Unbiased estimators for the Heston model with stochastic interest rates
@article{Zheng2023UnbiasedEF, title={Unbiased estimators for the Heston model with stochastic interest rates}, author={Chao Zheng and Jiangtao Pan}, journal={ArXiv}, year={2023}, volume={abs/2301.12072} }
We combine the unbiased estimators in Rhee and Glynn (Operations Research: 63(5), 1026-1043, 2015) and the Heston model with stochastic interest rates. Specif-ically, we first develop a semi-exact log-Euler scheme for the Heston model with stochastic interest rates, and then, under mild assumptions, we show that the convergence rate in L 2 norm is O ( h ), where h is the step size. The result applies to a large class of models, such as the Heston-Hull-While model, the Heston-CIR model and the…
One Citation
Optimal distributions for randomized unbiased estimators with an infinite horizon and an adaptive algorithm
- Mathematics
- 2023
The randomized unbiased estimators of Rhee and Glynn (Operations Research:63(5), 1026-1043, 2015) can be highly efficient at approximating expectations of path functionals associated with stochastic…
22 References
On the Heston Model with Stochastic Interest Rates
- MathematicsSIAM J. Financial Math.
- 2011
Two different approximations of the Heston hybrid model with stochastic interest rates driven by Hull-White or Cox-Ingersoll-Ross processes are presented in order to obtain the characteristic functions.
The Weak Convergence Rate of Two Semi-Exact Discretization Schemes for the Heston Model
- Mathematics, Economics
- 2021
Inspired by the article Weak Convergence Rate of a Time-Discrete Scheme for the Heston Stochastic Volatility Model, Chao Zheng, SIAM Journal on Numerical Analysis 2017, 55:3, 1243–1263, we studied…
Weak Convergence Rate of a Time-Discrete Scheme for the Heston Stochastic Volatility Model
- MathematicsSIAM J. Numer. Anal.
- 2017
This paper derives the weak convergence rate of a time-discrete scheme for the Heston stochastic volatility model, which employs the Stochastic trapezoidal rule to discretize the logarithmic asset process, provided that the variance process is simulated exactly.
Multilevel Monte Carlo Simulation for the Heston Stochastic Volatility Model
- Mathematics
- 2016
We combine the Multi-level Monte Carlo (MLMC) method with the numerical scheme for the Heston model that simulates the variance process exactly and applies the stochastic trapezoidal rule to…
Convergence of an Euler Scheme for a Hybrid Stochastic-Local Volatility Model with Stochastic Rates in Foreign Exchange Markets
- Mathematics, EconomicsSIAM J. Financial Math.
- 2018
The strong convergence of the exchange rate approximations is proved and the convergence of Monte Carlo estimators for a number of vanilla and path-dependent options are deduced.
Pricing Interest-Rate-Derivative Securities
- Economics
- 1990
This article shows that the one-state-variable interest-rate models of Vasicek (1977) and Cox, Ingersoll, and Ross (1985b) can be extended so that they are consistent with both the current term…
Unbiased Estimation with Square Root Convergence for SDE Models
- Mathematics, Computer ScienceOper. Res.
- 2015
This work introduces a simple randomization idea for creating unbiased estimators in such a setting based on a sequence of approximations for computing expectations of path functionals associated with stochastic differential equations (SDEs).
Generic pricing of FX, inflation and stock options under stochastic interest rates and stochastic volatility
- Economics
- 2008
We consider the pricing of FX, inflation and stock options under stochastic interest rates and stochastic volatility, for which we use a generic multi-currency framework. We allow for a general…
Rates of Convergence and CLTs for Subcanonical Debiased MLMC
- Mathematics
- 2016
In constructing debiased multi-level Monte Carlo (MLMC) estimators, one must choose a randomization distribution. In some algorithmic contexts, an optimal choice for the randomization distribution…
A Theory for the Term Structure of Interest Rates
- Computer Science
- 2004
The discretised theoretical distributions matching the empirical data from the Federal Reserve System are deduced from aDiscretised seed which enjoys remarkable scaling laws and may be used to develop new methods for the computation of the value-at-risk and fixed-income derivative pricing.