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Forecasting Realized Volatility Using A Nonnegative Semiparametric Model

Author

Listed:
  • Daniel Preve

    (Singapore Management University)

  • Anders Eriksson
  • Jun Yu
Abstract
This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the dependency structure and distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Simulation studies validate the new estimation method and suggest that it works reasonably well in finite samples. The out-of-sample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a log-linear AR(1) model, and two long-memory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.

Suggested Citation

  • Daniel Preve & Anders Eriksson & Jun Yu, 2009. "Forecasting Realized Volatility Using A Nonnegative Semiparametric Model," Finance Working Papers 23049, East Asian Bureau of Economic Research.
    • Handle: RePEc:eab:financ:23049
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    File URL: http://www.eaber.org/node/23049
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    References listed on IDEAS

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    Cited by:

    1. Preve, Daniel, 2015. "Linear programming-based estimators in nonnegative autoregression," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 225-234.
    2. Yiu-Kuen Tse, 2019. "Editorial for the Special Issue on Financial Econometrics," JRFM, MDPI, vol. 12(3), pages 1-2, September.
    3. Puneet Prakash & Vikas Sangwan & Kewal Singh, 2021. "Transformational Approach to Analytical Value-at-Risk for near Normal Distributions," JRFM, MDPI, vol. 14(2), pages 1-19, January.
    4. Bhimasankaram Pochiraju & Sridhar Seshadri & Dimitrios D. Thomakos & Konstantinos Nikolopoulos, 2020. "Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization," Stats, MDPI, vol. 3(3), pages 1-18, July.
    5. Thanasis Stengos, 2020. "Recent Advancements in Section “Economics and Finance”," JRFM, MDPI, vol. 13(11), pages 1-2, November.

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    More about this item

    Keywords

    Autoregression; nonlinear/non-Gaussian time series; realized volatility; semiparametric model; volatility forecast;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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