Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks

Chen, Chun‐Hung; Yu, Wei-Choun; Zivot, Eric

doi:10.1016/j.ijforecast.2011.04.005

Cited by 30 publications

(19 citation statements)

References 34 publications

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“…It is therefore found that when evaluating the AICc information criteria (in-sample analysis), the exogenous variables provided better-fitting models for most of the stocks. It is also worth noting that the pre-opening (OP) and the total overnight (OV) periods appear to incorporate more information than the after-market period, which may be explained by the lower variation of the after period in relation to the others, as seen in Table 6, and corroborating the results found by Chen et al (2012).…”

Section: Results Analysissupporting

confidence: 77%

“…Similarly to the window used by Chen et al (2012), the out-of-sample period analyzed in this study was approximately one year (260 days). The in-sample analysis was carried out with all the other data from the sample, since, according to Ng and Lam (2006), in-sample windows with approximately a thousand observations minimize the impacts on the estimation of the coefficients of the GARCH-family models.…”

Section: Methodsmentioning

confidence: 99%

“…However, most of the studies that seek to model the conditional volatility of stocks or indices ignore the variation that occurs between the opening period of one day and the close of the previous day, also known as the overnight period. We found some studies in Brazil that have sought to analyze the significance of the information found in the overnight period (Accioly & Mendes, 2015;Souza, 2004), while finding various studies from abroad that discuss the issue (Barclay & Hendershott, 2004;Chen, Yu & Zivot, 2012;Gallo & Pacini, 1998;Martens, 2002;Taylor, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…This study differs from the rest especially by dividing overnight return into sub periods, as is done by Chen et al (2012). However, due to the peculiarities of the Brazilian market, the overnight period was divided into two sub periods, instead of three: the after-market and the pre-opening periods.…”

Section: Introductionmentioning

confidence: 99%

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Modeling conditional volatility by incorporating non-regular trading hours into the APARCH model

Araújo

Camargos

Pinho

2019

Rev. contab. finanç.

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This study aims to evaluate how the after-market and pre-opening periods affect the estimation of conditional volatility one day ahead. Volatility features quite a lot in Finance studies because it is a fundamental parameter in derivatives pricing, the efficient allocation of portfolios, and risk management. The results are relevant for investment agents to be able to refine volatility forecasting models and achieve better results in derivatives pricing, risk management, and portfolio optimization. We used the asymmetric power autoregressive conditional heteroscedasticity (APARCH) model, incorporating the after-market, pre-opening, and total overnight periods to assess whether they contain important information for modeling volatility. We analyzed the 20 stocks of Brazilian companies listed on the São Paulo Stock, Commodities, and Futures Exchange (BM&FBovespa) and also belonging to the BR Titans 20 with ADRs listed on the New York Stock Exchange and the Nasdaq. The results were evaluated in-sample using the corrected Akaike information criterion (AICc) and the statistical significance of the coefficients, and out-of-sample using root mean squared error (RMSE), mean absolut percentage error (MAPE), the R² of the Mincer-Zarnowitz regression, and the Diebold Mariano test. The analysis does not enable it to be claimed which is the best model, because there is no unanimity among all the stocks; however, non-regular trading hours were shown to incorporate important information for most of the stocks. Furthermore, the models that incorporated the pre-opening period generally obtained superior results to the models that incorporated the after-market period, demonstrating that this period contains important information for forecasting conditional volatility.

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Section: Results Analysissupporting

confidence: 77%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling conditional volatility by incorporating non-regular trading hours into the APARCH model

Araújo

Camargos

Pinho

2019

Rev. contab. finanç.

View full text Add to dashboard Cite

show abstract

“…While Taylor (2007) and Tsiakas (2008) incorporate overnight information flow to their respective assessments of volatility on asset returns, Chen et al (2012) find that the inclusion of the preopen time can markedly improve the out-of-sample predictability of the next-day volatility forecast. Further, Anderson and Vahid (2007) develop univariate and multivariate forecasting models for historical volatility in Australian stocks and show that although the latter models outperform the former models, there was little difference between simple and sophisticated factor models.…”

Section: Introductionmentioning

confidence: 98%

Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns

Erdugan

Kulendran

Natoli

2019

Financ Mark Portf Manag

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This study examines whether incorporating volatility improves the forecast of directional changes in the returns of Australia's banking, industrial and resource sectors. This study first estimates a benchmark non-volatility logit regression model and assesses it against four estimated volatility logit models measured by mean absolute deviation, standard deviation, return squared (U 2 ) and range. An out-of-sample prediction performance, assessed by Brier's QPS statistic and hit ratio, confirms that volatility improves the prediction of directional changes of returns. A simple trading strategy is utilized to provide practical improvement in investors' market timing decisions.

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Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models

Wang

et al. 2021

Journal of Forecasting

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This paper explores the effects of global economic policy uncertainty (GEPU) on conditional volatility in the gold futures market using Markov regimeswitching GARCH-MIDAS models. The in-sample empirical results suggest that GEPU indeed contains predictive information for the gold futures market, and higher GEPU leads to higher volatility within the gold futures market.Moreover, the novel model, which adds Markov regime switching with timevarying transition probabilities and the GEPU index, achieves relatively better performance than those of the other competing models from a statistical point of view. Furthermore, we discuss the asymmetric effects of different changes in GEPU on the gold futures market and the models' performances with different horizons, and we find that our new model has better predictive performance under negative changes in GEPU than under positive changes in GEPU. Further discussion also confirms that our previous findings are robust during two special cases, the global financial crisis and European debt crisis, during which the market suffered from fierce fluctuations and was fraught with considerable uncertainty. K E Y W O R D SGARCH-MIDAS model, GEPU, gold futures market volatility, Markov regime switching, volatility forecasting | INTRODUCTIONDifferent from financial assets, gold, as a real asset, is always regarded as an effective buffer to slow down economic and political shocks (

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Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks

Cited by 30 publications

References 34 publications

Modeling conditional volatility by incorporating non-regular trading hours into the APARCH model

Modeling conditional volatility by incorporating non-regular trading hours into the APARCH model

Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns

Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models

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