Adaptive calibration of Heston Model using PCRLB based switching Filter

TL;DR

This paper introduces an adaptive Bayesian filtering framework using PCRLB and switching strategies to improve Heston model parameter estimation and volatility prediction in financial markets.

Contribution

It proposes a novel adaptive filtering approach combining PCRLB-based performance measures with switching filters for Heston model parameter estimation.

Findings

Effective volatility estimation on S&P 500 and NSE data

Outperforms traditional measures like VIX and historical volatility

Demonstrates adaptability to changing market conditions

Abstract

Stochastic volatility models have existed in Option pricing theory ever since the crash of 1987 which violated the Black-Scholes model assumption of constant volatility. Heston model is one such stochastic volatility model that is widely used for volatility estimation and option pricing. In this paper, we design a novel method to estimate parameters of Heston model under state-space representation using Bayesian filtering theory and Posterior Cramer-Rao Lower Bound (PCRLB), integrating it with Normal Maximum Likelihood Estimation (NMLE) proposed in [1]. Several Bayesian filters like Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF) are used for latent state and parameter estimation. We employ a switching strategy proposed in [2] for adaptive state estimation of the non-linear, discrete-time state-space model (SSM) like Heston model. We use a particle filter approximated PCRLB [3] based performance measure to judge the best filter at each time step. We test our proposed framework on pricing data from S&P 500 and NSE Index, estimating the underlying volatility and parameters from the index. Our proposed method is compared with the VIX measure and historical volatility for both the indexes. The results indicate an effective framework for estimating volatility adaptively with changing market dynamics.