A framework for adaptive Monte-Carlo procedures
Adaptive Monte Carlo methods are recent variance reduction techniques. In this work, we propose a mathematical setting which greatly relaxes the assumptions needed by for the adaptive importance sampling techniques presented by Vazquez-Abad and Dufresne, Fu and Su, and Arouna. We establish the convergence and asymptotic normality of the adaptive Monte Carlo estimator under local assumptions which are easily verifiable in practice. We present one way of approximating the optimal importance sampling parameter using a randomly truncated stochastic algorithm. Finally, we apply this technique to some examples of valuation of financial derivatives.
|Date of creation:||Jan 2010|
|Date of revision:||Jul 2010|
|Publication status:||Published in Monte Carlo Methods and Applications 17, 1 (2011) 77-98|
|Contact details of provider:|| Web page: http://arxiv.org/|
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- Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 1999. "Asymptotically Optimal Importance Sampling and Stratification for Pricing Path-Dependent Options," Mathematical Finance, Wiley Blackwell, vol. 9(2), pages 117-152.
- Arouna Bouhari, 2004. "Adaptative Monte Carlo Method, A Variance Reduction Technique," Monte Carlo Methods and Applications, De Gruyter, vol. 10(1), pages 1-24, March.
- Sujin Kim & Shane G. Henderson, 2007. "Adaptive Control Variates for Finite-Horizon Simulation," Mathematics of Operations Research, INFORMS, vol. 32(3), pages 508-527, August.
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