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

Department of Mathematics, University of California, San Diego, CA 92093, USA
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Econometrics 2019, 7(3), 34; https://doi.org/10.3390/econometrics7030034
Received: 31 October 2018 / Revised: 9 July 2019 / Accepted: 21 July 2019 / Published: 8 August 2019
(This article belongs to the Special Issue Resampling Methods in Econometrics)
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. View Full-Text
Keywords: bootstrap; L1 and L2 measures; GARCH(1,1); NoVaS transformation; multi-step prediction; Monte Carlo simulation bootstrap; L1 and L2 measures; GARCH(1,1); NoVaS transformation; multi-step prediction; Monte Carlo simulation
MDPI and ACS Style

Chen, J.; Politis, D.N. Optimal Multi-Step-Ahead Prediction of ARCH/GARCH Models and NoVaS Transformation. Econometrics 2019, 7, 34.

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