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A general method for debiasing a Monte Carlo estimator

Listed author(s):
  • Don McLeish
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    Consider a process, stochastic or deterministic, obtained by using a numerical integration scheme, or from Monte-Carlo methods involving an approximation to an integral, or a Newton-Raphson iteration to approximate the root of an equation. We will assume that we can sample from the distribution of the process from time 0 to finite time n. We propose a scheme for unbiased estimation of the limiting value of the process, together with estimates of standard error and apply this to examples including numerical integrals, root-finding and option pricing in a Heston Stochastic Volatility model. This results in unbiased estimators in place of biased ones i nmany potential applications.

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    Paper provided by in its series Papers with number 1005.2228.

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    Date of creation: May 2010
    Date of revision: Jun 2010
    Handle: RePEc:arx:papers:1005.2228
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