TY - JOUR
T1 - Combining Long Memory and Level Shifts in Modelling and Forecasting the Volatility of Asset Returns
AU - Varneskov, Rasmus T.
AU - Perron, Pierre
PY - 2018
Y1 - 2018
N2 - We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons.
AB - We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors. The Kalman filter is used to construct the state-augmented likelihood function and subsequently to generate forecasts, which are mean and path-corrected. We apply our model to eight daily volatility series constructed from both high-frequency and daily returns. Full sample parameter estimates reveal that random level shifts are present in all series. Genuine long memory is present in most high-frequency measures of volatility, whereas there is little remaining dynamics in the volatility measures constructed using daily returns. From extensive forecast evaluations, we find that our ARFIMA model with random level shifts consistently belongs to the 10% Model Confidence Set across a variety of forecast horizons, asset classes and volatility measures. The gains in forecast accuracy can be very pronounced, especially at longer horizons.
KW - Forecasting
KW - Kalman filter
KW - Long memory processes
KW - State space modelling
KW - Stochastic volatility
KW - Structural change
KW - Forecasting
KW - Kalman filter
KW - Long memory processes
KW - State space modelling
KW - Stochastic volatility
KW - Structural change
U2 - 10.1080/14697688.2017.1329591
DO - 10.1080/14697688.2017.1329591
M3 - Journal article
SN - 1469-7688
VL - 18
SP - 371
EP - 393
JO - Quantitative Finance
JF - Quantitative Finance
IS - 3
ER -