Economics at your fingertips  

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

Jie Chen () and Dimitris N. Politis ()
Additional contact information
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: Track citations by RSS feed

Downloads: (external link) (application/pdf) (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Econometrics is currently edited by Prof. Dr. Kerry Patterson

More articles in Econometrics from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().

Page updated 2019-11-24
Handle: RePEc:gam:jecnmx:v:7:y:2019:i:3:p:34-:d:255884