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Forecasting Beta Using Conditional Heteroskedastic Models
By Andrew Bentley
Conventional measurements of equity return volatility rely on the asset’s previous day closing price to infer the current level of volatility and fail to incorporate information concerning intraday influntuctuations. Realized measures of volatility, such as the realized variance, are able to integrate intraday information by utilizing high-frequency data to form a very accurate measure of the asset’s return volatility. These measures can be used in parallel with the traditional definition of the Capital Asset Pricing Model (CAPM) beta to better predict the time-varying systematic risk of an asset. In this analysis, realized measures were added to the General Autoregressive Conditional Heteroskedastic (GARCH) framework to form a predictive model of beta that can quickly respond to rapid changes in the level of volatility. The ndings suggest that this predictive beta is better able to explain the stylized characteristics of beta and is a more accurate forecast of the realized beta than the GARCH model or the benchmark Autoregressive Moving-Average (ARMA) model used as a comparison.
JEL Codes: C0, C3, C03, C32, C53, C58 | Tagged: Beta, GARCH, GARCHX, High-Frequency Data, Realized Varience
Time-Varying Beta: The Heterogeneous Autoregressive Beta Model
By Kunal Jain
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 micro-structure 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 Auto-regressive 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 micro-structure noise in high frequency data when compared to the existing benchmarks.
Advisor: George Tauchen | JEL Codes: C01, C13, C22, C29, C58 | Tagged: Beta, Financial Markets, Heterogeneous Autoregressive, Persistence
