Canonical Least-Squares Monte Carlo Valuation of American Options: Convergence and Empirical Pricing Analysis | Zendy

Xisheng Yu | Zendy; Qiang Liu | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Canonical Least-Squares Monte Carlo Valuation of American Options: Convergence and Empirical Pricing Analysis

Author(s) -

Xisheng Yu,

Qiang Liu

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/763751

Subject(s) - monte carlo methods for option pricing , monte carlo method , binomial options pricing model , mathematics , valuation of options , quasi monte carlo method , econometrics , mathematical optimization , computer science , hybrid monte carlo , markov chain monte carlo , statistics

The paper by Liu (2010) introduces a method termed the canonical least-squares Monte Carlo (CLM) which combines a martingale-constrained entropy model and a least-squares Monte Carlo algorithm to price American options. In this paper, we first provide the convergence results of CLM and numerically examine the convergence properties. Then, the comparative analysis is empirically conducted using a large sample of the S&P 100 Index (OEX) puts and IBM puts. The results on the convergence show that choosing the shifted Legendre polynomials with four regressors is more appropriate considering the pricing accuracy and the computational cost. With this choice, CLM method is empirically demonstrated to be superior to the benchmark methods of binominal tree and finite difference with historical volatilities

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research