Article2014

Bayesian Modeling and Forecasting of 24-Hour High-Frequency Volatility

Jonathan Stroud · Michael Slater Johannes

39 citations0/0 files storedJournal of the American Statistical Association

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Financial Risk and Volatility Modeling Financial Markets and Investment Strategies Stochastic processes and financial applications

Abstract

This article estimates models of high-frequency index futures returns using “around-the-clock” 5-min returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns, correlations between return and volatility shocks, and announcement effects. We develop an integrated MCMC approach to estimate interday and intraday parameters and states using high-frequency data without resorting to various aggregation measures like realized volatility. We provide a case study using financial crisis data from 2007 to 2009, and use particle filters to construct likelihood functions for model comparison and out-of-sample forecasting from 2009 to 2012. We show that our approach improves realized volatility forecasts by up to 50% over existing benchmarks and is also useful for risk management and trading applications. Supplementary materials for this article are available online.

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References

1986Generalized autoregressive conditional heteroskedasticity 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 2003Modeling and Forecasting Realized Volatility 2000Transform Analysis and Asset Pricing for Affine Jump-diffusions 1997Empirical Performance of Alternative Option Pricing Models 1998Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models 2002Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models

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2020Market Return Around the Clock: A Puzzle 2020Forecasting price of financial market crash via a new nonlinear potential GARCH model 2022Volatility Forecasting with Machine Learning and Intraday Commonality 2015Modelling household finances: A Bayesian approach to a multivariate two-part model

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