A Kalman particle filter for online parameter estimation with applications to affine models

TL;DR

This paper introduces a semi-recursive Kalman particle filter algorithm combining Kalman and particle filters for efficient online estimation of static parameters and states in continuous-time models, with proven convergence and practical applications.

Contribution

The paper proposes a novel two-layer Kalman particle filter that improves convergence speed and computational efficiency for online parameter estimation in state space models.

Findings

Proven convergence of the algorithm to true posterior distributions.

Demonstrated effectiveness in affine interest rate models with stochastic volatility.

Reduced computational time compared to traditional nested particle filters.

Abstract

In this paper we address the problem of estimating the posterior distribution of the static parameters of a continuous time state space model with discrete time observations by an algorithm that combines the Kalman filter and a particle filter. The proposed algorithm is semi-recursive and has a two layer structure, in which the outer layer provides the estimation of the posterior distribution of the unknown parameters and the inner layer provides the estimation of the posterior distribution of the state variables. This algorithm has a similar structure as the so-called recursive nested particle filter, but unlike the latter filter, in which both layers use a particle filter, this proposed algorithm introduces a dynamic kernel to sample the parameter particles in the outer layer to obtain a higher convergence speed. Moreover, this algorithm also implements the Kalman filter in the inner layer to reduce the computational time. This algorithm can also be used to estimate the parameters that suddenly change value. We prove that, for a state space model with a certain structure, the estimated posterior distribution of the unknown parameters and the state variables converge to the actual distribution in $L_p$ with rate of order $\mathcal{O}(N^{-\frac{1}{2}}+\delta^{\frac{1}{2}})$, where $N$ is the number of particles for the parameters in the outer layer and $\delta$ is the maximum time step between two consecutive observations. We present numerical results of the implementation of this algorithm, in particularly we implement this algorithm for affine interest models, possibly with stochastic volatility, although the algorithm can be applied to a much broader class of models.