Efficient Importance Sampling under Heston Model: Short Maturity and Deep Out-of-the-Money Options

TL;DR

This paper develops asymptotically optimal importance sampling schemes for pricing European call options under the Heston model, effectively reducing variance in short maturity and deep out-of-the-money regimes by leveraging large deviation principles.

Contribution

It introduces novel importance sampling strategies tailored for the Heston model in two rare-event regimes, achieving asymptotic optimality and significant variance reduction.

Findings

Achieves logarithmic efficiency in short-maturity regime.

Introduces a slow mean-reversion scaling for deep OTM options.

Demonstrates variance reduction factors exceeding several orders of magnitude.

Abstract

This paper investigates asymptotically optimal importance sampling (IS) schemes for pricing European call options under the Heston stochastic volatility model. We focus on two distinct rare-event regimes where standard Monte Carlo methods suffer from significant variance deterioration: the limit as maturity approaches zero and the limit as the strike price tends to infinity. Leveraging the large deviation principle (LDP), we design a state-dependent change of measure derived from the asymptotic behavior of the log-price cumulant generating functions. In the short-maturity regime, we rigorously prove that our proposed IS drift, inspired by the variational characterization of the rate function, achieves logarithmic efficiency (asymptotic optimality) by minimizing the decay rate of the second moment of the estimator. In the deep OTM regime, we introduce a novel slow mean-reversion scaling for the variance process, where the mean-reversion speed scales as the inverse square of the small-noise parameter (defined as the reciprocal of the log-moneyness). We establish that under this specific scaling, the variance process contributes non-trivially to the large deviation rate function, requiring a specialized Riccati analysis to verify optimality. Numerical experiments demonstrate that the proposed method yields substantial variance reduction--characterized by factors exceeding several orders of magnitude--compared to standard estimators in both asymptotic regimes.