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Informative Option Portfolios in Unscented Kalman Filter Design for Affine Jump Diffusion Models

SSRN, ISSN: 1556-5068
2020
  • 2
    Citations
  • 800
    Usage
  • 6
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    2
    • Citation Indexes
      2
  • Usage
    800
    • Abstract Views
      651
    • Downloads
      149
  • Captures
    6
  • Ratings
    • Download Rank
      395,069

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.

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