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Stock market volatility forecasting: Do we need high-frequency data?
International Journal of Forecasting ( IF 7.022 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.ijforecast.2020.12.001
Štefan Lyócsa , Peter Molnár , Tomáš Výrost

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.



中文翻译:

股市波动预测:我们需要高频数据吗?

波动率预测文献中的普遍共识是,高频波动率模型优于低频波动率模型。但是,当根据每日收益估算低频波动率模型时,可以得出这样的结论。取而代之的是,我们考虑基于每日开盘价,最高价,最低价和收盘价的每日低频波动估计量来研究此问题。我们的数据样本包含18个股市指数。我们发现,仅就短期预测而言,高频波动率模型往往优于低频波动率模型。随着预测范围的增加(最多一个月),对于大多数市场指数,预测准确性的差异在统计上将变得难以区分。为了评估结果的实际含义,我们研究了一个简单的资产分配问题。

更新日期:2021-01-01
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