Control variates

The control variates method is a variance reduction technique used in Monte Carlo methods. It exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknown quantity.^[1] ^[2] ^[3]

Underlying principle

Let the unknown parameter of interest be $\mu$ , and assume we have a statistic $m$ such that the expected value of m is μ: ${\mathbb {E}}\left[m\right]=\mu$ , i.e. m is an unbiased estimator for μ. Suppose we calculate another statistic $t$ such that ${\mathbb {E}}\left[t\right]=\tau$ is a known value. Then

m^{\star }=m+c\left(t-\tau \right)\,

is also an unbiased estimator for $\mu$ for any choice of the coefficient $c$ . The variance of the resulting estimator $m^{{\star }}$ is

{\textrm {Var}}\left(m^{\star }\right)={\textrm {Var}}\left(m\right)+c^{2}\,{\textrm {Var}}\left(t\right)+2c\,{\textrm {Cov}}\left(m,t\right).

It can be shown that choosing the optimal coefficient

c^{\star }=-{\frac {{\textrm {Cov}}\left(m,t\right)}{{\textrm {Var}}\left(t\right)}}

minimizes the variance of $m^{{\star }}$ , and that with this choice,

{\begin{aligned}{\textrm {Var}}\left(m^{\star }\right)&={\textrm {Var}}\left(m\right)-{\frac {\left[{\textrm {Cov}}\left(m,t\right)\right]^{2}}{{\textrm {Var}}\left(t\right)}}\\&=\left(1-\rho _{m,t}^{2}\right){\textrm {Var}}\left(m\right)\end{aligned}}

where

\rho _{m,t}={\textrm {Corr}}\left(m,t\right)\,

is the correlation coefficient of m and t. The greater the value of $\vert \rho _{{m,t}}\vert$ , the greater the variance reduction achieved.

In the case that ${\textrm {Cov}}\left(m,t\right)$ , ${\textrm {Var}}\left(t\right)$ , and/or $\rho _{{m,t}}\;$ are unknown, they can be estimated across the Monte Carlo replicates. This is equivalent to solving a certain least squares system; therefore this technique is also known as regression sampling.

When the expectation of the control variable, ${\mathbb {E}}\left[t\right]=\tau$ , is not known analytically, it is still possible to increase the precision in estimating $\mu$ (for a given fixed simulation budget), provided that the two conditions are met: 1) evaluating $t$ is significantly cheaper than computing $m$ ; 2) the magnitude of the correlation coefficient $|\rho _{m,t}|$ is close to unity. ^[3]

Example

We would like to estimate

I=\int _{0}^{1}{\frac {1}{1+x}}\,{\mathrm {d}}x

using Monte Carlo integration. This integral is the expected value of $f(U)$ , where

f(U)={\frac {1}{1+U}}

and U follows a uniform distribution [0, 1]. Using a sample of size n denote the points in the sample as $u_{1},\cdots ,u_{n}$ . Then the estimate is given by

I\approx {\frac {1}{n}}\sum _{i}f(u_{i}).

Now we introduce $g(U)=1+U$ as a control variate with a known expected value $\mathbb {E} \left[g\left(U\right)\right]=\int _{0}^{1}(1+x)\,\mathrm {d} x={\tfrac {3}{2}}$ and combine the two into a new estimate

I\approx {\frac {1}{n}}\sum _{i}f(u_{i})+c\left({\frac {1}{n}}\sum _{i}g(u_{i})-3/2\right).

Using $n=1500$ realizations and an estimated optimal coefficient $c^{\star }\approx 0.4773$ we obtain the following results

	Estimate	Variance
Classical estimate	0.69475	0.01947
Control variates	0.69295	0.00060

The variance was significantly reduced after using the control variates technique. (The exact result is $I=\ln 2\approx 0.69314718$ .)

Notes

↑ Lemieux, C. (2017). "Control Variates". Wiley StatsRef: Statistics Reference Online: 1--8. doi:10.1002/9781118445112.stat07947.
↑ Glasserman, P. (2004). Monte Carlo Methods in Financial Engineering. New York: Springer. ISBN 0-387-00451-3 (p. 185)
1 2 Botev, Z.; Ridder, A. (2017). "Variance Reduction". Wiley StatsRef: Statistics Reference Online: 1--6. doi:10.1002/9781118445112.stat07975.

References

Ross, Sheldon M. (2002) Simulation 3rd edition ISBN 978-0-12-598053-1
Averill M. Law & W. David Kelton (2000), Simulation Modeling and Analysis, 3rd edition. ISBN 0-07-116537-1
S. P. Meyn (2007) Control Techniques for Complex Networks, Cambridge University Press. ISBN 978-0-521-88441-9. Downloadable draft (Section 11.4: Control variates and shadow functions)

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[lemieux17-1] Lemieux, C. (2017). "Control Variates". Wiley StatsRef: Statistics Reference Online: 1--8. doi:10.1002/9781118445112.stat07947.

[2] Glasserman, P. (2004). Monte Carlo Methods in Financial Engineering. New York: Springer. ISBN 0-387-00451-3 (p. 185)

[varred17-3] 1 2 Botev, Z.; Ridder, A. (2017). "Variance Reduction". Wiley StatsRef: Statistics Reference Online: 1--6. doi:10.1002/9781118445112.stat07975.

Control variates

Underlying principle

Example

See also

Notes

References