Inference for Multi-Dimensional High-Frequency Data: Equivalence of Methods, Central Limit Theorems, and an Application to Conditional Independence Testing

TL;DR

This paper establishes the asymptotic behavior of multi-scale and kernel estimators for high-frequency multivariate financial data, enabling statistical inference and hypothesis testing in complex asset models.

Contribution

It demonstrates the asymptotic equivalence of multi-scale and kernel estimators and develops stable central limit theorems for high-frequency data analysis.

Findings

Asymptotic distribution of estimators derived

Establishment of stable CLTs for multivariate data

Application to conditional independence testing

Abstract

We find the asymptotic distribution of the multi-dimensional multi-scale and kernel estimators for high-frequency financial data with microstructure. Sampling times are allowed to be asynchronous and endogenous. In the process, we show that the classes of multi-scale and kernel estimators for smoothing noise perturbation are asymptotically equivalent in the sense of having the same asymptotic distribution for corresponding kernel and weight functions. The theory leads to multi-dimensional stable central limit theorems and feasible versions. Hence they allow to draw statistical inference for a broad class of multivariate models which paves the way to tests and confidence intervals in risk measurement for arbitrary portfolios composed of high-frequently observed assets. As an application, we enhance the approach to construct a test for investigating hypotheses that correlated assets are independent conditional on a common factor.