Forecasting stock price volatility: New evidence from the GARCH-MIDAS model

Wang, Lu; Ma, Feng; Liu, Jing; Yang, Lin

doi:10.1016/j.ijforecast.2019.08.005

Cited by 138 publications

(69 citation statements)

References 59 publications

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“…Pan and Liu (2018) extend GARCH-MIDAS to asymmetric GARCH-MIDAS and conclude that by taking asymmetric effects into account, GARCH-MIDAS significantly dominates other alternatives. This is in line with the findings presented by Wang, Ma, Liu, and Yang (2020).…”

Section: Introductionsupporting

confidence: 94%

“…Furthermore, when both traditional GARCH and GARCH-MIDAS are estimated by trading volume, the MIDAS approach is not able to dominate traditional GARCH in most cases. This contradicts the findings presented by Asgharian, Hou, and Javed (2013), Borup and Jakobsen (2019), , Wang, Ma, Liu, and Yang (2020), and Conrad and Kleen (2020), who support that GARCH-MIDAS could provide more accurate forecasts than traditional GARCH. However, it should be noted that previous research studies compare GARCH-MIDAS with a predictor to traditional GARCH without the same predictor due to the availability of relatively higher-frequency data for most macroeconomic indicators.…”

Section: Resultsmentioning

confidence: 60%

“…The process of estimating g i,t is similar to the process of estimating the conditional variance in the traditional GARCH family model. With respect to the presence of the asymmetric effect in a financial market time series, we also employ GJRGARCH-MIDAS as suggested by Pan and Liu (2018) and Wang, Ma, Liu, and Yang (2020).…”

Section: Methodsmentioning

confidence: 99%

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An empirical study on the role of trading volume and data frequency in volatility forecasting

Liu

Choo

2020

Journal of Forecasting

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This research investigates the role of trading volume and data frequency in volatility forecasting by evaluating the performance of Generalized Autoregressive Conditional Heteroskedasticity Mixed-Data Sampling (GARCH-MIDAS), traditional GARCH, and intraday GARCH models. We take trading volume as the proxy for information flow and examine whether the Sequential Information Arrival Hypothesis (SIAH) is supported in the China stock market. The contributions of this study are as follows. (1) We provide a more consistent comparison to evaluate the forecasting ability of the MIDAS approach. (2) We extend the literature on the forecasting performance of trading volume to the GARCH-MIDAS approach. (3) We present clear evidence to support that forecasting ability strongly relies upon data frequency. The empirical results show that: (1) GARCH-MIDAS is not able to beat the traditional GARCH method when both are estimated by the same predictor sampled at different frequencies; (2) there is a positive relation between trading volume and volatility, but no clear evidence appears that SIAH holds in the China stock market; and(3) high-frequency data are highly recommended for daily realized volatility (RV) forecasting, whereas intraday GARCH could significantly outperform traditional GARCH and GARCH-MIDAS in volatility forecasting.

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Section: Introductionsupporting

confidence: 94%

Section: Resultsmentioning

confidence: 60%

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An empirical study on the role of trading volume and data frequency in volatility forecasting

Liu

Choo

2020

Journal of Forecasting

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“…There is a limited and growing literature on the stock market volatility response towards Covid-19 (Apergis & Apergis, 2020;Gu et al, 2020;Onali, 2020;Wang et al, 2020;Yar, 2020;Yilmazkuday, 2020). Apergis & Apergis, (2020) studied the stock market of China and concluded that the stock market returns negative but significantly affected by Covid-19 while the volatility of market returns are positive and significantly affected.…”

Section: Introductionmentioning

confidence: 99%

Do the stocks' returns and volatility matter under the COVID-19 pandemic? A Case Study of Pakistan Stock Exchange

2021

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The catastrophe that the world is now facing in the form of COVID-19, has affected most of the world economies and financial markets as a result of lockdown, travelling restrictions, and social distances. The present study attempted to investigate the effects of COVID-19 on the stock returns of the Pakistan Stock Exchange. The data employed comprises daily prices of Pakistan Stock Exchange, the daily value of exchange rate over the period 01 January 2011 to 30 April 2021, and a dummy variable for COVID-19 which takes 1 for the period during COVID-19 and 0 for the period before. The data were sourced from the Karachi Stock Exchange website, National Institute of Health Sciences Pakistan, and State Bank of Pakistan. We applied the autoregressive conditional heteroskedastic (ARCH) and the associate generalized autoregressive conditionally heteroskedastic (GARCH) approaches to analyze the impact. Our findings revealed that a negative relationship exists between our variables of interest with mean returns and a positive relationship with the volatility of the KSE-100 index. This implies that the COVID-19 pandemic has affected the stock price and increases the volatility of the KSE-100 index, and further affects the financial system. The study recommends that an urgent and powerful response is needed on the part of the government,including strong measures to prevent a severe stock market crash in Pakistan in near future.

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“…Second, the GARCH-MIDAS model can fully capture the relevant information in real time and avoid the delay of economic data publication, and this could improve the model's forecasting accuracy. Owing to these advantages, the GARCH-MIDAS model is employed widely in empirical forecasting analyses and obtains excellent performance (Asgharian, Hou, & Javed, 2013;Conrad, Custovic, & Ghysels, 2018;Conrad & Kleen, 2019;Fang, Lee, & Su, 2020;Wang, Ma, Liu, & Yang, 2020;Wei, Liu, Lai, & Hu, 2017). However, nothing is perfect, and the GARCH-MIDAS model cannot adapt to structural breaks, which indeed commonly exist in the energy market.…”

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confidence: 99%

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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Forecasting stock price volatility: New evidence from the GARCH-MIDAS model

Cited by 138 publications

References 59 publications

An empirical study on the role of trading volume and data frequency in volatility forecasting

An empirical study on the role of trading volume and data frequency in volatility forecasting

Do the stocks' returns and volatility matter under the COVID-19 pandemic? A Case Study of Pakistan Stock Exchange

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

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