2008•Tongji yu xinxi luntanRequires access

Variance Reduction Techniques of Monte Carlo Simulation Methods in Options Pricing

Hui Chen

Abstract

The variance reduction techniques of Monte Carlo Simulation(MCS) methods play an important role in the simulation efficiency improvement,such as Control Variates(CV),Antithetic Variates(AV),Stratified Sampling(SS),Latin Hypercube Sampling(LHS),Moment Matching(MM) and Importance Sampling(IS) Techniques.From the view of variance reduction efficiency,different MCS of variance reduction techniques can significantly improve MCS efficiency of option pricing,the combination techniques of IS techniques based on the optimal drifting rate and optimal SS along the direction of stratified sampling create an extremely obvious effect of increasing efficiency than the general MCS method.

About this research paper

What this paper is about

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Key concepts: Variance reduction, Control variates, Latin hypercube sampling, Monte Carlo method, Stratified sampling, Importance sampling, Sampling (signal processing), Variance (accounting)