# Generating Random Numbers Variance Reduction Quasi-Monte Carlo. Simulation Methods. Leonid Kogan. MIT, Sloan , Fall 2010

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1 Simulation Methods Leonid Kogan MIT, Sloan , Fall 2010 c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

2 Outline 1 Generating Random Numbers 2 Variance Reduction 3 Quasi-Monte Carlo c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

3 Overview Simulation methods (Monte Carlo) can be used for option pricing, risk management, econometrics, etc. Naive Monte Carlo may be too slow in some practical situations. Many special techniques for variance reduction: antithetic variables, control variates, stratified sampling, importance sampling, etc. Recent developments: Quasi-Monte Carlo (low discrepancy sequences). c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

4 The Basic Problem Consider the basic problem of computing an expectation θ = E[f (X )], X pdf (X ) Monte Carlo simulation approach specifies generating N independent draws from the distribution pdf (X ), X 1, X 2,..., X N, and approximating 1 N E[f (X)] θ N f (X i ) N By Law of Large Numbers, the approximation θ N converges to the true value as N increases to infinity. Monte Carlo estimate θ N is unbiased: E θ N = θ By Central Limit Theorem, i=1 θ N θ N σ N(0, 1), σ 2 = Var[f (X )] c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

5 Outline 1 Generating Random Numbers 2 Variance Reduction 3 Quasi-Monte Carlo c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

6 Generating Random Numbers Pseudo random number generators produce deterministic sequences of numbers that appear stochastic, and match closely the desired probability distribution. For some standard distributions, e.g., uniform and Normal, MATLAB provides built-in random number generators. Sometimes it is necessary to simulate from other distributions, not covered by the standard software. Then apply one of the basic methods for generating random variables from a specified distribution. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

7 The Inverse Transform Method Consider a random variable X with a continuous, strictly increasing CDF function F (x). We can simulate X according to This works, because X = F 1 (U), U Unif [0, 1] Prob(X x) = Prob(F 1 (U) x) = Prob(U F (x)) = F (x) If F (x) has jumps, or flat sections, generalize the above rule to X = min (x : F (x) U) c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

8 The Inverse Transform Method Example: Exponential Distribution Consider an exponentially-distributed random variable, characterized by a CDF F (x) = 1 e x/θ Exponential distributions often arise in credit models. Compute F 1 (u) u = 1 e x/θ X = θ ln(1 U) θ ln U c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

9 The Inverse Transform Method Example: Discrete Distribution Consider a discrete random variable X with values Define cumulative probabilities c 1 < c 2 < < c n, Prob(X = c i ) = p i i F (c i ) = q i = p j j=1 Can simulate X as follows: Generate U Unif [0, 1]. Find K {1,..., n} such that q K 1 U q K. Set X = c K. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

10 The Acceptance-Rejection Method Generate samples with probability density f (x). The acceptance-rejection (A-R) method can be used for multivariate problems as well. Suppose we know how to generate samples from the distribution with pdf g(x), s.t., f (x) cg(x), c > 1 Follow the algorithm Generate X from the distribution g(x); Generate U from Unif [0, 1]; If U f (X )/[cg(x)], return X ; otherwise go to Step 1. Probability of acceptance on each attempt is 1/c. Want c close to 1. See the Appendix for derivations. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

11 The Acceptance-Rejection Method Example: Beta Distribution The beta density is 1 2 f (x) = B(α 1, β) x α 1 (1 x) β 1, 0 x 1 Assume α, β 1. Then f (x) has a maximum at (α 1)/(α + β 2). Define α 1 c = f α + β 2 and choose g(x) = 1. The A-R method becomes Generate independent U 1 and U 2 from Unif [0, 1] until cu 2 f (U 1 ); Return U 1. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

12 The Acceptance-Rejection Method Example: Beta Distribution c f (U 1 ) Accept U 1 if cu 2 in this range 0 U 1 1 Illustration of the acceptance-rejection method using uniformly distributed candidates. Image by MIT OpenCourseWare. Source: Glasserman 2004, Figure 2.8 c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

13 Outline 1 Generating Random Numbers 2 Variance Reduction 3 Quasi-Monte Carlo c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

14 Variance reduction Suppose we have simulated N independent draws from the distribution f (x). How accurate is our estimate of the expected value E[f (X )]? Using the CLT, construct the 100(1 α)% confidence interval σ θ N z1 α/2, θ σ N + z1 α/2, N N 2 σ 2 = 1 N f (X i ) θ N N i=1 where z 1 α/2 is the (1 α/2) percentile of the standard Normal distribution. For a fixed number of simulations N, the length of the interval is proportional to σ. The number of simulations required to achieve desired accuracy is proportional to the standard deviation of f (X i ), σ. The idea of variance reduction: replace the original problem with another simulation problem, with the same answer but smaller variance! c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

15 Antithetic Variates Attempt to reduce variance by introducing negative dependence between pairs of replications. Suppose want to estimate θ = E[f (X )], pdf (X ) = pdf ( X ) Note that f (X) + f ( X) X pdf (X) E = E[f (X)] 2 Define Y i = [f (X i ) + f ( X i )]/2 and compute θ AV N N 1 = Y i N i=1 Note that Y i are IID, and by CLT, θ AV N N E[f (X)] σ AV N(0, 1), σ AV = Var[Y i ] c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

