Unbiased simulation of distributions with explicitly known integral transforms
نویسندگان
چکیده
In this paper, we propose an importance-sampling based method to obtain an unbiased simulator to evaluate expectations involving random variables whose probability density functions are unknown while their Fourier transforms have an explicit form. We give a general principle about how to choose appropriate importance samplers under different models. Compared with the existing methods, our method avoids time-consuming numerical Fourier inversion and can be applied effectively to high dimensional financial applications such as option pricing and sensitivity estimation under Heston stochastic volatility model, high dimensional affine jumpdiffusion model, and various Levy processes.
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