# Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models

## TL;DR

This paper introduces an interweaving strategy that significantly enhances MCMC sampling efficiency for stochastic volatility models, enabling more robust Bayesian inference across diverse parameter settings.

## Contribution

It applies the ancillarity-sufficiency interweaving strategy to stochastic volatility models, improving sampling efficiency and allowing inference in previously infeasible parameter regimes.

## Key findings

- Enhanced MCMC sampling efficiency across all parameters.
- Effective inference without pre-selecting parameterizations.
- Overcomes limitations of centered and non-centered models.

## Abstract

Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior utilizing the standard centered parameterization break down when the volatility of volatility parameter in the latent state equation is small, non-centered versions of the model show deficiencies for highly persistent latent variable series. The novel approach of ancillarity-sufficiency interweaving has recently been shown to aid in overcoming these issues for a broad class of multilevel models. In this paper, we demonstrate how such an interweaving strategy can be applied to stochastic volatility models in order to greatly improve sampling efficiency for all parameters and throughout the entire parameter range. Moreover, this method of "combining best of different worlds" allows for inference for parameter constellations that have previously been infeasible to estimate without the need to select a particular parameterization beforehand.