Large and moderate deviations for importance sampling in the Heston model
研究了随机波动率模型中通过重要性抽样进行方差缩减的问题，利用大偏差和中偏差理论推导最优测度变换，并在Heston模型中得到闭式解，数值分析验证了方差缩减效果。
Abstract We provide a detailed importance sampling analysis for variance reduction in stochastic volatility models. The optimal change of measure is obtained using a variety of results from large and moderate deviations: small-time, large-time, small-noise. Specialising the results to the Heston model, we derive many closed-form solutions, making the whole approach easy to implement. We support our theoretical results with a detailed numerical analysis of the variance reduction gains.