Non-parametric estimation of conditional quantiles for time series with heavy tails

TL;DR

This paper introduces a modified non-parametric estimator for conditional quantiles in heavy-tailed time series, demonstrating its asymptotic properties and practical effectiveness through simulations and real data application.

Contribution

It presents a novel weighted Nadaraya-Watson estimator tailored for heavy-tailed time series, with proven asymptotic normality and demonstrated empirical performance.

Findings

Estimator exhibits asymptotic normality.

Simulation results show improved accuracy.

Method effectively applied to real dataset.

Abstract

We propose a modified weighted Nadaraya-Watson estimator for the conditional distribution of a time series with heavy tails. We establish the asymptotic normality of the proposed estimator. Simulation study is carried out to assess the performance of the estimator. We illustrate our method using a dataset.