6533b7ddfe1ef96bd127550c
RESEARCH PRODUCT
A New Nonparametric Estimate of the Risk-Neutral Density with Applications to Variance Swaps
Shuang ZhouKeren LiLiyuan JiangJie YangFangfang Wangsubject
FOS: Computer and information sciencesStatistics and ProbabilityVariance swapOptimization problemvariance swapStatistics - ApplicationsFOS: Economics and businessNormal-inverse Gaussian distributiondouble-constrained optimizationpricingEconometricsApplications (stat.AP)Asset (economics)normal inverse Gaussian distributionMathematicsParametric statisticslcsh:T57-57.97Applied MathematicsNonparametric statisticsEstimatorVariance (accounting)lcsh:Applied mathematics. Quantitative methodsPricing of Securities (q-fin.PR)risk-neutral densitylcsh:Probabilities. Mathematical statisticslcsh:QA273-280Quantitative Finance - Pricing of Securitiesdescription
We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem. We evaluate our approach using the S\&P 500 market option prices from 1996 to 2015. A comprehensive cross-validation study shows that our approach outperforms the existing nonparametric quartic B-spline and cubic spline methods, as well as the parametric method based on the Normal Inverse Gaussian distribution. As an application, we use the proposed density estimator to price long-term variance swaps, and the model-implied prices match reasonably well with those of the variance future downloaded from the CBOE website.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-25 |