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The Role of Dynamic Specification in Forecasting Volatility in the Presence of Jumps and Noisy High-Frequency Data

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  • Rasmus Tangsgaard Varneskov

    ()
    (School of Economics and Management, Aarhus University and CREATES)

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    Abstract

    This paper considers the performance of di erent long-memory dynamic models when forecasting volatility in the stock market using implied volatility as an exogenous variable in the information set. Observed volatility is sep- arated into its continuous and jump components in a framework that allows for consistent estimation in the presence of market microstructure noise. A comparison between a class of HAR- and ARFIMA models is facilitated on the basis of out-of-sample forecasting performance. Implied volatility conveys incremental information about future volatility in both specifications, improv- ing performance both in- and out-of-sample for all models. Furthermore, the ARFIMA class of models dominates the HAR speci cations in terms of out-of- sample performance both with and without implied volatility in the information set. A vectorized ARFIMA (vecARFIMA) model is introduced to control for possible endogeneity issues. This model is compared to a vecHAR speci cation, re-enforcing the results from the single equation framework.

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    File URL: ftp://ftp.econ.au.dk/creates/rp/10/rp10_39.pdf
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    Bibliographic Info

    Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-39.

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    Length: 37
    Date of creation: 19 Aug 2010
    Date of revision:
    Handle: RePEc:aah:create:2010-39

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    Web page: http://www.econ.au.dk/afn/

    Related research

    Keywords: ARFIMA; HAR; Implied Volatility; Jumps; Market Microstructure Noise; VecARFIMA; Volatility Forecasting;

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