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Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

机译：交易和非交易期实现的市场波动：预测美国股票的波动是否重要？

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摘要

We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks' own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks' sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean-variance investor. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：我们研究了使用交易时段和非交易时段的市场波动率来建模和预测未来1到22天的股票波动率的潜在优势。通过使用十年的标准普尔500指数高频数据，通过估计有无交易期市场风险因素的异质自回归（HAR）模型规格，我们证明了隔夜波动率信息的作用。股票本身的隔夜收益平方在股票和预测范围以及资产配置过程中表现不佳。相比之下，我们发现有压倒性的证据表明，由S＆P Mini期货代理的市场水平的波动对于提高模型拟合和波动率预测的准确性至关重要。对于最多五个交易日的短期预测范围以及非交易期市场水平的波动，发现了最大的模型拟合和预测改进。已发现有记载的预测准确性提高与股票对市场风险因素的敏感性有关。最后，我们表明，交易和非交易时段市场实现的波动率在资产分配环境中都是相关的，因为它们增加了平均方差投资者的平均收益，夏普比率和确定性等价收益。 （C）2019国际预报员协会。由Elsevier B.V.发布。保留所有权利。

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