Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Social and Behavioral Sciences
Rank Tests At Jump Events, Jia Li, Viktor Todorov, George Tauchen, Huidi. Lin
Rank Tests At Jump Events, Jia Li, Viktor Todorov, George Tauchen, Huidi. Lin
Research Collection School Of Economics
We propose a test for the rank of a cross-section of processes at a set of jump events. The jump events are either specific known times or are random and associated with jumps of some process. The test is formed from discretely sampled data on a fixed time interval with asymptotically shrinking mesh. In the first step, we form nonparametric estimates of the jump events via thresholding techniques. We then compute the eigenvalues of the outer product of the cross-section of increments at the identified jump events. The test for rank r is based on the asymptotic behavior of the …
Efficient Estimation Of Integrated Volatility Functionals Via Multi-Scale Jackknife, Jia Li, Yunxiao Liu, Dacheng. Xiu
Efficient Estimation Of Integrated Volatility Functionals Via Multi-Scale Jackknife, Jia Li, Yunxiao Liu, Dacheng. Xiu
Research Collection School Of Economics
We propose semiparametrically efficient estimators for general integrated volatility functionals of multivariate semimartingale processes. A plug-in method that uses nonparametric estimates of spot volatilities is known to induce high-order biases that need to be corrected to obey a central limit theorem. Such bias terms arise from boundary effects, the diffusive and jump movements of stochastic volatility and the sampling error from the nonparametric spot volatility estimation. We propose a novel jackknife method for bias correction. The jackknife estimator is simply formed as a linear combination of a few uncorrected estimators associated with different local window sizes used in the estimation …