Conditional Heteroskedasticity of Return Range Processes

TL;DR

This paper introduces an interval-valued GARCH model for asset return ranges, capturing volatility information from price ranges as random intervals, with empirical analysis on Dow Jones stocks.

Contribution

It proposes a novel Int-GARCH model for return ranges as random intervals and develops a metric-based CLS estimation method.

Findings

Model effectively captures volatility from price ranges

Empirical results show the model's practical usefulness

Provides new insights into asset volatility analysis

Abstract

Price range contains important information about the asset volatility, and has long been considered an important indicator for it. In this paper, we propose to jointly model the [low, high] price range as a random interval and introduce an interval-valued GARCH (Int-GARCH) model for the corresponding [low, high] return range process. Model properties are presented under the general framework of random sets, and the parameters are estimated by a metric-based conditional least squares (CLS) method. Our empirical analysis of the daily return range data of Dow Jones component stocks yields very interesting results.