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Long Memory Volatility Models in R: Application to a Regional Blue Chips Index. (2015). Malaj, Visar .
In: European Journal of Interdisciplinary Studies Articles.
RePEc:eur:ejisjr:41.

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  3. Discovering the drivers of stock market volatility in a data-rich world. (2023). Ryu, Doojin ; Cho, Hoon ; Chun, Dohyun.
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  4. Intraday and overnight tail risks and return predictability in the crude oil market: Evidence from oil-related regular news and extreme shocks. (2023). Bouri, Elie ; Wang, Cheng ; Zhang, Dingsheng ; Xu, Yahua.
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  6. The relative importance of overnight sentiment versus trading-hour sentiment in volatility forecasting. (2023). Qiu, Jianying ; Wan, Xinmin ; Chu, Xiaojun.
    In: Journal of Behavioral and Experimental Finance.
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  7. Overnight volatility, realized volatility, and option pricing. (2022). Yu, Mei ; Yin, Fangsheng ; Cheng, Sicong ; Wang, Tianyi.
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  8. Forecasting risk measures using intraday and overnight information. (2022). Candido, Osvaldo ; Tofoli, Paula V ; Santos, Douglas G.
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  9. State Heterogeneity Analysis of Financial Volatility using high?frequency Financial Data. (2022). Kim, Donggyu ; Chun, Dohyun.
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  10. Observation-driven models for realized variances and overnight returns applied to Value-at-Risk and Expected Shortfall forecasting. (2021). Lucas, Andre ; Opschoor, Anne.
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  31. Long Memory Volatility Models in R: Application to a Regional Blue Chips Index. (2015). Malaj, Visar .
    In: European Journal of Interdisciplinary Studies Articles.
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    Full description at Econpapers || Download paper

  32. Overnight information flow and realized volatility forecasting. (2014). Soucek, Michael ; Souek, Michael ; Todorova, Neda.
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