Text

Efficiently Backtesting Conditional Value-at-Risk and Conditional Expected Shortfall

Given the importance of backtesting risk models and forecasts for financial institutions and regulators, we develop an efficient empirical likelihood backtest for either conditional value-at-risk or conditional expected shortfall when the given risk variable is modeled by an ARMA-GARCH process. Using a two-step procedure, the proposed backtests require less finite moments than existing backtests, allowing for robustness to heavier tails. Furthermore, we add a constraint on the goodness of fit of the error distribution to provide more accurate risk forecasts and improved test power. A simulation study confirms the good finite sample performance of the new backtests, and empirical analyses demonstrate the usefulness of these efficient backtests for monitoring financial crises.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.116 No.536 December 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2041-2052

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CONDITIONAL VALUE-AT-RISK
EMPIRICAL LIKELIHOOD
BACKTEST
CONDITIONAL EXPECTED SHORTFALL
ARMA-GARCH MODEL

Info Detail Spesifik

https://doi.org/10.1080/01621459.2020.1763804

Pernyataan Tanggungjawab

Qihul Su, Zhongling Qin, Liang Peng, Gengsheng Qin

Versi lain/terkait

Lampiran Berkas

Komentar