Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity
2004-06-05
Research Paper
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The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model to realized volatilities of the S&P 500 stock index and three exchange rates produces forecasts that clearly improve upon the ones obtained from a linear ARFIMA model and from conventional time-series models based on daily returns, treating volatility as a latent variable.
Keywords
- smooth transition
- long memory
- realized volatility
- volatility forecasting
- high-frequency data
- day-of-the-week effect
- leverage effect
Classifications using
Journal of Economic Literature (JEL) Classification System
- C53 : Forecasting and Other Model Applications
- G15 : International Financial Markets
- C22 : Time-Series Models; Dynamic Quantile Regressions
Automatically Extracted Terms
- model
- volatility
- return
- forecast
- estimate
- table
- effect
- deviation
- period
- figure
- december
- variance
- function
- january
- volatility forecasts
- observation
- arfi models
- &p 500
- journal
- leverage
