Tail Risk and Quantile Methods in Financial Econometrics

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 665

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Guest Editor
Department of Economics, School of Social Sciences, University of Crete, 74100 Rethymno, Greece
Interests: bayesian statistics-econometrics; forecasting; financial time-series; environmental time-series

Special Issue Information

Dear Colleagues,

Understanding financial markets requires moving beyond averages and into the full shape of the return distribution. In periods of crisis, calm, and everything in between, the behavior of returns in the tails—and their changing structure over time—has proven essential for effective risk assessment, pricing, and forecasting.

This Special Issue seeks to bring together innovative research on distributional methods in finance, with a focus on quantiles, tail risk, skewness, and kurtosis. We invite contributions that explore both theoretical advancements and empirical applications, especially those that challenge traditional Gaussian assumptions and embrace the asymmetric, heavy-tailed, and nonlinear realities of financial markets.

Topics may include the following:

  • Quantile regression and quantile-based forecasting;
  • Tail risk modeling and stress testing;
  • Extreme value theory in financial risk management;
  • Asymmetric volatility and skewness dynamics;
  • Time-varying higher moments (skewness, kurtosis);
  • Quantile-based portfolio optimization and allocation;
  • Distributional modeling in cryptocurrencies, derivatives, or fixed-income markets;
  • Forecasting during market turbulence or crises using nonparametric methods.

We especially welcome interdisciplinary approaches that integrate statistical models, econometrics, and computational finance. The goal is to highlight how distributional analysis can improve our understanding of market dynamics—both under normal conditions and during systemic disruptions.

Dr. Georgios K. Tsiotas
Guest Editor

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Keywords

  • tail risk
  • extreme value theory
  • skewness and kurtosis
  • financial econometrics
  • distributional analysis
  • asymmetric risk
  • time-varying moments
  • inequality measurement
  • quantile forecasting
  • risk management
  • nonlinear dynamics
  • macro-financial modeling
  • quantile treatment effects
  • value-at-risk (VaR)
  • stochastic skewness
  • portfolio tail risk

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Published Papers (1 paper)

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Research

16 pages, 385 KB  
Article
Bayesian Estimation of Extreme Quantiles and the Distribution of Exceedances for Measuring Tail Risk
by Douglas E. Johnston
J. Risk Financial Manag. 2025, 18(12), 659; https://doi.org/10.3390/jrfm18120659 - 21 Nov 2025
Viewed by 437
Abstract
Estimating extreme quantiles and the number of future exceedances is an important task in financial risk management. More important than estimating the quantile itself is to insure zero coverage error, which implies the quantile estimate should, on average, reflect the desired probability of [...] Read more.
Estimating extreme quantiles and the number of future exceedances is an important task in financial risk management. More important than estimating the quantile itself is to insure zero coverage error, which implies the quantile estimate should, on average, reflect the desired probability of exceedance. In this research, we show that for unconditional distributions isomorphic to the exponential, a Bayesian quantile estimate results in zero coverage error. This compares to the traditional maximum likelihood method, where the coverage error can be significant under small sample sizes even though the quantile estimate is unbiased. More generally, we prove a sufficient condition for an unbiased quantile estimator to result in coverage error and we show our result holds by virtue of using a Jeffreys prior for the unknown parameters and is independent of the true prior. We derive a new, predictive distribution, and the moments, for the number of quantile exceedances, and highlight its superior performance. We extend our results to the conditional tail of distributions with asymptotic Paretian tails and, in particular, those in the Fréchet maximum domain of attraction which are typically encountered in finance. We illustrate our results using simulations for a variety of light and heavy-tailed distributions. Full article
(This article belongs to the Special Issue Tail Risk and Quantile Methods in Financial Econometrics)
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