Stock volatility predictability in bull and bear markets

被引:9
|
作者
Li, Xingyi [1 ]
Zakamulin, Valeriy [1 ]
机构
[1] Univ Agder, Sch Business & Law, Serv Box 422, N-4604 Kristiansand, Norway
关键词
Stock markets; Volatility forecasting; State dependence; High-frequency data; HIGH-FREQUENCY DATA; IMPLIED VOLATILITIES; EQUITY PREMIUM; RETURN; MODEL; INFORMATION; RISK; COMPONENTS; GARCH;
D O I
10.1080/14697688.2020.1725101
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
The recent literature on stock return predictability suggests that it varies substantially across economic states, being strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state dependent. In particular, in this paper, we use a large data set of high-frequency data on individual stocks and a few popular time-series volatility models to comprehensively examine how volatility forecastability varies across bull and bear states of the stock market. We find that the volatility forecast horizon is substantially longer when the market is in a bear state than when it is in a bull state. In addition, over all but the shortest horizons, the volatility forecast accuracy is higher when the market is in a bear state. This difference increases as the forecast horizon lengthens. Our study concludes that stock volatility predictability is strongest during bad economic times, proxied by bear market states.
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页码:1149 / 1167
页数:19
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