Revisiting Volume vs. GARCH Effects Using Univariate and Bivariate GARCH Models: Evidence from U.S. Stock Markets

Qiao, Zhuo; Wong, Wing‐Keung

doi:10.1007/978-0-387-77117-5_76

Cited by 2 publications

(4 citation statements)

References 49 publications

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“…The existing model is usually set up in a bivariate process, which takes the form of a GARCH specification (Qiao and Wong, ) or in a bivariate autoregression (Lee and Rui, ). However, these bivariate models with the elements of stock returns and trading volume are often commonly correlated with risk factors.…”

Section: Introductionmentioning

confidence: 99%

“…Although a GARCH-type specification has previously been built into models (Lamoureux and Lastrapes, 1990;Wagner and Marsh, 2005;Qiao and Wong, 2010), detailed econometric refinements, including discriminating among data in different regimes, choosing thresholds, and modelling the asymmetric effects on volatility and nonlinear estimations, have been left out.…”

Section: Introductionmentioning

confidence: 99%

“…The evidence suggests that the volatility persistence decreases when trading volume is considered as an exogenous variable in the conditional variance equation. Using a bivariate GARCH model, Qiao and Wong () examine the spillover effects of volume and turnover for 20 companies from the New York Stock Exchange (NYSE) during several sub‐periods from 1982 to 2002. Their findings suggest that volume has an effect on conditional volatility and, hence, volume is included in the variance equation, which is consistent with the findings of Lamoureux and Lastrapes ().…”

Section: Introductionmentioning

confidence: 99%

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Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

Chen

Chiang

2015

Japanese Economic Review

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This paper presents a capital asset pricing model‐based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and “abnormal trading volume”. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

Chen

Chiang

2015

Japanese Economic Review

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“…Lamoureux and Lastrapes [ 24 ] showed that the encountered high persistence in volatility processes resulted from the volume effects that had not been taken into account. Qiao and Wong [ 25 ] followed a bivariate approach and confirmed that the Lamoureux and Lastrapes [ 24 ] effect exists due to the volume and turnover effects on conditional volatility and after the introduction of volume/turnover as exogenous variables, it is possible to obtain a significant decline in the the persistence. Mikosch and Stǎricǎ [ 26 ] showed that structural changes had an important impact that leads to accepting an integrated GARCH process.…”

Section: Introductionmentioning

confidence: 99%

Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

Bildirici

Ersin

2014

The Scientific World Journal

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The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.

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Revisiting Volume vs. GARCH Effects Using Univariate and Bivariate GARCH Models: Evidence from U.S. Stock Markets

Cited by 2 publications

References 49 publications

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

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