The long memory HEAVY process: modeling and forecasting financial volatility

Volatility around the clock: Bayesian modeling and forecasting of intraday volatility in the financial crisis

High frequency data provides a rich source of information for understanding financial markets and time series properties of returns. This paper estimates models of high frequency index futures returns using ‘around the clock’ 5-minute returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capt...

Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models

This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Car...

Modeling long memory in stock market volatility

Inspired by the idea that regime switching may give rise to persistence that is observationally equivalent to a unit root, we derive a regime switching process that exhibits long memory. The feature of the process that generates long memory is a heavytailed duration distribution. Using this process for volatility, we obtain a regime switching stochastic volatility (RSSV) model that we "t to dai...

Modeling and pricing long memory in stock market volatility

A new class of fractionally integrated GARCH and EGARCH models for characterizing financial market volatility is discussed. Monte Carlo simulations illustrate the reliability of quasi maximum likelihood estimation methods, standard model selection criteria, and residual-based portmanteau diagnostic tests in this context. New empirical evidence suggests that the apparent long-run dependence in U...

Modeling and forecasting financial market volatility: past developments and new directions