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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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Journal ArticleDOI

Options and Bubbles

TL;DR: In this article, the Cox-Ingersoll-Ross (CIR) term structure model, the constant elasticity of variance (CEV) model, and the Heston stochastic volatility model were proposed.
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Pricing and hedging derivative securities with neural networks and a homogeneity hint

TL;DR: In this article, a generalized option pricing model with a functional shape similar to the usual Black-Scholes formula is proposed. But instead of setting up a learning network mapping the ratio St/K and the time to maturity directly into the derivative price, the pricing function is broken down into two parts, one controlled by the ratio S/K, the other one by a function of time-to-maturity.
Journal ArticleDOI

Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging

TL;DR: The results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years.
Journal ArticleDOI

A Nonlinear Factor Analysis of S&P 500 Index Option Returns

TL;DR: In this paper, Coval and Shumway examined the returns on deltahedged option positions, focusing on the at-the-money straddle, and found average returns close to minus 3% per week.
Journal ArticleDOI

The Valuation of Volatility Options

TL;DR: In this paper, the authors examined the valuation of European and American-style volatility options based on a general equilibrium stochastic volatility framework and provided the optimal exercise region and the option price when volatility follows a general diffusion process.
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.