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FORECASTING VOLATILITY IN THE PRESENCE OF MODEL INSTABILITY
Author(s) -
Maheu John M.,
Reeves Jonathan J.,
Xie Xuan
Publication year - 2010
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2010.00576.x
Subject(s) - volatility (finance) , econometrics , instability , forward volatility , stochastic volatility , economics , implied volatility , mathematics , realized variance , mechanics , physics
Summary Recent advances in financial econometrics have allowed for the construction of efficient ex post measures of daily volatility. This paper investigates the importance of instability in models of realised volatility and their corresponding forecasts. Testing for model instability is conducted with a subsampling method. We show that removing structurally unstable data of a short duration has a negligible impact on the accuracy of conditional mean forecasts of volatility. In contrast, it does provide a substantial improvement in a model's forecast density of volatility. In addition, the forecasting performance improves, often dramatically, when we evaluate models on structurally stable data.