Quantiled conditional variance, skewness, and kurtosis by Cornish-Fisher expansion

TL;DR

This paper introduces a new quantile-based method using Cornish-Fisher expansion to estimate conditional moments like variance, skewness, and kurtosis in time series, avoiding model mis-specification and estimation instability.

Contribution

It proposes a novel quantiled conditional moments (QCMs) estimation approach that is consistent, simple, and effective without prior mean estimation, improving robustness in time series analysis.

Findings

QCMs are consistent with a convergence rate of n^{-1/2}

Simulation studies show robustness under various errors

Application to exchange rates demonstrates practical effectiveness

Abstract

The conditional variance, skewness, and kurtosis play a central role in time series analysis. These three conditional moments (CMs) are often studied by some parametric models but with two big issues: the risk of model mis-specification and the instability of model estimation. To avoid the above two issues, this paper proposes a novel method to estimate these three CMs by the so-called quantiled CMs (QCMs). The QCM method first adopts the idea of Cornish-Fisher expansion to construct a linear regression model, based on $n$ different estimated conditional quantiles. Next, it computes the QCMs simply and simultaneously by using the ordinary least squares estimator of this regression model, without any prior estimation of the conditional mean. Under certain conditions, the QCMs are shown to be consistent with the convergence rate $n^{-1/2}$. Simulation studies indicate that the QCMs perform well under different scenarios of Cornish-Fisher expansion errors and quantile estimation errors. In the application, the study of QCMs for three exchange rates demonstrates the effectiveness of financial rescue plans during the COVID-19 pandemic outbreak, and suggests that the existing ``news impact curve'' functions for the conditional skewness and kurtosis may not be suitable.