Informative Option Portfolios in Unscented Kalman Filter Design for Affine Jump Diffusion Models
SSRN, ISSN: 1556-5068
2020
- 2Citations
- 800Usage
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Option pricing models are tools for pricing and hedging derivatives. Good models are complex and the econometrician faces many design decisions when bringing them to the data. I show that strategically constructed low-dimensional filter designs outperform those that try to use all the available option data. I construct Unscented Kalman Filters around option portfolios that aggregate option data, and track changes in risk-neutral volatility and skewness. These low-dimensional filters perform equivalently to or better than standard approaches that treat full option panels. The performance advantage is greatest in empirically relevant settings: in models with strongly skewed jump components that are not driven by Brownian volatility.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85109533218&origin=inward; http://dx.doi.org/10.2139/ssrn.3527094; https://www.ssrn.com/abstract=3527094; https://dx.doi.org/10.2139/ssrn.3527094; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3527094; https://ssrn.com/abstract=3527094
Elsevier BV
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