Nonparametric Filtering of Conditional State-Price Densities
60 Pages Posted: 20 Jan 2016 Last revised: 31 Jul 2018
Date Written: May 8, 2018
This paper studies the use of noisy high-frequency data to estimate the time-varying state-price density implicit in European option prices. Our kernel estimator of the conditional pricing function and its derivatives can be used for model-free pricing, hedging, and risk measurement. Infill asymptotic theory is derived that applies when the pricing function is either smoothly varying or driven by diffusive state variables. Trading times and moneyness levels are modelled by nonstationary marked point processes to capture intraday trading patterns. The estimator is applied to S&P 500 E-mini European call and put option mid quotes using iterated plug-in bandwidth surfaces. A simulation study investigates the performance of the estimator in various scenarios. An application towards delta- and minimum variance-hedging illustrates the use of the estimator.
Keywords: Option Pricing, Kernel Regression, High Frequency Data, Random Sampling Times
JEL Classification: C14, G13
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