The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing

TL;DR

This paper introduces a nonparametric estimator for the empirical distribution of latent stochastic volatility using high-frequency financial data, demonstrating its consistency and usefulness for goodness-of-fit testing of volatility models.

Contribution

It develops a new realized empirical distribution function (REDF) estimator for stochastic variance and applies it to goodness-of-fit testing in financial models.

Findings

REDF is consistent as observation mesh shrinks

REDF converges to the true volatility distribution over time

Goodness-of-fit tests perform well in simulations and empirical data

Abstract

We propose a nonparametric estimator of the empirical distribution function (EDF) of the latent spot variance of the log-price of a financial asset. We show that over a fixed time span our realized EDF (or REDF) -- inferred from noisy high-frequency data -- is consistent as the mesh of the observation grid goes to zero. In a double-asymptotic framework, with time also increasing to infinity, the REDF converges to the cumulative distribution function of volatility, if it exists. We exploit these results to construct some new goodness-of-fit tests for stochastic volatility models. In a Monte Carlo study, the REDF is found to be accurate over the entire support of volatility. This leads to goodness-of-fit tests that are both correctly sized and relatively powerful against common alternatives. In an empirical application, we recover the REDF from stock market high-frequency data. We inspect the goodness-of-fit of several two-parameter marginal distributions that are inherent in standard stochastic volatility models. The inverse Gaussian offers the best overall description of random equity variation, but the fit is less than perfect. This suggests an extra parameter (as available in, e.g., the generalized inverse Gaussian) is required to model stochastic variance.