The Effect of News on Return Volatility and Volatility Persistence: The Turkish Economy during Crisis

This paper examines the impact of firms’ sustainability engagement on their stock returns and volatility by employing the EGARCH and FIGARCH models using data from the major financial firms listed in the Chinese stock market. We find evidence of a positive association between sustainability engagement and stock returns, suggesting firms’ sustainability news release in favour of the market. Although volatility persistence can largely be explained by news flows, the results show that sustainability news release has the significant and largest drop in volatility persistence, followed by popularity in Google search engine and the general news. Sustainability news release is found to affect positively stock return volatility. We also find evidence that market expectation can be driven by the dominant social paradigm when sustainability is included. These findings have important implications for market efficiency and effective portfolio management decisions.

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A Multivariate Analysis of the Effects of Structural Breaks on Stock Return Volatility Persistence: The Case of the US and Japan

International Journal of Business Administration ◽

10.5430/ijba.v10n3p39 ◽

2019 ◽

Vol 10 (3) ◽

pp. 39

Author(s):

Chikashi Tsuji

Keyword(s):

Multivariate Analysis ◽

Stock Returns ◽

Stock Return ◽

Structural Breaks ◽

Structural Break ◽

Return Volatility ◽

Time Varying ◽

Stock Return Volatility ◽

Volatility Persistence ◽

The Us

This paper quantitatively inspects the effects of structural breaks in stock returns on their volatility persistence by using the stock return data of the US and Japan. More concretely, applying the diagonal BEKK-MGARCH model with and without structural break dummies to the returns of S&P 500 and TOPIX, we reveal the following interesting findings. (1) First, we clarify that for both the US and Japanese stock returns, the values of the GARCH parameters, namely, the values of the volatility persistence parameters in the diagonal BEKK-MGARCH models decrease when we include the structural break dummies in the models. (2) Second, we further find that interestingly, during the Lehman crisis in 2008, the estimated time-varying volatilities from the diagonal BEKK-MGARCH model with structural break dummies are slightly higher than those from the no structural break dummy model. (3) Third, we furthermore reveal that also very interestingly, the estimated time-varying correlations from the diagonal BEKK-MGARCH model with no structural break dummy are slightly higher than those from the structural break dummy model.

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Modeling stock market return volatility in the presence of structural breaks

International Journal of Research in Business and Social Science (2147-4478) ◽

10.20525/ijrbs.v8i5.308 ◽

2019 ◽

Vol 8 (5) ◽

pp. 156-171

Author(s):

Caroline Michere Ndei ◽

Stephen Muchina ◽

Kennedy Waweru

Keyword(s):

Stock Market ◽

Risk Premium ◽

Structural Breaks ◽

Return Volatility ◽

Additional Risk ◽

Volatility Clustering ◽

Volatility Persistence ◽

Market Return ◽

Stock Market Return ◽

Best Fit

This study sought to model the stock market return volatility at the Nairobi Securities Exchange (NSE) in the presence of structural breaks. Using daily NSE 20 share index for the period 04/01/2010 to 29/12/2017, the market return volatility was modeled using different GARCH type models and taking into account four endogenously identified structural breaks. The market exhibited a non-normal distribution that was leptokurtic and negatively skewed and also showed evidence for ARCH effects, volatility clustering, and volatility persistence. We found that by considering structural breaks, volatility persistence was reduced, while leverage effects were found to lead to explosive volatility. In addition, investors were not rewarded for taking up additional risk since the risk premium was insignificant for the full period. However, during explosive volatility, investors were rewarded for taking up more risk. Moreover, we found that risk premium, leverage effects, and volatility persistence were significantly correlated. The GARCH (1,1) and TGARCH(1,1) models were found to be the best fit models to test for symmetric and asymmetric effects respectively. While the GARCH models were able to provide evidence for the stylized facts in the NSE, we conclude that the presence or absence of these features is period specific. This especially relates to volatility persistence, leverage effects, and risk premium effects. Caution should, therefore, be taken in using a specific GARCH model to forecast market return volatility in Kenya. It is thus imperative to pretest the data before any return volatility forecasting is done.

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Determining the Return Volatility of Major Stock Markets Before and During the COVID-19 Pandemic by Applying the EGARCH Model

Scientific Annals of Economics and Business ◽

10.47743/saeb-2021-0022 ◽

2021 ◽

Vol 68 (4) ◽

pp. 405-419

Author(s):

Letife Özdemir ◽

Ercan OZEN ◽

Simon Grima ◽

Inna Romānova

Keyword(s):

Stock Markets ◽

Stock Index ◽

Return Volatility ◽

Volatility Persistence ◽

Egarch Model ◽

Stock Indexes

With this study, we aim to determine the effect of the Covid-19 pandemic on the return volatility of the DJI, the DAX, the FTSE100 and the CAC40 stock indexes. We take return volatility between 1st January 2019 and 17th July 2020 and split it into two separate periods - before the Covid-19 pandemic outbreak and the first wave of the ‘In-Pandemic’ period. Only the so-called first wave of the pandemic was chosen to avoid the influence of knowledge of possible vaccines and antiviral solutions. Data were analysed by using the exponential GARCH (EGARCH) model. Findings show excessive volatility in the major stock markets with short volatility persistence and the presence of leverage in returns during the first wave of the Covid-19 pandemic outbreak. Moreover, during the pandemic period, positive shocks have been observed to have a greater effect than negative socks on the stock index return volatility.

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