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ON THE CONSISTENCY OF REGRESSION‐BASED MONTE CARLO METHODS FOR PRICING BERMUDAN OPTIONS IN CASE OF ESTIMATED FINANCIAL MODELS
Author(s) -
Fromkorth Andreas,
Kohler Michael
Publication year - 2015
Publication title -
mathematical finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.98
H-Index - 81
eISSN - 1467-9965
pISSN - 0960-1627
DOI - 10.1111/mafi.12025
Subject(s) - monte carlo method , monte carlo methods for option pricing , consistency (knowledge bases) , econometrics , valuation of options , binomial options pricing model , nonparametric statistics , autoregressive conditional heteroskedasticity , economics , mathematics , computer science , finance , statistics , geometry , volatility (finance)
In many applications of regression‐based Monte Carlo methods for pricing, American options in discrete time parameters of the underlying financial model have to be estimated from observed data. In this paper suitably defined nonparametric regression‐based Monte Carlo methods are applied to paths of financial models where the parameters converge toward true values of the parameters. For various Black–Scholes, GARCH, and Levy models it is shown that in this case the price estimated from the approximate model converges to the true price.