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Found 8 papers, 0 papers with code
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 date of the explosive bubble, its collapsing date, and the recovering date to the normal market under assumption of homoskedasticity; second, we collect the residuals and then employ the WLS-based estimation of the bubble dates.
Testing for explosive bubbles: a review
This review discusses methods of testing for explosive bubbles in time series.
On the asymptotic behavior of bubble date estimators
We provide the asymptotic and finite sample justification of the consistency of the collapse date estimator in the two-regime AR(1) model.
Robust Inference on Income Inequality: $t$-Statistic Based Approaches
Following the approaches, in particular, a robust large sample test on equality of two parameters of interest (e. g., a test of equality of inequality measures in two regions or countries considered) is conducted as follows: The data in the two samples dealt with is partitioned into fixed numbers $q_1, q_2\ge 2$ (e. g., $q_1=q_2=2, 4, 8$) of groups, the parameters (inequality measures dealt with) are estimated for each group, and inference is based on a standard two-sample $t-$test with the resulting $q_1, q_2$ group estimators.
Time-Transformed Test for the Explosive Bubbles under Non-stationary Volatility
This paper is devoted to testing for the explosive bubble under time-varying non-stationary volatility.
COVID-19: Tail Risk and Predictive Regressions
The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World.
New robust inference for predictive regressions
To relax the exogenous volatility assumption, we propose another method which relies on the nonparametric correction of volatility.
New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence
The approaches are based on robust $t-$statistics tests and new results on their applicability are presented.
