Crossing penalised CAViaR

TL;DR

This paper introduces a novel joint estimation method for multiple dynamic quantiles in time-series data, using a crossing penalty to improve accuracy and robustness, validated through simulations and real data application.

Contribution

It proposes a new objective function with a crossing penalty for joint quantile estimation, addressing limitations of existing sequential or restrictive methods.

Findings

The method effectively prevents crossing of quantiles.

Monte Carlo experiments demonstrate improved estimation accuracy.

Empirical application on FTSE100 shows practical utility.

Abstract

Dynamic quantiles, or Conditional Autoregressive Value at Risk (CAViaR) models, have been extensively studied at the individual level. However, efforts to estimate multiple dynamic quantiles jointly have been limited. Existing approaches either sequentially estimate fitted quantiles or impose restrictive assumptions on the data generating process. This paper fills this gap by proposing an objective function for the joint estimation of all quantiles, introducing a crossing penalty to guide the process. Monte Carlo experiments and an empirical application on the FTSE100 validate the effectiveness of the method, offering a flexible and robust approach to modelling multiple dynamic quantiles in time-series data.