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American options with stochastic dividends and volatility: A nonparametric investigation

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In this paper, the authors provide a full discussion of the theoretical foundations of American option valuation and exercise boundaries and show how they depend on the various sources of uncertainty which drive dividend rates and volatility, and derive equilibrium asset prices, derivative prices and optimal exercise boundaries in a general equilibrium model.
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This article is published in Journal of Econometrics.The article was published on 2000-01-01 and is currently open access. It has received 95 citations till now. The article focuses on the topics: Implied volatility & Volatility smile.

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Citations
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Book ChapterDOI

The Econometrics of Option Pricing

TL;DR: In this article, a survey of the interplay between preferences and option pricing is presented, focusing on the complexity of the latent variables and the inherent complex nonlinearities of the pricing formulas.
Journal ArticleDOI

Nonparametric state price density estimation using constrained least squares and the bootstrap

TL;DR: This article proposed a nonparametric estimator of option pricing models which incorporates various restrictions (such as monotonicity and convexity) within a single least squares procedure, and applied the techniques to option pricing data on the DAX.
Journal ArticleDOI

Option Pricing With Modular Neural Networks

TL;DR: This paper investigates a nonparametric modular neural network model to price the S&P-500 European call options and concludes that modularity improves the generalization properties of standard feedforward neural network option pricing models.
Journal ArticleDOI

The Normal Inverse Gaussian Distribution and the Pricing of Derivatives

TL;DR: In this article, the authors proposed the class of normal inverse gaussian (NIG) distributions to approximate an unknown risk-neutral density, which is characterized by the first four moments: mean, variance, skewness, and kurtosis.
Journal ArticleDOI

A unified approach to Bermudan and barrier options under stochastic volatility models with jumps

TL;DR: In this paper, the authors developed a fast and accurate method for pricing American and barrier options in regime switching jump diffusion models by blending regime switching models and Markov chain approximation techniques in the Fourier domain.
References
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BookDOI

Density estimation for statistics and data analysis

TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Journal ArticleDOI

Conditional heteroskedasticity in asset returns: a new approach

Daniel B. Nelson
- 01 Mar 1991 - 
TL;DR: In this article, an exponential ARCH model is proposed to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987, which is an improvement over the widely-used GARCH model.
Journal ArticleDOI

Generalized Additive Models.

Book

Brownian Motion and Stochastic Calculus

TL;DR: In this paper, the authors present a characterization of continuous local martingales with respect to Brownian motion in terms of Markov properties, including the strong Markov property, and a generalized version of the Ito rule.
Journal ArticleDOI

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.
Frequently Asked Questions (10)
Q1. What contributions have the authors mentioned in the paper "American options with stochastic dividends and volatility: a nonparametric investigation" ?

In this paper, the authors study the effect of volatility on the performance of the OEX contract on the S & P100 stock index. 

To choose the bandwith parameter the authors followed a procedure called generalized cross-validation, described in Craven and Wahba (1979) and used in the context of option pricing in Broadie et. al. (1995). 

1Two critical assumptions, namely (1) a constant dividend rate and(2) constant volatility, are often cited as restrictive and counter-factual. 

the nonparametric approach does achieve the main goal of their econometric anaylsis, namely to determine whether the volatility and/or the dividend rate a ect the valuation of the contract and the exercise policy. 

The most widely used kernel estimator of g in (3.11) is the NadarayaWatson estimator de ned byĝ (z) =Pn i=1K Zi zYiPni=1K Zi z ; (3.12) so thatĝ (Z1); : : : ; ĝ (Zn) 0 =WKn ( )Y; where Y = (Y1; : : : ; Yn) 0 and WKn is a n n matrix with its (i; j)-th element equal to K Zj Zi Pn k=1K Zk Zi : WKn is called the in uence matrix associated with the kernel K: 

The argument is that for a wide variety of misspeci ed ARCH models the di erence between the (EG)ARCH volatility estimates and the true underlying di usion volatilities converges to zero in probability as the length of the sampling time interval goes to zero at an appropriate rate. 

Several papers were devoted to the subject, namely Nelson (1990, 1991, 1992, 1996a,b) and Nelson and Foster (1994, 1995), which brought together two approaches, ARCH and continuous time SV, for modelling time-varying volatility in nancial markets. 

In this context, the value ofany contingent claim is simply given by its shadow price, i.e., the priceat which the representative agent is content to forgo holding the asset. 

Two state variables are required tomodel a stochastic dividend yield which is imperfectly correlated with thevolatility coe cients of the stock price process. 

The results so far seem to suggest two things: (1) conditioning on t does not displace pricing of options and (2) the volatility e ect seems to be present only for large (fourth quartile) volatilities.