Diaa Noureldin
Neil Shephard
Kevin Sheppard

multivariate high-frequency-based volatility (heavy) models (replication data)

This paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences from multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations.

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Suggested Citation

Noureldin, Diaa; Shephard, Neil; Sheppard, Kevin (2012): Multivariate high-frequency-based volatility (HEAVY) models (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022320.0729962017