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    • Publications (5)
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    • Working Paper (4)
    • Journal Article (1)
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    • International Macroeconomics (5)
    • Macroeconomic Research (5)
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  • Tags: long memory
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  1. Publication

    Relative Forecasting Performance of Volatility Models: Monte Carlo Evidence

    01.01.2010

    A Monte Carlo (MC) experiment is conducted to study the forecasting performance of a variety of volatility models under alternative data generating processes (DGPs). The models included in the MC study are the (Fractionally Integrated) Generalized...

  2. Publication

    Forecasting Volatility under Fractality, Regime-Switching, Long Memory and Student-t Innovations

    01.07.2009

    The Markov-Switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-Switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood...

  3. Publication

    Forecasting Volatility under Fractality, Regime-Switching, Long Memory and Student-t Innovations

    01.11.2010

    The Markov-Switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-Switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood...

  4. Publication

    Flexible and Robust Modelling of Volatility Comovements: A Comparison of Two Multifractal Models

    01.02.2010

    Long memory (long-term dependence) of volatility counts as one of the ubiquitous stylized facts of financial data. Inspired by the long memory property, multifractal processes have recently been introduced as a new tool for modeling financial time...

  5. Publication

    A Markov-switching Multifractal Approach to Forecasting Realized Volatility

    01.10.2011

    The volatility specification of the Markov-switching Multifractal (MSM) model is proposed as an alternative mechanism for realized volatility (RV). We estimate the RV-MSM model via Generalized Method of Moments and perform forecasting by means of...

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