Time-Transformed Test for Bubbles under Non-stationary Volatility
针对时变非平稳波动环境下的泡沫检验问题，提出一种基于时间域变形的检验方法，其渐近分布与同方差下的标准检验相同，无需复杂计算，蒙特卡洛模拟和加密货币数据应用验证了有效性。
Abstract This paper is devoted to testing for bubbles under time-varying non-stationary volatility. Because the limiting distribution of the seminal Phillips, Wu, and Yu (2011) test depends on the variance function and usually requires a bootstrap implementation under heteroskedasticity, we construct the test based on a deformation of the time domain. The proposed test is asymptotically pivotal under the null hypothesis and its limiting distribution coincides with that of the standard test under homoskedasticity, so that the test does not require computationally extensive methods for inference. Appealing finite sample properties are demonstrated through Monte-Carlo simulations. An empirical application demonstrates that the upsurge behavior of cryptocurrency time series in the middle of the sample is partially explained by the volatility change.