Control variates are a popular technique for reducing the variance of Monte Carlo estimates. Recent literature has enlarged the set of potentially useful control variates. Still, finding an control variate that efficiently reduces estimation error can be a challenging task for which the theoretical literature provides little guidance. In this note we show by theory and example how to construct ...

this paper presents an analytical view of variance reduction by control variate technique for pricing arithmetic asian options as a financial derivatives. in this paper, the effect of correlation between two random variables is shown. we propose an efficient method for choose suitable control in pricing arithmetic asian options based on the control variates (cv). the numerical experiment shows ...

Estimating the contribution of a blurred photon plane involves the computation of an integral along the interval of overlap between the camera segment and the photon plane. In order to reduce variance introduced by this estimation, we apply control variates [Glasserman 2003] in our implementation of the photon plane estimator for homogeneous media. The concept of control variates is simple. Say...

Sequential Monte Carlo is a useful simulation-based method for online filtering of state-space models. For certain complex state-space models, a single proposal distribution is usually not satisfactory and using multiple proposal distributions is a general approach to address various aspects of the filtering problem. This article proposes an efficient method of using multiple proposals in combi...

In this paper we present an overview of classical results about the variance reduction technique of control variates. We emphasize aspects of the theory that are of importance to the practitioner, as well as presenting relevant applications.

Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control variate method to effectively reduce variance for policy gradient methods. Motivated by the Stein’s...

We explore a class of control variates for the American option pricing problem. We construct the control variates by using multivariate adaptive linear regression splines to approximate the option’s value function at each time step; the resulting approximate value functions are then combined to construct a martingale that approximates a “perfect” control variate. We demonstrate that significant...

In this short communication, we present a new, simple control-variate Monte Carlo procedure for enhancing the evaluation accuracy of alternative reinsurance strategies that an insurance company might adopt.

We investigate the use of Antithetic Variables, Control Variates and Importance Sampling to reduce the statistical errors of option sensitivities calculated with the Likelihood Ratio Method in Monte Carlo. We show how Antithetic Variables solve the well-known problem of the divergence of the variance of Delta for short maturities and small volatilities. With numerical examples within a Gaussian...