A nonparametric approach to understand multivariate quantile dynamics in financial time series

TL;DR

This paper develops a flexible nonparametric framework for analyzing multivariate quantile dynamics in financial time series, accommodating dependence and providing theoretical guarantees.

Contribution

It introduces a novel multivariate nonparametric regression approach that handles temporal dependence and establishes convergence and consistency results.

Findings

Strong and weak convergence of estimators proven.

Conditional geometric quantiles are shown to be consistent.

Simulation studies demonstrate practical effectiveness.

Abstract

Over the last decade, nonparametric methods have gained increasing attention for modeling complex data structures due to their flexibility and minimal structural assumptions. In this paper, we study a general multivariate nonparametric regression framework that encompasses a broad class of parametric models commonly used in financial econometrics. Both the response and the covariate processes are allowed to be multivariate with fixed finite dimensions, and the framework accommodates temporal dependence, thereby introducing additional modeling and theoretical hurdles. To address these challenges, we adopt a functional dependence structure which permits flexible dynamic behavior while maintaining tractable asymptotic analysis. Within this setting, we establish strong and weak convergence results for the estimators of the conditional mean and volatility functions. In addition, we investigate conditional geometric quantiles in the multivariate time series context and prove their consistency under mild regularity conditions. The finite sample performance is examined through comprehensive simulation studies, and the methodology is illustrated by modeling the stock returns of Maersk and Lockheed Martin as a nonparametric function of a geopolitical risk index.