Optimal Multi-Step-Ahead Prediction of ARCH/GARCH Models and NoVaS Transformation

Jie Chen and Dimitris N. Politis
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Jie Chen: Department of Mathematics, University of California, San Diego, CA 92093, USA
Dimitris N. Politis: Department of Mathematics, University of California, San Diego, CA 92093, USA

Econometrics, 2019, vol. 7, issue 3, 1-23

Abstract: This paper gives a computer-intensive approach to multi-step-ahead prediction of volatility in financial returns series under an ARCH/GARCH model and also under a model-free setting, namely employing the NoVaS transformation. Our model-based approach only assumes i . i . d innovations without requiring knowledge/assumption of the error distribution and is computationally straightforward. The model-free approach is formally quite similar, albeit a GARCH model is not assumed. We conducted a number of simulations to show that the proposed approach works well for both point prediction (under L 1 and/or L 2 measures) and prediction intervals that were constructed using bootstrapping. The performance of GARCH models and the model-free approach for multi-step ahead prediction was also compared under different data generating processes.

Keywords: bootstrap; L 1 and L 2 measures; GARCH(1,1); NoVaS transformation; multi-step prediction; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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