Model Risk of Volatility Models

Lazar Emese; Ning Zhang

doi:10.1016/j.ecosta.2022.06.002

Model Risk of Volatility Models

Lazar Emese, Ning Zhang^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

A new model risk measure and estimation methodology based on loss functions is proposed in order to evaluate the accuracy of volatility models. The reliability of the proposed estimation has been verified via simulations and the estimates provide a reasonable fit to the true model risk measure. An empirical analysis based on several assets is undertaken to identify the models most affected by model risk, and concludes that the accuracy of volatility models can be improved by adjusting variance forecasts for model risk. The results indicate that after crisis situations, model risk increases especially for badly fitting volatility models.

Original language	English
Journal	Econometrics and Statistics
DOIs	https://doi.org/10.1016/j.ecosta.2022.06.002
Publication status	Accepted/In press - Jun 2022
Externally published	Yes

Keywords

Model Risk
Scoring Functions
Volatility Forecast

Access to Document

10.1016/j.ecosta.2022.06.002

Cite this

@article{81b844c90b804b20ba65fa6ce2d8c93f,

title = "Model Risk of Volatility Models",

abstract = "A new model risk measure and estimation methodology based on loss functions is proposed in order to evaluate the accuracy of volatility models. The reliability of the proposed estimation has been verified via simulations and the estimates provide a reasonable fit to the true model risk measure. An empirical analysis based on several assets is undertaken to identify the models most affected by model risk, and concludes that the accuracy of volatility models can be improved by adjusting variance forecasts for model risk. The results indicate that after crisis situations, model risk increases especially for badly fitting volatility models.",

keywords = "Model Risk, Scoring Functions, Volatility Forecast",

author = "Lazar Emese and Ning Zhang",

note = "Funding Information: We are grateful to editors, two anonymous referees and Michael P. Clements for their helpful feedback and comments. We also thank the participants of the 14th International Conference on Computational and Financial Econometrics (CFE 2020, Virtual). We acknowledge that this research received no external funding. Publisher Copyright: {\textcopyright} 2022 EcoSta Econometrics and Statistics",

year = "2022",

month = jun,

doi = "10.1016/j.ecosta.2022.06.002",

language = "English",

journal = "Econometrics and Statistics",

issn = "2452-3062",

}

TY - JOUR

T1 - Model Risk of Volatility Models

AU - Emese, Lazar

AU - Zhang, Ning

N1 - Funding Information: We are grateful to editors, two anonymous referees and Michael P. Clements for their helpful feedback and comments. We also thank the participants of the 14th International Conference on Computational and Financial Econometrics (CFE 2020, Virtual). We acknowledge that this research received no external funding. Publisher Copyright: © 2022 EcoSta Econometrics and Statistics

PY - 2022/6

Y1 - 2022/6

N2 - A new model risk measure and estimation methodology based on loss functions is proposed in order to evaluate the accuracy of volatility models. The reliability of the proposed estimation has been verified via simulations and the estimates provide a reasonable fit to the true model risk measure. An empirical analysis based on several assets is undertaken to identify the models most affected by model risk, and concludes that the accuracy of volatility models can be improved by adjusting variance forecasts for model risk. The results indicate that after crisis situations, model risk increases especially for badly fitting volatility models.

AB - A new model risk measure and estimation methodology based on loss functions is proposed in order to evaluate the accuracy of volatility models. The reliability of the proposed estimation has been verified via simulations and the estimates provide a reasonable fit to the true model risk measure. An empirical analysis based on several assets is undertaken to identify the models most affected by model risk, and concludes that the accuracy of volatility models can be improved by adjusting variance forecasts for model risk. The results indicate that after crisis situations, model risk increases especially for badly fitting volatility models.

KW - Model Risk

KW - Scoring Functions

KW - Volatility Forecast

UR - http://www.scopus.com/inward/record.url?scp=85133886136&partnerID=8YFLogxK

U2 - 10.1016/j.ecosta.2022.06.002

DO - 10.1016/j.ecosta.2022.06.002

M3 - Article

AN - SCOPUS:85133886136

SN - 2452-3062

JO - Econometrics and Statistics

JF - Econometrics and Statistics

ER -

Model Risk of Volatility Models

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this