 1998 to 22nd March, 2017. The study employed symmetric GARCH (3,2) and GARCH (2,1)-M models to estimate volatility of asset returns, symmetric GARCH (2,2) and GARCH (2,1)-M to model volatility of volume of trade returns and asymmetric EGARCH (2,2), TGARCH (3,2) and PGARCH (2,3) models to measure the volatility of asset returns as well as asymmetric EGARCH (2,1), TGARCH (1,3) and PGARCH (3,2) models to estimate volatility of volume of trade returns. These models were optimally selected using information criteria and log likelihood as the best fitting symmetric and asymmetric GARCH models to estimate the conditional volatility of asset and volume of trade returns in the Nigerian stock market with and without structural breaks. Results revealed that when random level shifts were ignored in volatility models, the shocks persistence were very high with long memory and variance explosion. But when the random level shifts were incorporated into the GARCH models, there was a significant reduction in the volatility shocks persistence and long memory. Moreover, volatility half-lives also declined drastically while accounting for these sudden level shifts in variance. The study found asymmetry without leverage effects as well as a positive risk-return tradeoff for both asset and volume of trade returns in the Nigerian stock market. The Nigeria banking reform of 2004, the Global Financial and Economic Crises, as well as other local events in Nigeria, were found to have negative and significant impacts on the Nigerian stock market. The study provided some policy recommendations.

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Section: Parameter Estimates Of Symmetric and Asymmetric Volatility Msupporting

confidence: 88%

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

Kuhe

Chiawa

Nwaosu

et al. 2019

AJPAS

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“…Our results indicate the NRIG has better performance in capture the DAX returns dynamics. Guo (2017aGuo ( , 2017b showed the NRIG distribution also performs well in risk management of the US stock return series. We guess the GARCH model with the NRIG distribution would also perform well in risk management of the Germany stock return series.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we follow the model framework in Guo (2017aGuo ( , 2017b and are particularly interested in the NRIG distribution, a newly-developed heavy-tailed distribution. Our focuses are on their empirical performance in fitting the stock market returns in German.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We take advantage of the model framework in Guo (2017aGuo ( , 2017b and are also particularly interested in the normal reciprocal inverse Gaussian (NRIG) distribution, a new class of heavy-tailed distribution, recently emerging in the literature. Guo (2017aGuo ( , 2017b compared a variety of heavy-tailed distributions, Student's t, Skewed t, normal inverse Gaussian (NIG) and NRIG within the generalized autoregressive conditional heteroskedasticity (GARCH) framework for the stock market returns, Guo showed that the NRIG distribution has the best empirical performance in fitting asset returns in the stock markets of the United States and Hong Kong. In this paper, we only focus on the Student's t distribution and the NRIG distributions, and our results indicate the NRIG distribution also has superior performance in fitting the stock market returns in German.…”

Section: Introductionmentioning

confidence: 99%

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A New Class of Heavy-Tailed Distribution and the Stock Market Returns in Germany

Oden¹,

Hurt²,

Gentry³

2017

RBM

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During the past decade, as the fourth largest economy over the world Germany's financial sector plays a key role in the global economy. As one of the most important components of the financial sector, the equity market played a more and more important role. Thus, risk management of its stock market is crucial for welfare of its market participants. To account for the two stylized facts, volatility clustering and conditional heavy tails, we take advantage of the framework in Guo (2017a) and consider empirical performance of the GARCH model with normal reciprocal inverse Gaussian distribution in fitting the German stock return series. Our results indicate the NRIG distribution has superior performance in fitting the stock market returns.

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“…This paper uses the framework of Guo (2017Guo ( , 2019, and is also particularly interested in the performance of the normal reciprocal inverse Gaussian (NRIG) distribution, a new class of GHDs that has been emerging in the literature since 2017. Guo (2017Guo ( , 2019 compared a variety of heavy-tailed distributions (e.g., the Students t, skewed t, normal inverse Gaussian (NIG) and NRIG) within the GARCH framework for the stock market returns. He noted that the NRIG distribution has the best empirical performance in fitting asset returns in stock markets of the United States and Hong Kong.…”

Section: Introductionmentioning

confidence: 99%

Modelling Crude Oil Returns Using the NRIG Distribution

Chinhamu

Mabaso

Chifurira

2020

Stat., optim. inf. comput.

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Over the past decade, crude oil prices have risen dramatically, making the oil market very volatile and risky; hence, implementing an efficient risk management tool against market risk is crucial. Value-at-risk (VaR) has become the most common tool in this context to quantify market risk. Financial data typically have certain features such as volatility clustering, asymmetry, and heavy and semi-heavy tails, making it hard, if not impossible, to model them by using a normal distribution. In this paper, we propose the subclasses of the generalised hyperbolic distributions (GHDs), as appropriate models for capturing these characteristics for the crude oil and gasoline returns. We also introduce the new subclass of GHDs, namely normal reciprocal inverse Gaussian distribution (NRIG), in evaluating the VaR for the crude oil and gasoline market. Furthermore, VaR estimation and backtesting procedures using the Kupiec likelihood ratio test are conducted to test the extreme tails of these models. The main findings from the Kupiec likelihood test statistics suggest that the best GHD model should be chosen at various VaR levels. Thus, the final results of this research allow risk managers, financial analysts, and energy market academics to be flexible in choosing a robust risk quantification model for crude oil and gasoline returns at their specific VaR levels of interest. Particularly for NRIG, the results suggest that a better VaR estimation is provided at the long positions.

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GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns

Cited by 9 publications

References 11 publications

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

Modeling Volatility of Asset and Volume of Trade Returns in the Nigerian Stock Market in the Presence of Random Level Shifts

A New Class of Heavy-Tailed Distribution and the Stock Market Returns in Germany

Modelling Crude Oil Returns Using the NRIG Distribution

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