Work

Forecasting value-at-risk by encompassing CAViaR models via information criteria

Year: 2013

Type: article

Abstract: This paper proposes a new method of VaR forecasting using the conditional autoregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification.

Source: Journal of the Korean Data and Information Science Society

DOI: 10.7465/jkdi.2013.24.6.1531

Authors: Sangyeol Lee, Jungsik Noh

Institution: Seoul National University

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: bronze

Funders: National Research Foundation, Ministry of Science, ICT and Future Planning, National Research Foundation of Korea

Award: G5045347524