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Results for “chop” · papers 3 · wiki 1
Academic Papers · 3arXiv q-fin live 3 · desk corpus 0
arXiv · arXiv q-fin · 2017
This paper shows how to carry out efficient asymptotic variance reduction when estimating volatility in the presence of stochastic volatility and microstructure noise with the realized kernels (RK) from [Barndorff-Nielsen et al., 2008] and the quasi-maximum likelihood estimator (QMLE) studied in [Xiu, 2010]. To obtain such a reduction, we chop the data into B blocks, compute the RK (or QMLE) on each block, and aggreg…
Simon Clinet, Yoann Potiron
arXiv · arXiv q-fin · 2016
We introduce and show the existence of a Hawkes self-exciting point process with exponentially-decreasing kernel and where parameters are time-varying. The quantity of interest is defined as the integrated parameter $T^{-1}\int_0^Tθ_t^*dt$, where $θ_t^*$ is the time-varying parameter, and we consider the high-frequency asymptotics. To estimate it naïvely, we chop the data into several blocks, compute the maximum like…
Simon Clinet, Yoann Potiron
arXiv · arXiv q-fin · 2009
Signatures of universality are detected by comparing individual eigenvalue distributions and level spacings from financial covariance matrices to random matrix predictions. A chopping procedure is devised in order to produce a statistical ensemble of asset-price covariances from a single instance of financial data sets. Local results for the smallest eigenvalue and individual spacings are very stable upon reshuffling…
Gernot Akemann, Jonit Fischmann, Pierpaolo Vivo
Option Blackboard · 0
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