37 research outputs found
Conditional sampling for barrier option pricing under the LT method
We develop a conditional sampling scheme for pricing knock-out barrier
options under the Linear Transformations (LT) algorithm from Imai and Tan
(2006). We compare our new method to an existing conditional Monte Carlo scheme
from Glasserman and Staum (2001), and show that a substantial variance
reduction is achieved. We extend the method to allow pricing knock-in barrier
options and introduce a root-finding method to obtain a further variance
reduction. The effectiveness of the new method is supported by numerical
results
Smoothing the payoff for efficient computation of Basket option prices
We consider the problem of pricing basket options in a multivariate Black
Scholes or Variance Gamma model. From a numerical point of view, pricing such
options corresponds to moderate and high dimensional numerical integration
problems with non-smooth integrands. Due to this lack of regularity, higher
order numerical integration techniques may not be directly available, requiring
the use of methods like Monte Carlo specifically designed to work for
non-regular problems. We propose to use the inherent smoothing property of the
density of the underlying in the above models to mollify the payoff function by
means of an exact conditional expectation. The resulting conditional
expectation is unbiased and yields a smooth integrand, which is amenable to the
efficient use of adaptive sparse grid cubature. Numerical examples indicate
that the high-order method may perform orders of magnitude faster compared to
Monte Carlo or Quasi Monte Carlo in dimensions up to 35
Density Estimation by Monte Carlo and Quasi-Monte Carlo
Estimating the density of a continuous random variable has been studied extensively in statistics, in the setting where independent observations of are given a priori and one wishes to estimate the density from that. Popular methods include histograms and kernel density estimators. In this review paper, we are interested instead in the situation where the observations are generated by Monte Carlo simulation from a model. Then, one can take advantage of variance reduction methods such as stratification, conditional Monte Carlo, and randomized quasi-Monte Carlo (RQMC), and obtain a more accurate density estimator than with standard Monte Carlo for a given computing budget. We discuss several ways of doing this, proposed in recent papers, with a focus on methods that exploit RQMC. A first idea is to directly combine RQMC with a standard kernel density estimator. Another one is to adapt a simulation-based derivative estimation method such as smoothed perturbation analysis or the likelihood ratio method to obtain a continuous estimator of the cumulative density function (CDF), whose derivative is an unbiased estimator of the density. This can then be combined with RQMC. We summarize recent theoretical results with these approaches and give numerical illustrations of how they improve the convergence of the mean square integrated error.NSERC Discovery Grant, IVADO Grant, ERDF, ESF, EXP. 2019/0043