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Realized Stochastic Volatility with General Asymmetry and Long Memory

Author

Listed:
  • Manabu Asai

    (Soka University, Japan)

  • Chia-Lin Chang

    (National Chung Hsing University, Taiwan)

  • Michael McAleer

    (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, The Netherlands;Complutense University of Madrid, Spain; Yokohama National University, Japan)

Abstract

The paper develops a novel realized stochastic volatility model of asset returns and realized volatility that incorporates general asymmetry and long memory (hereafter the RSV-GALM model). The contribution of the paper ties in with Robert Basmann’s seminal work in terms of the estimation of highly non-linear model specifications (“Causality tests and observationally equivalent representations of econometric models”, Journal of Econometrics, 1988), especially for specifying causal effects from returns to future volatility. This paper discusses asymptotic results of a Whittle likelihood estimator for the RSV-GALM model and a test for general asymmetry, and analyses the finite sample properties. The paper also develops an approach to obtain volatility estimates and out-of-sample forecasts. Using high frequency data for three US financial assets, the new model is estimated and evaluated. The paper compares the forecasting performance of the new model with a realized conditional volatility model.

Suggested Citation

  • Manabu Asai & Chia-Lin Chang & Michael McAleer, 2017. "Realized Stochastic Volatility with General Asymmetry and Long Memory," Tinbergen Institute Discussion Papers 17-038/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20170038
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    3. Zea Bermudez, Patrícia de & Rue, Havard, 2021. "Integrated nested Laplace approximations for threshold stochastic volatility models," DES - Working Papers. Statistics and Econometrics. WS 31804, Universidad Carlos III de Madrid. Departamento de Estadística.
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    More about this item

    Keywords

    Stochastic Volatility; Realized Measure; Long Memory; Asymmetry; Whittle likelihood;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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