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Estimating Stochastic Volatility from Open, High, Low, and Close Prices

Article arXiv papers · Author: Abel Rodriguez et al.
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Summary

The paper develops stochastic-volatility models that use opening and closing prices together with each period’s observed high and low. The motivation is that extreme prices within a period contain information about volatility that close-to-close observations may miss. The models use those four price measures to infer the underlying evolution of asset-price volatility and are compared with related approaches in prior literature.

The paper also discusses sequential Monte Carlo methods for fitting the models. It demonstrates their features in a simulation study and with S&P 500 index data. The supplied description does not report specific comparative performance results or establish that the approach improves trading outcomes. Its scope is volatility estimation, and the quality of inference depends on the model and fitting procedure as well as the information captured by the period’s price extremes.

Key ideas

  • Intraperiod highs and lows can provide information about volatility beyond opening and closing prices.
  • The proposed model class combines open, high, low, and close observations to infer volatility dynamics.
  • The paper compares the models with related methods from existing research.
  • Sequential Monte Carlo algorithms are discussed for fitting the models.
  • The approach is illustrated through simulation and S&P 500 index data.

Tags

VolatilityStatisticsEquitiesUS markets
Full text
# Stochastic Volatility Models Including Open, Close, High and Low Prices


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.