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Abstract Details
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Activity Number:
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669
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #302729 |
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Title:
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Forecasting Volatility: A Continuous Time Model Versus Discrete Time Models
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Author(s):
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Carles Breto*+ and Helena Veiga
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Companies:
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Universidad Carlos III de Madrid and Universidad Carlos III de Madrid
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Address:
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, , International, 28903, Spain
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Keywords:
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asymmetry ;
maximum likelihood ;
particle filter ;
iterated filtering ;
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Abstract:
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We compare empirically the forecasting performance of a continuous time stochastic volatility model with leverage to that of a set of alternative discrete time models. We use three return time series, four loss functions and tests of conditional predictive ability. The tests show evidence that pure long memory GARCH-type models often seem to perform worse than the competitors for a one day ahead horizon. The inclusion of leverage, heavy tails and/or skewed distributions in the models is key for improving forecasting ability.
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