In the context of high frequency data, one often has to deal with observations occurring at irregularly spaced times, at transaction times for example in finance. Here we examine how the estimation of the squared or other powers of the volatility is affected by irregularly spaced data. The emphasis is on the kind of assumptions on the sampling scheme which allow to provide consistent estimators, together with an associated central limit theorem, and especially when the sampling scheme depends on the observed process itself.
|ジャーナル||Annales de l'institut Henri Poincare (B) Probability and Statistics|
|出版ステータス||Published - 2011 11月|
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