Realized volatility forecast with the Bayesian random compressed multivariate HAR model

Jiawen Luo and Langnan Chen

International Journal of Forecasting, 2020, vol. 36, issue 3, 781-799

Abstract: We develop a Bayesian random compressed multivariate heterogeneous autoregressive (BRC-MHAR) model to forecast the realized covariance matrices of stock returns. The proposed model randomly compresses the predictors and reduces the number of parameters. We also construct several competing multivariate volatility models with the alternative shrinkage methods to compress the parameter’s dimensions. We compare the forecast performances of the proposed models with the competing models based on both statistical and economic evaluations. The results of statistical evaluation suggest that the BRC-MHAR models have the better forecast precision than the competing models for the short-term horizon. The results of economic evaluation suggest that the BRC-MHAR models are superior to the competing models in terms of the average return, the Shape ratio and the economic value.

Keywords: Realized volatility forecast; Bayesian random compressed; Multivariate HAR model; Forecast precision evaluations; Economic evaluations (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:3:p:781-799

DOI: 10.1016/j.ijforecast.2019.09.002

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