主  題:Bootstrap and Large Deviation for Realized Laplace Transform of Volatility

內(nèi)容簡介:We develop and implement bootstrap methods for realized Laplace transform of volatility-based statistics. We show that a naive wild bootstrap method fails to work for realized Laplace transform of volatility. Next, we consider a modified wild bootstrap and the local Gaussian bootstrap methods and prove their first-order asymptotic validity. Motivated by the good performance of the local Gaussian bootstrap method in finite samples, we use Edgeworth expansions to compare its accuracy with the existing first-order feasible asymptotic theory. Our cumulants expansions show that the local Gaussian bootstrap able to mimic the higher-order bias of the studentized statistic and for which second-order asymptotic are obtained. Our Monte Carlo simulations studies show that the local Gaussian bootstrap outperforms the finite sample properties of the modified wild bootstrap and the existing first-order asymptotic theory. Finally, we will discuss some large deviation principles for the realized Laplace transform of volatility. This is joint works with Ulrich Hounyo and Rasmus T. Varneskov, and with Lidan He and Xinwei Feng.

報告人:劉志      副教授

時  間:2018-12-26    10:00

地  點:竟慧東樓302

舉辦單位:統(tǒng)計與數(shù)學學院  統(tǒng)計科學與大數(shù)據(jù)研究院  科研部