Estimating tail-risk using semiparametric conditional variance with an application to meme stocks

d’Addona, Stefano and Najrin Khanom

International Review of Economics & Finance, 2022, vol. 82, issue C, 241-260

Abstract: In this paper, we propose using Mishra, Su, Ullah’s (2010) semiparametric variance to estimate Value at Risk (VaR) and Expected Shortfall (ES). Although the variance estimate is established in the literature, it has not been applied to VaR and ES estimation. Here the returns’ variance is estimated using Mishra, Su, Ullah’s (2010) conditional semiparametric estimator, and the standardized residuals’ distribution is fitted nonparametrically. In order to reduce possible misspecification bias, the proposed estimators decouple the assumption of the returns’ distribution and the variance estimation. Empirical and simulated data are used to compare the performance of the new estimators against existing parametric and nonparametric VaR and ES models, both conditional and unconditional. The estimators are further applied to daily returns of certain meme stocks to estimate tail risk and test the performance of the estimators during periods of extreme volatility. Compared to the models studied, the proposed conditional, semiparametric VaR model produces fewer violations, and violations without a recognizable pattern - upholding the regulatory requirements. The expected shortfall estimated by the conditional, semiparametric model is also closest to the observed mean of the violations.

Keywords: Value at risk; Expected shortfall; Risk modeling; Nonparametric; Semiparametric; Meme stocks (search for similar items in EconPapers)
JEL-codes: C14 C22 C58 G17 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1059056022001538
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:82:y:2022:i:c:p:241-260

DOI: 10.1016/j.iref.2022.05.012

Access Statistics for this article

International Review of Economics & Finance is currently edited by H. Beladi and C. Chen

More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().