Inference of Volatility Models Using Triple Information Sources: An Approximate Bayesian Computation Approach
Speaker: Associate Professor Ole Maneesoonthorn (Melbourne Business School, University of Melbourne)
Time: 2-3pm 5 August 2022
Abstract: This paper utilizes three sources of information, daily returns, high
frequency data and market option prices, to conduct inference about stochastic
volatility models. The inferential method of choice is the approximate Bayesian
computation (ABC) method, which allows us to construct posterior distributions of the
model unknowns from data summaries without assuming a large dimensional measurement
model from the three information sources. We employ ABC cut posteriors to dissect the
information sources in posterior inference and show that it significantly reduces the
computational burden compared to conventional posterior sampling. The benefit of
utilizing multiple information sources in inference is explored in the context of
predictive performance of financial returns.Zoom link at https://uni-sydney.zoom.us/j/84034742477