Is There an AI Bubble? Robust Date-Stamping for Periods of Exuberance
是否存在人工智能泡沫？亢奋期的稳健起止时间识别AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究价格序列的持续波动、爆炸性检验及泡沫阶段的起止时间识别。
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是否存在人工智能泡沫？亢奋期的稳健起止时间识别
- 研究问题
- 股票价格具有持续且时变的波动率时，如何检验爆炸性泡沫并识别其起止时间，避免波动尖峰造成短暂误报？
- 方法与思路
- 作者将右尾 Dickey–Fuller 检验扩展至持续均值和波动动态，利用中等偏差渐近理论构造随机波动稳健检验及起止临界值。
- 材料陈述的贡献
- 摘要称，新程序无需干扰参数，时间定位估计具有一致性；模拟展示强波动下的改进，股票应用报告不同企业的亢奋时段。
- 阅读关注点
- 应核对持续性与随机波动的允许范围、起止判据和临界值、时间定位一致性条件，以及样本期间和股价序列的处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未获核验；摘要未说明样本截止日，统计爆炸性证据不能直接确定基本面偏离或未来走势。
原文摘要
The recent surge in valuations among AI related firms has renewed concerns that markets may be entering a new phase of speculative exuberance, especially in the technology and semiconductor sectors at the center of the AI investment wave. This paper develops a practical econometric framework for detecting, date-stamping, and drawing inference on the origination and collapse of bubble episodes when prices evolve under persistent, time-varying volatility. Standard bubble tests are typically derived under homoskedasticity or weak heteroskedasticity and may therefore yield misleading inference in more general settings. We extend right-tailed Dickey-Fuller unit root tests to autoregressive models with highly persistent mean and volatility dynamics, delivering a stochastic-volatility-robust ADF (SV-ADF) test that accommodates persistent variance without imposing strict parametric structure. Building on a moderate-deviation asymptotic theory, the SV-ADF yields nuisance-parameter-free procedures with distinct critical values for origination and collapse, producing more stable alarms and fewer transient false positives around volatility spikes. We establish consistency of the date-stamping estimator and show that it remains asymptotically tractable. Monte Carlo simulations document strong power and substantial gains over homoskedastic (PWY) procedures when volatility dynamics are pronounced. An empirical analysis of AI-exposed equities, including the "Magnificent Seven" and leading semiconductor firms, finds pervasive exuberance with substantial heterogeneity in timing, intensity, and duration. The evidence points to especially strong bubble dynamics for Alphabet and TSMC in the current cycle, while Tesla and Nvidia exhibited pronounced explosive episodes in earlier phases of the AI-driven market cycle.