Exploiting the errors: A simple approach for improved volatility forecasting

Abstract

We propose a new family of easy-to-implement realized volatility based forecasting models.The models exploit the asymptotic theory for high-frequency realized volatility estimation to improve the accuracy of the forecasts. By allowing the parameters of the models to vary explicitly with the (estimated) degree of measurement error, the models exhibit stronger persistence, and in turn generate more responsive forecasts, when the measurement error is relatively low. Implementing the new class of models for the S&P500 equity index and the individual constituents of the Dow Jones Industrial Average, we document significant improvements in the accuracy of the resulting forecasts compared to the forecasts from some of the most popular existing models that implicitly ignore the temporal variation in the magnitude of the realized volatility measurement errors.

Modeling and forecasting (un)reliable realized covariances for more reliable financial decisions

“…Bollerslev, Patton, and Quaedvlieg (2016) proposed a simple modification to accommodate this in the context of univariate volatility models, by allowing θ 1 in (6) to depend on an estimate for the measurement error variance:…”

“…However, the magnitude of the errors generally decrease with the horizon, and the difficulties in accurately estimating the integrated quarticity may easily outweigh the benefits of adjusting the weekly and monthly coefficients; Bollerslev, Patton, and Quaedvlieg (2016) provides an analysis of the corresponding tradeoffs in the univariate context. 8 separately.…”

“…In the HARQ-DRD model analyzed below, we rely on the univariate HARQ model of Bollerslev, Patton, and Quaedvlieg (2016), defined analogously to the multivariate model in equation (7) above, together with the scalar multivariate HAR in equation (6) for modeling the vectorized correlation matrix. It would of course be possible to also consider a HARQ specification for the correlations.…”

“…Extending the basic idea and simple univariate volatility models in Bollerslev, Patton, and Quaedvlieg (2016), we propose a new class of multivariate realized covariance based forecasting models that explicitly take into account the influence of measurement errors. To do so, we rely on the asymptotic distribution theory for high-frequency realized covariance estimation (BarndorffNielsen and Shephard, 2004;Barndorff-Nielsen, Hansen, Lunde, and Shephard, 2011) to help guide the magnitude of the time-varying attenuation in the parameters of the models: the parameters should be relatively large on days when the realized covariances are precisely estimated and more heavily attenuated on days when the measurement errors are large and the signals are weak.…”

Improving Volatility Risk Forecasting Accuracy in Industry Sector

“…Reference [14] examines and predicts aggregate volatility, and the researcher developed a model of individual returns to the study of volatility. Reference [15] examined a new class of volatility forecasting models and they noticed significant improvements in the accuracy of the resulting forecasts compared to the forecasts from some of the most popular existing models. They found that the HARQ model is slightly more subtle while the HAR places greater weight on the weekly and monthly lags.…”