# Nonparametric estimation of conditional value-at-risk and expected shortfall based on extreme value theory

arXiv: https://arxiv.org/abs/1612.08099 · HTML: https://mlsanity.com/paper/1612.08099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2016.

## Built on by (most cited first, 1 of 1)

- [Weighted-average quantile regression](https://mlsanity.com/paper/2203.03032/) (2022)

## Abstract (arXiv)

We propose nonparametric estimators for conditional value-at-risk (CVaR) and conditional expected shortfall (CES) associated with conditional distributions of a series of returns on a financial asset. The return series and the conditioning covariates, which may include lagged returns and other exogenous variables, are assumed to be strong mixing and follow a nonparametric conditional location-scale model. First stage nonparametric estimators for location and scale are combined with a generalized Pareto approximation for distribution tails proposed by Pickands (1975) to give final estimators for CVaR and CES. We provide consistency and asymptotic normality of the proposed estimators under suitable normalization. We also present the results of a Monte Carlo study that sheds light on their finite sample performance. Empirical viability of the model and estimators is investigated through a backtesting exercise using returns on future contracts for five agricultural commodities.