Common price and volatility jumps in noisy high-frequency data

TL;DR

This paper develops a robust statistical test for detecting simultaneous jumps in asset prices and volatility using high-frequency data, accounting for market microstructure noise, and demonstrates its effectiveness through simulations and real data analysis.

Contribution

It introduces a new nonparametric spectral estimator-based test for co-jumps in price and volatility that is robust to microstructure noise.

Findings

The test effectively detects co-jumps in simulated data.

Empirical analysis confirms the presence of co-jumps in NASDAQ data.

The method is practical and robust to market frictions.

Abstract

We introduce a statistical test for simultaneous jumps in the price of a financial asset and its volatility process. The proposed test is based on high-frequency data and is robust to market microstructure frictions. For the test, local estimators of volatility jumps at price jump arrival times are designed using a nonparametric spectral estimator of the spot volatility process. A simulation study and an empirical example with NASDAQ order book data demonstrate the practicability of the proposed methods and highlight the important role played by price volatility co-jumps.