Abstract:
Conventional models of volatility estimation do not capture the persistence in high-frequency market data and are not able to limit the impact of market microstructure noise present at very finely sampled intervals. In an attempt to incorporate these two elements, we use the beta-metric as a proxy for equity-specific volatility and use finely sampled time-varying conditional forecasts estimated using the Heterogeneous Autoregressive framework to form a predictive beta model. The findings suggest that this predictive beta is better able to capture persistence in financial data and limit the effect of microstructure noise in high-frequency data when compared to the existing benchmarks.
