Modeling tick-by-tick realized correlations

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractA tree-structured heterogeneous autoregressive (tree-HAR) process is proposed as a simple and parsimonious model for the estimation and prediction of tick-by-tick realized correlations. The model can account for different time and other relevant predictors' dependent regime shifts in the conditional mean dynamics of the realized correlation series. Testing the model on S&P 500 Futures and 30-year Treasury Bond Futures realized correlations, empirical evidence that the tree-HAR model reaches a good compromise between simplicity and flexibility is provided. The model yields accurate single-and multi-step out-ofsample forecasts. Such forecasts are also better than those obtained from other standard approaches, in particular when the final goal is multi-period forecasting.

Multivariate realized volatility forecasts of agricultural commodity futures

“…The HAR model includes the daily, weekly, and monthly volatility components as predictors based on the heterogeneous market hypothesis and the HARCH model of Müller et al (). Audrino and Corsi () suggest that the HAR processes improve the in‐sample fit and out‐of‐sample forecast performances in modeling the variance and covariance. Bauer and Vorkink () propose a multivariate matrix logarithm HAR model to forecast the covariance matrix of size‐sorted stock returns.…”

Smooth Transition Patterns in the Realized Stock Bond Correlation

“…Aslanidis and Christiansen (2010) document large di¤erences between stock-bond correlations based on high and low frequency data. The recent studies by Audrino and Corsi (2010) and Christiansen and Ranaldo (2007) use high frequency data in the analysis of the stock-bond correlation. The …rst paper adopts a heterogeneous autoregressive model and shows that its out-of-sample forecasts are more accurate than those of standard autoregressive models.…”

Realized EquiCorrelation: a bird’s-eye view of financial stress on equity markets

“…Motivated by this success, Audrino and Corsi (2010) and Barndorff-Nielsen et al (2011) -among others -have demonstrated that the use of high-frequency data provides superior estimates of the level of latent covariance between assets, relative to estimates from daily returns.…”