ArticlePloS one2020
The value of Monte Carlo model-based variance reduction technology in the pricing of financial derivatives.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
- Retraction · 2024-11-07Concerns/Issues about Peer Review · Duplication of/in Article · Euphemisms for Duplication · Compromised Peer Review · Investigation by Journal/Publisher · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of the study is to reduce the error in the pricing process of financial derivatives, as well as to obtain more accurate product values, thereby reducing transaction costs, accelerating transaction speed, establishing a larger investment scale, and enabling investors to obtain excellent returns under market conditions as much as possible. Based on the variance reduction technology, a Monte Carlo model that can effectively analyze financial prices is added to analyze price fluctuations and find the optimal holding time for users of financial derivatives, thereby reducing the risk of holding the financial derivatives. The results show that the Monte Carlo model-based variance reduction technology can significantly improve the simulation efficiency of financial derivatives pricing. In addition, the importance sampling method is used to optimize the selection, thereby making it closer to the theoretical values. The proposed method is easy to implement and has higher computational efficiency, which can ensure the financial benefits of users holding financial derivatives during the holding period. It can be seen that the Monte Carlo model-based variance reduction technology has high application value in the pricing of financial derivatives, and it is of great significance for the pricing of other products.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.