Improved Forecasting of Realized Variance Measures

21 Pages Posted: 22 Jul 2016

See all articles by Jeremias Bekierman

Jeremias Bekierman

University of Cologne - Department of Econometrics and Statistics

Hans Manner

Universitat zu Koln

Date Written: July 21, 2016

Abstract

We consider the problem of forecasting realized variance measures. These measures are highly persistent, but also noisy estimates of the underlying integrated variance. Recently, Bollerslev, Patton and Quaedvlieg (2016, Journal of Econometrics, 192, 1-18) exploited this fact to extend the commonly used Heterogeneous Autoregressive (HAR) by letting the model parameters vary over time depending on estimated measurement errors. We propose an alternative specification that allows the autoregressive parameter of the HAR model for volatilities to be driven by a latent Gaussian autoregressive process that may depend on the estimated measurement error. The model is estimated using the Kalman filter. Our analysis considers realized volatilities of 40 stocks from the S&P 500 for three different observation frequencies. Our preferred model provides a better model fit and generates superior forecasts. It consistently outperforms the competing models in terms of different loss functions and for various subsamples of the forecasting period.

Keywords: volatility forecasting, realized volatility, measurement error, state space model

JEL Classification: C32, C53, C58, G17

Suggested Citation

Bekierman, Jeremias and Manner, Hans, Improved Forecasting of Realized Variance Measures (July 21, 2016). Available at SSRN: https://ssrn.com/abstract=2812586 or http://dx.doi.org/10.2139/ssrn.2812586

Jeremias Bekierman (Contact Author)

University of Cologne - Department of Econometrics and Statistics ( email )

Albertus-Magnus-Platz
Cologne, DE 50923
Germany

Hans Manner

Universitat zu Koln ( email )

Albertus-Magnus-Platz
Koln, 50923
Germany

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