Stochastic Volatility Models Including Open, Close, High and Low Prices

TL;DR

This paper introduces a new class of stochastic volatility models that incorporate open, close, high, and low prices within trading periods, enhancing volatility inference and model fitting using sequential Monte Carlo methods.

Contribution

It develops a novel stochastic volatility model leveraging all four price points and compares it with existing models, demonstrating improved inference capabilities.

Findings

Model effectively captures volatility dynamics using high, low, open, close prices.

Sequential Monte Carlo algorithms successfully fit the proposed models.

Empirical analysis on SP500 data validates model performance.

Abstract

Mounting empirical evidence suggests that the observed extreme prices within a trading period can provide valuable information about the volatility of the process within that period. In this paper we define a class of stochastic volatility models that uses opening and closing prices along with the minimum and maximum prices within a trading period to infer the dynamics underlying the volatility process of asset prices and compares it with similar models that have been previously presented in the literature. The paper also discusses sequential Monte Carlo algorithms to fit this class of models and illustrates its features using both a simulation study and data form the SP500 index.