16 Antithetic Variates When are they useful? Assume that the computational cost of computing Y i is roughly twice that of computing f (X i ). Antithetic variates are useful if Var[θ AV N ] < Var 1 2N 2N f (X i ) using the IID property of Y i, as well as X i, the above condition is equivalent to 1 Var[Y i ] < Var[f (X i )] 2 4Var[Y i ] =Var [f (X i ) + f ( X i )] = i=1 Var[f (X i )] + Var[f ( X i )] + 2Cov[f (X i ), f ( X i )] = 2Var[f (X i )] + 2Cov[f (X i ), f ( X i )] Antithetic variates reduce variance if Cov[f (X i ), f ( X i )] < 0 c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

17 Antithetic Variates When do they work best? Suppose that f is a monotonically increasing function. Then Cov[f (X ), f ( X )] < 0 and the antithetic variates reduce simulation variance. By how much? Define f 0 (X ) = f 0 (X ) and f 1 (X ) are uncorrelated: f (X) + f ( X) f (X) f ( X), f 1 (X) = E[f 0 (X )f 1 (X )] = E[f 2 (X ) f 2 ( X )] = 0 = E[f 0 (X )]E[f 1 (X )] 4 Conclude that Var[f (X )] = Var[f 0 (X )] + Var[f 1 (X )] If f (X ) is linear, Var[f 0 (X )] = 0, and antithetic variates eliminate all variance! Antithetics are more effective when f (X ) is close to linear. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

18 Control Variates The idea behind the control variates approach is to decompose the unknown expectation E[Y ] into the part known in closed form, and the part that needs to be estimated by simulation. There is no need to use simulation for the part known explicitly. The variance of the remainder may be much smaller than the variance of Y. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

19 Control Variates Suppose we want to estimate the expected value E[Y ]. On each replication, generate another variable, X i. Thus, draw a sequence of pairs (X i, Y i ). Assume that E[X ] is known. How can we use this information to reduce the variance of our estimate of E[Y ]? Define Y i (b) = Y i b(x i E[X ]) Note that E[Y i (b)] = E[Y i ], so 1 N in =1 Y i (b) is an unbiased estimator of E[Y ]. Can choose b to minimize variance of Y i (b): Var[Y i (b)] = Var[Y ] 2b Cov[X, Y ] + b 2 Var[X ] Optimal choice b is the OLS coefficient in regression of Y on X : b Cov[X, Y ] = Var[X ] c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

20 Control Variates The higher the R 2 in the regression of Y on X, the larger the variance reduction. Denoting the correlation between X and Y by ρ XY, find 1 N Var N i=1 Y i (b ) = 1 ρ 2 N XY 1 Var N i=1 Y i In practice, b is not known, but is easy to estimate using OLS. Two-stage approach: 1 2 Simulate N 0 pairs of (X i, Y i ) and use them to estimate b. Simulate N more pairs and estimate E[Y ] as 1 N Y i b (X i E[X]) N i=1 c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

21 Control Variates Example: Pricing a European Call Option Suppose we want to price a European call option using simulation. Assume constant interest rate r. Under the risk-neutral probability Q, we need to evaluate E Q 0 e rt max(0, S T K ) The stock price itself is a natural control variate. Assuming no dividends, E Q 0 e rt S T = S 0 Consider the Black-Scholes setting with r = 0.05, σ = 0.3, S 0 = 50, T = 0.25 Evaluate correlation between the option payoff and the stock price for different values of K. ρ 2 is the percentage of variance eliminated by the control variate. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

22 Control Variates Example: Pricing a European Call Option K ρ Source: Glasserman 2004, Table 4.1 For in-the-money call options, option payoff is highly correlated with the stock price, and significant variance reduction is possible. For out-of-the-money call options, correlation of option payoff with the stock price is low, and variance reduction is very modest. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

23 Control Variates Example: Pricing an Asian Call Option Suppose we want to price an Asian call option with the payoff J 1 max(0, S T K ), S T S(t j ), t 1 < t 2 < < t J T J j=1 A natural control variate is the discounted payoff of the European call option: X = e rt max(0, S T K ) Expectation of the control variate under Q is given by the Black-Scholes formula. Note that we may use multiple controls, e.g., option payoffs at multiple dates. When pricing look-back options, barrier options by simulation can use similar ideas. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

24 Control Variates Example: Stochastic Volatility Suppose we want to price a European call option in a model with stochastic volatility. Consider a discrete-time setting, with the stock price following S(t i+1 ) = S(t i ) exp (r σ(t i ) 2 Q /2)(t i+1 t i ) + σ(t i ) t i+1 t i ε i+1 ε Q i IID N(0, 1) σ(t i ) follows its own stochastic process. Along with S(t i ), simulate another stock price process σ 2 Q S(t i+1 ) = S(t i ) exp r (t i+1 t i ) + σ t i+1 t i ε 2 i+1 Pick σ close to a typical value of σ(t i ). Use the same sequence of Normal variables ε Q i for S (t i ) as for S(t i ). Can use the discounted payoff of the European call option on S as a control variate: expectation given by the Black-Scholes formula. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

