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Leopoldo Catania

Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Standard

Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study. / Ardia, David; Bluteau, Keven; Boudt, Kris et al.
In: International Journal of Forecasting, Vol. 34, No. 4, 2018, p. 733-747.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Harvard

Ardia, D, Bluteau, K, Boudt, K & Catania, L 2018, 'Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study', International Journal of Forecasting, vol. 34, no. 4, pp. 733-747. https://doi.org/10.1016/j.ijforecast.2018.05.004

APA

Ardia, D., Bluteau, K., Boudt, K., & Catania, L. (2018). Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study. International Journal of Forecasting, 34(4), 733-747. https://doi.org/10.1016/j.ijforecast.2018.05.004

CBE

Ardia D, Bluteau K, Boudt K, Catania L. 2018. Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study. International Journal of Forecasting. 34(4):733-747. https://doi.org/10.1016/j.ijforecast.2018.05.004

MLA

Ardia, David et al. "Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study". International Journal of Forecasting. 2018, 34(4). 733-747. https://doi.org/10.1016/j.ijforecast.2018.05.004

Vancouver

Ardia D, Bluteau K, Boudt K, Catania L. Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study. International Journal of Forecasting. 2018;34(4):733-747. doi: 10.1016/j.ijforecast.2018.05.004

Author

Ardia, David ; Bluteau, Keven ; Boudt, Kris et al. / Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study. In: International Journal of Forecasting. 2018 ; Vol. 34, No. 4. pp. 733-747.

Bibtex

@article{10dccc48cd474102a5c5d06cc720ec52,
title = "Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study",
abstract = "We perform a large-scale empirical study in order to compare the forecasting performances of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that MSGARCH models yield more accurate Value-at-Risk, expected shortfall, and left-tail distribution forecasts than their single-regime counterparts for daily, weekly, and ten-day equity log-returns. Also, our results indicate that accounting for parameter uncertainty improves the left-tail predictions, independently of the inclusion of the Markov-switching mechanism.",
keywords = "Expected shortfall, Forecasting performance, GARCH, Large-scale study, MSGARCH, Risk management, Value-at-risk",
author = "David Ardia and Keven Bluteau and Kris Boudt and Leopoldo Catania",
year = "2018",
doi = "10.1016/j.ijforecast.2018.05.004",
language = "English",
volume = "34",
pages = "733--747",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier BV",
number = "4",

}

RIS

TY - JOUR

T1 - Forecasting Risk with Markov-Switching GARCH Models: A large-scale performance study

AU - Ardia, David

AU - Bluteau, Keven

AU - Boudt, Kris

AU - Catania, Leopoldo

PY - 2018

Y1 - 2018

N2 - We perform a large-scale empirical study in order to compare the forecasting performances of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that MSGARCH models yield more accurate Value-at-Risk, expected shortfall, and left-tail distribution forecasts than their single-regime counterparts for daily, weekly, and ten-day equity log-returns. Also, our results indicate that accounting for parameter uncertainty improves the left-tail predictions, independently of the inclusion of the Markov-switching mechanism.

AB - We perform a large-scale empirical study in order to compare the forecasting performances of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that MSGARCH models yield more accurate Value-at-Risk, expected shortfall, and left-tail distribution forecasts than their single-regime counterparts for daily, weekly, and ten-day equity log-returns. Also, our results indicate that accounting for parameter uncertainty improves the left-tail predictions, independently of the inclusion of the Markov-switching mechanism.

KW - Expected shortfall

KW - Forecasting performance

KW - GARCH

KW - Large-scale study

KW - MSGARCH

KW - Risk management

KW - Value-at-risk

U2 - 10.1016/j.ijforecast.2018.05.004

DO - 10.1016/j.ijforecast.2018.05.004

M3 - Journal article

VL - 34

SP - 733

EP - 747

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 4

ER -