Control Variates For Variance Reduction In Indirect Inference: Interest Rate Models In Continuous Time  

Oleh:

Calzolari, Giorgio ; Iorio, Francesca Di ; Fiorentini, Gabriele

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The Econometrics Journal vol. 1 no. 1 (1998), page 100-112.
Topik: Efficient Monte Carlo; Variance reduction techniques; Control variates; Indirect inference; Euler discretization; Short-term interest rate; Stochastic differential equation.
Fulltext: 100.pdf (160.02KB)

Isi artikel

Simulation estimators, such as indirect inference or simulated maximum likelihood, are successfully employed for estimating stochastic differential equations. They adjust for the bias (inconsistency) caused by discretization of the underlying stochastic process, which is in continuous time. The price to be paid is an increased variance of the estimated parameters. The variance suffers from an additional component, which depends on the stochastic simulation involved in the estimation procedure. To reduce this undesirable effect, one could increase the number of simulations (or the length of each simulation) and thus the computational cost. Alternatively, this paper shows howvariance reduction can be achieved, at virtually no additional computational cost, by use of control variates. The Ornstein–Uhlenbeck process, used by Vasicek to model the short-term interest rate in continuous time, and the square-root process, used by Cox, Ingersoll and Ross, are explicitly considered and experimented with. Monte Carlo experiments show a global efficiency gain of almost 50% over the simple indirect estimator.

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