Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment

Rohit Deo (), Clifford Hurvich and Yi Lu
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Yi Lu: New York University

Econometrics from University Library of Munich, Germany

Abstract: We study the modeling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of large datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalizing the volatility in high frequency data is proposed, that allows for slowly varying seasonality. Using both simulated as well as real data, we compare the forecasting performance of the LMSV model for forecasting realized volatility to that of a linear long memory model fit to the log realized volatility. The performance of the new seasonal adjustment is also compared to a recently proposed procedure using real data.

Keywords: Realized Volatility; Long Memory Stochastic Volatility Model; High Frequency Data; Seasonal Adjustment (search for similar items in EconPapers)
JEL-codes: C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2005-01-07
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fin and nep-fmk
Note: Type of Document - pdf; pages: 46
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Citations: View citations in EconPapers (9)

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Related works:
Journal Article: Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0501002

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