Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1197-1218 |
| Number of pages | 22 |
| Journal | Annales de l'institut Henri Poincare (B) Probability and Statistics |
| Volume | 47 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2011 Nov |
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Keywords
- Discrete observations
- High frequency data
- Power variations
- Quadratic variation
- Stable convergence
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Irregular sampling and central limit theorems for power variations : The continuous case. / Hayashi, Takaki; Jacod, Jean; Yoshida, Nakahiro.
In: Annales de l'institut Henri Poincare (B) Probability and Statistics, Vol. 47, No. 4, 11.2011, p. 1197-1218.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Irregular sampling and central limit theorems for power variations
T2 - The continuous case
AU - Hayashi, Takaki
AU - Jacod, Jean
AU - Yoshida, Nakahiro
PY - 2011/11
Y1 - 2011/11
N2 - 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.
AB - 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.
KW - Discrete observations
KW - High frequency data
KW - Power variations
KW - Quadratic variation
KW - Stable convergence
UR - http://www.scopus.com/inward/record.url?scp=80054098976&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054098976&partnerID=8YFLogxK
U2 - 10.1214/11-AIHP432
DO - 10.1214/11-AIHP432
M3 - Article
AN - SCOPUS:80054098976
VL - 47
SP - 1197
EP - 1218
JO - Annales de l'institut Henri Poincare (B) Probability and Statistics
JF - Annales de l'institut Henri Poincare (B) Probability and Statistics
SN - 0246-0203
IS - 4
ER -