BOYLE, P., M. BROADIE, and P. GLASSERMAN (1997): Simulation Methods for Security Pricing, J.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Carlo simulation grows linearly with the number of dimensions, whereas lattice methods and numerical PDEs have time complexities which grow exponentially. A drawback of Monte Carlo simulation is that it cannot be used to price options which are continuously earlyexercisable (American options) [7]. Lattice methods are perhaps the most widely used numerical method in finance. Although lattice methods are often viewed as a distinct technique in the finance literature [18, 36] they are in fact simple explicit finite difference schemes [35] The popularity of lattice methods can be in part ....

P. P. Boyle, M. Broadie, and P. Glasserman. Simulation Methods for Security Pricing. Journal of Economic Dynamics and Control, 21:1267--1321, 1997.

....of simulation have motivated substantial interest in the financial industry in methods for increased efficiency, as evidenced in part by the growth in commercial software offering enhancements to Monte Carlo and a large number of publications on the subject in the practitioner literature. See Boyle, Broadie, and Glasserman 1997 for an overview of simulation methods for option pricing. If a particular derivative security is to be valued just once or only rarely, the investment required to value it efficiently may not be justified. If, however, a pricing routine is to be run often because a firm regularly quotes prices ....

....and Wang (1997) Heidelberger (1995) and Shahabuddin (1995) for numerous references and discussions of the literature, and see Chen et al. 1993) for the Gaussian case in particular. In the option pricing setting, Reider (1993) uses importance sampling to price deep out of the money calls, and Boyle et al. 1997) give an application to pricing knock in options far from the barrier. In the rare event setting, the integrand is usually the indicator of a rare set and importance sampling is used to make the set less rare. In our setting, the set on which a positive payoff occurs need not be rare at all, and ....

BOYLE, P., M. BROADIE, and P. GLASSERMAN (1997): Simulation Methods for Security Pricing, J.

....variance; more generally, if F is close to linear, this choice of density can be expected to eliminate much of the variance in the original estimator. This gives an alternative interpretation of the method above for choosing . See GHS for a more extensive analysis and discussion. See Boyle et al. [1], Newton [9] and Schoenmakers and Heemink [11] for other approaches to importance sampling in option pricing. 2.3 Stratied Sampling Although importance sampling by itself can, in some cases, yield substantial variance reduction (particularly in pricing options for which the probability of a ....

Boyle, P., M. Broadie, and P. Glasserman, \Simulation Methods for Security Pricing," J. Economic Dynamics and Control, 21, 1267-1321, 1997.

....that makes the simulated bond prices match true bond prices over finitely many paths. We then examine the impact of this adjustment on caplet prices. A similar adjustment is proposed in a setting without discretization error by Duan and Simonato [6] see also the discussion in Boyle et al. [4]. Similar adjustments appear to be in widespread use in practice. We detail the method in the case of the discretization X n in the terminal measure. Let X (k ) n (ih) k =1, K , denote the values of X n simulated at the i th step of K independent replications. Because each X n is a ....

Boyle, P., Broadie, M., Glasserman, P.: Simulation Methods for Security Pricing. J. Econ. Dynamics Control 21, 1267--1321 (1997)

....substantial interest in the financial industry in methods for increased efficiency, as evidenced in part by the growth in commercial software offering enhancements to Monte Carlo and a large number of publications on the subject in the practitioner literature. See Boyle, Broadie, and Glasserman [3] for an overview of simulation methods for option pricing. If a particular derivative security is to be valued just once or only rarely, the investment required to value it efficiently may not be justified. If, however, a pricing routine is to be run often because a firm regularly quotes ....

....Heidelberger [14] Shahabuddin [31] for numerous references and discussions of the literature, and see Chen et al. 5] for the Gaussian case in particular. In the option pricing setting, Reider [26] uses importance sampling to price deep out of the money calls, and Boyle, Broadie, and Glasserman [3] give an application to pricing knock in options far from the barrier. In the rare event setting, the integrand is usually the indicator of a rare set and importance sampling is used to make the set less rare. In our setting, the set on which a positive payoff occurs need not be rare at all, and ....

Boyle, P., M. Broadie, and P. Glasserman, "Simulation Methods for Security Pricing," J. Economic Dynamics and Control 21, 1267-1321, 1997.

....variance; more generally, if F is close to linear, this choice of density can be expected to eliminate much of the variance in the original estimator. This gives an alternative interpretation of the method above for choosing . See GHS for a more extensive analysis and discussion. See Boyle et al. [1], Newton [9] and Schoenmakers and Heemink [11] for other approaches to importance sampling in option pricing. 2.3 Stratified Sampling Although importance sampling by itself can, in some cases, yield substantial variance reduction (particularly in pricing options for which the probability of a ....

Boyle, P., M. Broadie, and P. Glasserman, "Simulation Methods for Security Pricing," J. Economic Dynamics and Control, 21, 1267-1321, 1997.

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