Semiparametric GARCH via Bayesian model averaging

TL;DR

This paper introduces a flexible semiparametric GARCH model that estimates volatility by allowing the news impact function to be any smooth shape, using Bayesian model averaging and MCMC, improving adaptability to market changes.

Contribution

It develops a novel semiparametric volatility model with a Bayesian approach to estimate the news impact function, enhancing flexibility over traditional parametric models.

Findings

The model accurately learns the shape of the news impact function from data.

Different asset types have significantly different news impact functions.

Assets of the same type share similar impact function shapes.

Abstract

As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assumptions on how prices change over time. Most volatility models impose a particular parametric functional form that relates an observed price change to a volatility forecast (news impact function). We propose a new class of functional coefficient semiparametric volatility models where the news impact function is allowed to be any smooth function, and study its ability to estimate volatilities compared to the well known parametric proposals, in both a simulation study and an empirical study with real financial data. We estimate the news impact function using a Bayesian model averaging approach, implemented via a carefully developed Markov chain Monte Carlo (MCMC) sampling algorithm. Using simulations we show that our flexible semiparametric model is able to learn the shape of the news impact function from the observed data. When applied to real financial time series, our new model suggests that the news impact functions are significantly different in shapes for different asset types, but are similar for the assets of the same type.