TY - JOUR
T1 - Forecasting tail risk measures for financial time series
T2 - an extreme value approach with covariates
AU - James, Robert
AU - Leung, Henry
AU - Leung, Jessica Wai Yin
AU - Prokhorov, Artem
N1 - Funding Information:
Helpful comments from seminar participants at Monash University, and participants of the 41st International Symposium on Forecasting and the International Conference on Econometrics and Business Analytics are gratefully acknowledged. The use of the University of Sydney’s high performance computing cluster, Artemis, is acknowledged. This research was supported in part by the Monash eResearch Centre and eSolutions-Research Support Services through the use of the MonARCH HPC Cluster. Research for this paper was supported by grants from Australian Research Council (James, Project DP200103549 ) and Russian Science Foundation (Prokhorov, Project No. 22-18-00588 ) for various and non-overlapping parts of this research.
Funding Information:
Helpful comments from seminar participants at Monash University, and participants of the 41st International Symposium on Forecasting and the International Conference on Econometrics and Business Analytics are gratefully acknowledged. The use of the University of Sydney's high performance computing cluster, Artemis, is acknowledged. This research was supported in part by the Monash eResearch Centre and eSolutions-Research Support Services through the use of the MonARCH HPC Cluster. Research for this paper was supported by grants from Australian Research Council (James, Project DP200103549) and Russian Science Foundation (Prokhorov, Project No. 22-18-00588) for various and non-overlapping parts of this research.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/3
Y1 - 2023/3
N2 - The paper develops a tail risk forecasting model that incorporates the wealth of economic and financial information available to risk managers. The approach can be viewed as a regularized extension of the two-stage GARCH-EVT model of McNeil and Frey (2000) where we permit a time-varying data-driven selection of a sparse set of covariates affecting the scale of the extreme value distribution of risk. We use a rich data set from the U.S. equity market to explore when this additional information improves Value-at-Risk and Expected Shortfall forecasts compared to popular tail risk forecasting methods such as the traditional and non-regularized GARCH-EVT models, and the GJR-GARCH(1,1), Hawkes POT model, CaViaR and CARE models. Under an extensive set of performance criteria and tests we demonstrate that our approach produces competitive risk forecasts, particularly during periods of financial distress.
AB - The paper develops a tail risk forecasting model that incorporates the wealth of economic and financial information available to risk managers. The approach can be viewed as a regularized extension of the two-stage GARCH-EVT model of McNeil and Frey (2000) where we permit a time-varying data-driven selection of a sparse set of covariates affecting the scale of the extreme value distribution of risk. We use a rich data set from the U.S. equity market to explore when this additional information improves Value-at-Risk and Expected Shortfall forecasts compared to popular tail risk forecasting methods such as the traditional and non-regularized GARCH-EVT models, and the GJR-GARCH(1,1), Hawkes POT model, CaViaR and CARE models. Under an extensive set of performance criteria and tests we demonstrate that our approach produces competitive risk forecasts, particularly during periods of financial distress.
KW - Expected shortfall
KW - Extreme value theory
KW - GARCH models
KW - Regularization
KW - Value-at-risk
KW - Variable selection
UR - http://www.scopus.com/inward/record.url?scp=85147810306&partnerID=8YFLogxK
U2 - 10.1016/j.jempfin.2023.01.002
DO - 10.1016/j.jempfin.2023.01.002
M3 - Article
AN - SCOPUS:85147810306
VL - 71
SP - 29
EP - 50
JO - Journal of Empirical Finance
JF - Journal of Empirical Finance
SN - 0927-5398
ER -