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A Term Structure Model of Interest Rates with Quadratic Volatility

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  • TAKAMIZAWA, Hideyuki
  • 高見澤, 秀幸

Abstract

This study proposes a no-arbitrage term structure model that can capture the volatility of interest rates without sacrificing the goodness-of-_t to the cross-section and predictive ability about the level of interest rates. The key feature of the model is the covariance matrix of changes in factors, which is specified as quadratic functions of factors. The quadratic specification can capture intense volatility even with spanned factors, which is not the case for the affine specification. Furthermore, since the quadratic specification guarantees the positive definiteness of the covariance matrix without restricting the sign of factors, it allows for a flexible specification of the physical drift as does the Gaussian term structure model, contributing also to accurate level prediction.

Suggested Citation

  • TAKAMIZAWA, Hideyuki & 高見澤, 秀幸, 2017. "A Term Structure Model of Interest Rates with Quadratic Volatility," Working Paper Series G-1-18, Hitotsubashi University Center for Financial Research.
    • Handle: RePEc:hit:hcfrwp:g-1-18
    Note: This draft: December 9, 2017
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    References listed on IDEAS

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    Cited by:

    1. Takamizawa, Hideyuki, 2022. "How arbitrage-free is the Nelson–Siegel model under stochastic volatility?," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 205-223.

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    More about this item

    Keywords

    Term structure; Interest rate; Volatility; Affine model; Prediction;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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