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Improving Score-Driven Density Forecasts with an Application to Implied Volatility Surface Dynamics

Author

Listed:
  • Xia Zou

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

  • Yicong Lin

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

  • André Lucas

    (Vrije Universiteit Amsterdam and Tinbergen Institute)

Abstract

Point forecasts of score-driven models have been shown to behave at par with those of state-space models under a variety of circumstances. We show, however, that density rather than point forecasts of plain-vanilla score-driven models substantially underperform their state-space counterparts in a factor model context. We uncover the origins of this phenomenon and show how a simple adjustment of the measurement density of the score-driven model can put score-driven and state-space models approximately back on an equal footing again. The score-driven models can subsequently easily be extended with non-Gaussian features to fit the data even better without complicating parameter estimation. We illustrate our findings using a factor model for the implied volatility surface of S&P500 index options data.

Suggested Citation

  • Xia Zou & Yicong Lin & André Lucas, 2025. "Improving Score-Driven Density Forecasts with an Application to Implied Volatility Surface Dynamics," Tinbergen Institute Discussion Papers 25-036/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20250036
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    References listed on IDEAS

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    1. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
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    4. Koopman, Siem Jan & Mallee, Max I. P. & Van der Wel, Michel, 2010. "Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 329-343.
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    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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