Uncertainty shocks of Trump election in an interval model of stock market
Uncertainty shocks of Trump election in an interval model of stock market
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股市区间模型中特朗普当选的不确定性冲击
DOI：
10.1080/14697688.2020.1800070
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发表时间：
2020-09
影响因子：
1.3
通讯作者：
Wang Shouyang
中科院分区：
文献类型：
--
作者：
Sun Yuying;Qiao Kenan;Wang Shouyang
提出了一类新的区间值时间序列的非线性区间模型。通过区间模型与区间观测值的匹配，我们给出了区间模型的非线性最小距离估计方法，并建立了估计量的渐近理论。与传统的基于点的方法相比，该方法具有上级优点，可以同时评估趋势和波动的变化。本文以2016年美国总统大选（以下简称特朗普大选）对美国股市的影响为例，在所提出的区间框架内进行了实证分析。考虑到日最高价-最低价区间作为市场有效性指标的有效性，我们采用区间值收益率来同时衡量基本价值运动和市场有效性。实证结果表明，一个强有力的证据表明，特朗普当选增加了水平/趋势，并降低了标准普尔500指数的波动性在事前和事后分析。此外，对基本价值影响的半衰期（62.4天）比高-低范围（15.9天）更长，这表明特朗普的胜利对基本价值的影响比对市场效率的影响更持久。
This paper proposes a new class of nonlinear interval models for interval-valued time series. By matching the interval model with interval observations, we develop a nonlinear minimum-distance estimation method for the proposed models, and establish the asymptotic theory for the proposed estimators. Superior to traditional point-based methods, the proposed interval modelling approach can assess the change in both the trend and volatility simultaneously. Within the proposed interval framework, this paper examines the impact of the 2016 US presidential election (henceforth Trump election) on the US stock market as a case study. Considering the validity of daily high-low range as a proxy of market efficiency, we employ an interval-valued return to jointly measure the fundamental value movement and market efficiency simultaneously. Empirical results suggest a strong evidence that the Trump election has increased the level/trend and lowered the volatility of the S&P 500 index in both ex ante and ex post analysis. Furthermore, a longer half-life period for the impact on fundamental value (62.4 days) than high-low range (15.9 days) has shown that the impact of Trump's victory on fundamental value is more persistent than its impact on market efficiency.
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影响因子：
12.6
作者：
Craig Mackinlay
通讯作者：
Craig Mackinlay
DOI：
10.3386/w10117
发表时间：
2003-10
期刊：
Econometrics: Multiple Equation Models eJournal
影响因子：
--
作者：
G. Gallo;R. Engle
通讯作者：
G. Gallo;R. Engle
影响因子：
1.3
作者：
Wei Yang;Ai Han;Yongmiao Hong;Shouyang Wang
通讯作者：
Wei Yang;Ai Han;Yongmiao Hong;Shouyang Wang
影响因子：
3.8
作者：
M. Velden;Patrick J. F. Groenen;Jeroen Poblome
通讯作者：
M. Velden;Patrick J. F. Groenen;Jeroen Poblome
影响因子：
8
作者：
Tetlock, Paul C.
通讯作者：
Tetlock, Paul C.