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Empirical analysis and forecasting of volatility dynamics in high‐frequency returns with time‐varying components
Author(s) -
Man Kasing,
Wu Chunchi
Publication year - 2010
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1156
Subject(s) - volatility (finance) , econometrics , autocorrelation , absolute return , ibm , economics , forward volatility , realized variance , autoregressive conditional heteroskedasticity , stochastic volatility , gaussian , mathematics , statistics , return on investment , materials science , investment performance , production (economics) , macroeconomics , nanotechnology , physics , quantum mechanics
We study intraday return volatility dynamics using a time‐varying components approach, and the method is applied to analyze IBM intraday returns. Empirical evidence indicates that with three additive components—a time‐varying mean of absolute returns and two cosine components with time‐varying amplitudes—together they capture very well the pronounced periodicity and persistence behaviors exhibited in the empirical autocorrelation pattern of IBM returns. We find that the long‐run volatility persistence is driven predominantly by daily level shifts in mean absolute returns. After adjusting for these intradaily components, the filtered returns behave much like a Gaussian noise, suggesting that the three‐components structure is adequately specified. Furthermore, a new volatility measure (TCV) can be constructed from these components. Results from extensive out‐of‐sample rolling forecast experiments suggest that TCV fares well in predicting future volatility against alternative methods, including GARCH model, realized volatility and realized absolute value. Copyright © 2009 John Wiley & Sons, Ltd.