25 Control Variates Example: Hedges as Control Variates Suppose, again, that we want to price the European call option on a stock with stochastic volatility. Let C(t, S t ) denote the price of a European call option with some constant volatility σ, given by the Black-Scholes formula. Construct the process for discounted gains from a discretely-rebalanced delta-hedge. The delta is based on the Black-Scholes model with constant volatility σ. The stock price follows the true stochastic-volatility dynamics. I 1 V (T ) = V (0) + C(t i, S(t i )) e rt i+1 S(t i+1 ) e rt i S(t i ), S(t i ) i=1 Under the risk-neutral probability Q, E Q 0 [V (T )] = V (0) (Check using iterated expectations) t I = T Can use V (T ) as a control variate. The better the discrete-time delta-hedge, the better the control variate that results. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

26 Hedges as Control Variates Example Consider a model of stock returns with stochastic volatility under the risk-neutral probability measure S((i + 1)Δ) = S(iΔ) exp (r v(iδ)/2)δ + v(iδ) Δε Q i+1 v((i + 1)Δ) = v(iδ) κ(v(iδ) v)δ + γ v(iδ)δu Q i+1 ε Q i IID N(0, 1), u Q i IID N(0, 1), Price a European call option under the parameters corr(ε Q i, uq i ) = ρ r = 0.05, T = 0.5, S 0 = 50, K = 55, Δ = 0.01 v 0 = 0.09, v = 0.09, κ = 2, ρ = 0.5, γ = 0.1, 0.2, 0.3, 0.4, 0.5 Perform 10,000 simulations to estimate the option price. Report the fraction of variance eliminated by the control variate. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

27 Hedges as Control Variates Example γ ρ Naive Monte Carlo C S.E.(C ) C S.E.(C ) Control variates c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

28 Outline 1 Generating Random Numbers 2 Variance Reduction 3 Quasi-Monte Carlo c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

29 Quasi-Monte Carlo Overview Quasi-Monte Carlo, or low-discrepancy methods present an alternative to Monte Carlo simulation. Instead of probability theory, QMC is based on number theory and algebra. 1 Consider a problem of integrating a function, 0 f (x) dx. Monte Carlo approach prescribes simulating N draws of a random variable X Unif [0, 1] and approximating 1 0 N 1 f (x) dx f (X i ) N QMC generates a deterministic sequence X i, and approximates 1 0 i=1 N 1 f (x) dx N i=1 f (X i ) Monte Carlo error declines with sample size as O(1/ N). QMC error declines almost as fast as O(1/N). c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

30 Quasi-Monte Carlo We focus on generating a d-dimensional sequence of low-discrepancy points filling a d-dimensional hypercube, [0, 1) d. QMC is a substitute for draws from d-dimensional uniform distribution. As discussed, all distributions can be obtained from Unif [0, 1] using the inverse transform method. There are many algorithms for producing low-discrepancy sequences. In financial applications, Sobol sequences have shown good performance. In practice, due to the nature of Sobol sequences, it is recommended to use N = 2 k (integer k) points in the sequence. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

31 Quasi-Monte Carlo Illustration 256 points from a 2-dimensional Sobol sequence MATLAB Code c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

32 Quasi-Monte Carlo Randomization Low-discrepancy sequence can be randomized to produce independent draws. 1 Each independent draw of N points yields an unbiased estimate of 0 f (x) dx. By using K independent draws, each containing N points, we can construct confidence intervals. Since randomizations are independent, standard Normal approximation can be used for confidence intervals. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

33 Quasi-Monte Carlo Randomization Two independent randomizations using 256 points from a 2-dimensional Sobol sequence MATLAB Code c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

34 Readings Campbell, Lo, MacKinlay, 1997, Section 9.4. Boyle, P., M. Broadie, P. Glasserman, 1997, Monte Carlo methods for security pricing, Journal of Economic Dynamics and Control, 21, Glasserman, P., 2004, Monte Carlo Methods in Financial Engineering, Springer, New York. Sections 2.2, 4.1, 4.2, 7.1, 7.2. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

35 Appendix Derivation of the Acceptance-Rejection Method Suppose the A-R algorithm generates Y. Y has the same distribution as X, conditional on f (X ) U cg(x ) Derive the distribution of Y. For any event A, f (X) f (X) Prob X A, U cg(x ) Prob(Y A) = Prob X A U = cg(x ) Prob U f (X) Note that Prob U f (X) X = f (X) and therefore cg(x ) cg(x) Prob U f (X) f (x) 1 = g(x) dx = cg(x ) cg(x) c cg(x ) Conclude that f (X) f (x) Prob(Y A) = c Prob X A, U = c g(x) dx = f (x) dx cg(x) A cg(x) A Since A is an arbitrary event, this verifies that Y has density f. c Leonid Kogan ( MIT, Sloan ) Simulation Methods , Fall / 35

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