Fast algorithm for nonparametric arbitrage-free SPD estimation
State price density (SPD) contains important information concerning market expectations. An estimator of the SPD based on observed European option prices, taking into account the time of the trade, has been previously considered. Financial markets produce huge amounts of data and, due to time constr...
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| Published in | Computational statistics & data analysis Vol. 51; no. 4; pp. 2339 - 2349 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.12.2006
Elsevier |
| Series | Computational Statistics & Data Analysis |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-9473 1872-7352 |
| DOI | 10.1016/j.csda.2006.08.006 |
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| Abstract | State price density (SPD) contains important information concerning market expectations. An estimator of the SPD based on observed European option prices, taking into account the time of the trade, has been previously considered. Financial markets produce huge amounts of data and, due to time constraints, it is not always possible to calculate the estimator using all available data. Using a model for the covariance structure of the observed option prices, the algorithm identifies observations with little importance to the estimator. Dropping these observations increases the speed of computation and allows frequenter updating of the estimator. The algorithms efficiently use indices that combine information contained in the data. Fast algorithms are proposed and their properties are investigated using both simulated and real data sets. |
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| AbstractList | State price density (SPD) contains important information concerning market expectations. An estimator of the SPD based on observed European option prices, taking into account the time of the trade, has been previously considered. Financial markets produce huge amounts of data and, due to time constraints, it is not always possible to calculate the estimator using all available data. Using a model for the covariance structure of the observed option prices, the algorithm identifies observations with little importance to the estimator. Dropping these observations increases the speed of computation and allows frequenter updating of the estimator. The algorithms efficiently use indices that combine information contained in the data. Fast algorithms are proposed and their properties are investigated using both simulated and real data sets. |
| Author | Hlávka, Zdeněk |
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| Cites_doi | 10.1086/296025 10.1016/S0304-4076(03)00102-7 10.1016/0304-4149(81)90026-0 10.1016/S0304-4076(99)00016-0 |
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| Keywords | Option pricing Nonlinear least squares Constrained estimation |
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| References | Harville (bib6) 1997 Aït-Sahalia, Lo (bib2) 2000; 94 Harrison, Pliska (bib5) 1981; 11 Breeden, Litzenberger (bib3) 1978; 51 Härdle, W., Hlávka, Z., 2006. Dynamics of state price densities, J. Econometrics, submitted for publication. Aït-Sahalia, Duarte (bib1) 2003; 116 Breeden (10.1016/j.csda.2006.08.006_bib3) 1978; 51 10.1016/j.csda.2006.08.006_bib4 Harville (10.1016/j.csda.2006.08.006_bib6) 1997 Aït-Sahalia (10.1016/j.csda.2006.08.006_bib1) 2003; 116 Aït-Sahalia (10.1016/j.csda.2006.08.006_bib2) 2000; 94 Harrison (10.1016/j.csda.2006.08.006_bib5) 1981; 11 |
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| SubjectTerms | Constrained estimation Nonlinear least squares Option pricing |
| Title | Fast algorithm for nonparametric arbitrage-free SPD estimation |
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