  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.
