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

Abstract

We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less) reliable. Applying the new procedures in the construction of minimum variance and minimum tracking error portfolios results in reduced turnover and statistically superior positions compared to existing procedures. Translating these statistical improvements into economic gains, we find that under empirically realistic assumptions a risk-averse investor would be willing to pay up to 170 basis points per year to shift to using the new class of forecasting models.

Realized volatility forecasting: Robustness to measurement errors

“…As a matter of fact, Cipollini et al (2013) show that there is a definite improvement in the forecasting performance of the (multivariate) modeling of realized volatility over realized variance. Finally, the HARQ model has been recently extended to the multivariate case by Bollerslev et al (2018). The challenge would be to develop a similar exercise, dealing with multivariate MEM (Cipollini et al, 2013;Taylor and Xu, 2017) with and without the presence of Markov Switching dynamics in the time-varying covariance matrix (see, for example, Otranto, 2010).…”

Multivariate realized volatility forecasts of agricultural commodity futures

“…Čech and Baruník (2017) construct a generalized HAR (GHAR) model by incorporating the Cholesky factors of covariance matrix into a seemingly unrelated model structure and compare the forecast performances of the proposed model with other multivariate models in the literature. Bollerslev, Patton, and Quaedvlieg (2018) propose a scalar version of the MHAR model and incorporate the effect of time-varying attenuation biases into the multivariate HAR model to achieve greater economic gains for the covariance forecasts. The above models make full use of high-frequency data and yield a better forecast performance than do the GARCH-type multivariate volatility models with low-frequency data.The extended forms of a multivariate HAR model can be considered a restricted vector autoregression (VAR) model that accommodates the long-memory property of volatility.…”

Fractional Integration and Fat Tails for Realized Covariance Kernels and Returns

“…where h stands for the forecast horizon. As indicated by Bollerslev et al (2016), direct forecasts might be more adequate than iterative forecasts due to the possibility of model misspecification.…”

“…Similar results hold for the GAS tF model. We followBollerslev et al (2016) by considering direct forecasts in case of the the multivariate HAR model. These forecasts are obtained by running the following regression:…”