Homogeneous Volatility Bridge Estimators

TL;DR

This paper introduces homogeneous volatility bridge estimators that efficiently utilize open, high, low, and close prices of stochastic processes, outperforming traditional estimators like Garman-Klass and Parkinson.

Contribution

It develops a new theoretical framework for volatility estimation using homogeneous bridge estimators based on incomplete price bridges.

Findings

New estimators show improved efficiency over Garman-Klass and Parkinson estimators.

The theory encodes information from open, high, low, and close prices effectively.

Performance comparisons demonstrate the advantages of the proposed estimators.

Abstract

We present a theory of homogeneous volatility bridge estimators for log-price stochastic processes. The main tool of our theory is the parsimonious encoding of the information contained in the open, high and low prices of incomplete bridge, corresponding to given log-price stochastic process, and in its close value, for a given time interval. The efficiency of the new proposed estimators is favorably compared with that of the Garman-Klass and Parkinson estimators.