Realized Real-Time GARCH: A Joint Model for Returns, Realized Measures and Current Information

Zhimin Wu () and Guanghui Cai
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Zhimin Wu: Hangzhou City University
Guanghui Cai: Hangzhou City University

Computational Economics, 2025, vol. 66, issue 4, No 22, 3359-3400

Abstract: Abstract Existing high-frequency-based volatility models usually regard the volatility process of financial returns as a function of the past daily-frequency and high-frequency information, and cannot take full advantage of the current information. This paper incorporates the Real-time information into the Realized GARCH model and proposes the Realized Real-time GARCH model. The new model retains the basic structure of the Realized GARCH model and considers the volatility process as a mixed product of past information and current information. Then some significant properties of the proposed model are discussed. Also, the variation of this model, the Realized Real-time GARCH-L model, is proposed to describe the leverage effect of the Real-time information. Our empirical results show that considering Real-time information makes the model perform better in terms of dealing with sudden jumps of volatility, improves the in-sample empirical fitting, and contributes to the improvements in forecasting multi-step ahead volatility, conditional density of returns and value at risk (VaR). Besides, the leverage effect of Real-time information also provides substantial improvements over the Realized Real-time GARCH model.

Keywords: Real-time information; Realized GARCH; Conditional density forecasts; Volatility forecasts; High-frequency data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10805-z

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