On the asymptotic behavior of bubble date estimators
In this study, we extend the three-regime bubble model of Pang et al. (2021) to allow the forth regime followed by the unit root process after recovery. We provide the asymptotic and finite sample justification of the consistency of the collapse date estimator in the two-regime AR(1) model. The consistency allows us to split the sample before and after the date of collapse and to consider the estimation of the date of exuberation and date of recovery separately. We have also found that the limiting behavior of the recovery date varies depending on the extent of explosiveness and recovering.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Improving the accuracy of bubble date estimators under time-varying volatility
In this study, we consider a four-regime bubble model under the assumption of time-varying volatility and propose the algorithm of estimating the break dates with volatility correction: First, we estimate the emerging da…
Endogenous Stochastic Arbitrage Bubbles and the Black--Scholes model
This paper develops a model that incorporates the presence of stochastic arbitrage explicitly in the Black--Scholes equation. Here, the arbitrage is generated by a stochastic bubble, which generalizes the deterministic a…
Testing for an Explosive Bubble using High-Frequency Volatility
Based on a continuous-time stochastic volatility model with a linear drift, we develop a test for explosive behavior in financial asset prices at a low frequency when prices are sampled at a higher frequency. The test ex…
Asymptotic and finite-sample properties of estimators based on stochastic gradients
Stochastic gradient descent procedures have gained popularity for parameter estimation from large data sets. However, their statistical properties are not well understood, in theory. And in practice, avoiding numerical i…
parameter estimationQuantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation
Nowadays, recommendation systems have become crucial to online platforms, shaping user exposure by accurate preference modeling. However, such an exposure strategy can also reinforce users' existing preferences, leading …
Recommendation Systems