Vast volatility matrix estimation for high-frequency financial data

TL;DR

This paper introduces a new estimator for large integrated volatility matrices in high-frequency financial data, addressing the inconsistency of existing methods when both the number of assets and data points grow large.

Contribution

It develops a novel estimator with proven asymptotic properties that remains consistent and efficient in high-dimensional settings with many assets.

Findings

Proposed estimators achieve high convergence rates under sparsity.

Numerical studies show strong performance for large asset numbers.

Application to real data demonstrates practical effectiveness.

Abstract

High-frequency data observed on the prices of financial assets are commonly modeled by diffusion processes with micro-structure noise, and realized volatility-based methods are often used to estimate integrated volatility. For problems involving a large number of assets, the estimation objects we face are volatility matrices of large size. The existing volatility estimators work well for a small number of assets but perform poorly when the number of assets is very large. In fact, they are inconsistent when both the number, $p$, of the assets and the average sample size, $n$, of the price data on the $p$ assets go to infinity. This paper proposes a new type of estimators for the integrated volatility matrix and establishes asymptotic theory for the proposed estimators in the framework that allows both $n$ and $p$ to approach to infinity. The theory shows that the proposed estimators achieve high convergence rates under a sparsity assumption on the integrated volatility matrix. The numerical studies demonstrate that the proposed estimators perform well for large $p$ and complex price and volatility models. The proposed method is applied to real high-frequency financial data.