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Nonparametric American Option Pricing

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TLDR
In this article, a nonparametric method to accurately price American option contingent claims is proposed, which uses only historical stock price data, not option price data to generate the American option price.
Abstract
We introduce a nonparametric method to accurately price American style contingent claims. This method uses only historical stock price data, not option price data, to generate the American option price. We test the accuracy of this method in a controlled experimental environment under both Black & Scholes (1973) and Heston (1993) assumptions and perform an error-metric analysis. These numerical experiments demonstrate that this method is an accurate and precise method of pricing American options under a variety of market conditions.

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Alternative Tilts for Nonparametric Option Pricing

TL;DR: The authors generalizes the nonparametric approach to option pricing of Stutzer (1996) by demonstrating that the canonical valuation methodology in-troduced therein is one member of the Cressie-Read family of divergence mea- sures.
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Empirical Tests of Canonical Nonparametric American Option Pricing Methods

TL;DR: In this article, Alcock and Carmichael (2008) introduced a nonparametric method for pricing American style options that is derived from the canonical valuation developed by Stutzer (1996).
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Option pricing under GARCH models with Hansen's skewed-t distributed innovations

TL;DR: In this article, option pricing under GARCH models with Hansen's skewed-t distributed innovations is considered, and risk-neutralization is applied to the empirical distribution of the simulated sample paths rather than the innovations' parametric distribution.
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Semi-parametric estimation of American option prices

TL;DR: In this paper, a semi-parametric estimator of American option prices is proposed based on a parameterized stochastic discount factor and is nonparametric w.r.t. the historical dynamics of the Markovian state variables.
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Pricing American Options by Canonical Least-Squares Monte Carlo

TL;DR: In this article, a variation of canonical valuation called canonical least-squares Monte Carlo is proposed to price American options, which proceeds in three stages: first, given a set of historical gross returns (or price ratios) of the underlying for a chosen time interval, a discrete risk-neutral distribution is obtained via the canonical approach.
References
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Journal ArticleDOI

The Pricing of Options and Corporate Liabilities

TL;DR: In this paper, a theoretical valuation formula for options is derived, based on the assumption that options are correctly priced in the market and it should not be possible to make sure profits by creating portfolios of long and short positions in options and their underlying stocks.
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Bootstrap Methods: Another Look at the Jackknife

TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
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A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options

TL;DR: In this paper, a closed-form solution for the price of a European call option on an asset with stochastic volatility is derived based on characteristi c functions and can be applied to other problems.
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Valuing American Options by Simulation: A Simple Least-Squares Approach

TL;DR: In this paper, a new approach for approximating the value of American options by simulation is presented, using least squares to estimate the conditional expected payoff to the optionholder from continuation.

Valuing American Options by Simulation: A Simple Least-Squares Approach - eScholarship

TL;DR: In this article, a simple yet powerful new approach for approximating the value of American options by simulation is presented, based on the use of least squares to estimate the conditional expected payoff to the optionholder from continuation.
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