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Generalized Autoregressive Gamma Processes

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  • Bruno Feunou

Abstract

We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes that extends the class of existing autoregressive gamma (ARG) processes in one important dimension: each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. The paper provides ergodicity conditions for GARG processes and derives closed-form conditional and unconditional moments. The paper also presents estimation and inference methods, illustrated by an application to European option pricing where the daily realized variance follows a GARG dynamic. Our results show that using GARG processes reduces pricing errors by substantially more than using ARG processes does.

Suggested Citation

  • Bruno Feunou, 2023. "Generalized Autoregressive Gamma Processes," Staff Working Papers 23-40, Bank of Canada.
    • Handle: RePEc:bca:bocawp:23-40
    DOI: 10.34989/swp-2023-40
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    References listed on IDEAS

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    1. Bruno Feunou & Roméo Tédongap, 2012. "A Stochastic Volatility Model With Conditional Skewness," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 576-591, July.
    2. Bollerslev, Tim & Zhou, Hao, 2004. "Corrigendum to "Estimating stochastic volatility diffusion using conditional moments of integrated volatility" [J. Econom. 109 (2002) 33-65]," Journal of Econometrics, Elsevier, vol. 119(1), pages 221-222, March.
    3. Christoffersen, Peter & Heston, Steve & Jacobs, Kris, 2006. "Option valuation with conditional skewness," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 253-284.
    4. Knight, John L. & Yu, Jun, 2002. "Empirical Characteristic Function In Time Series Estimation," Econometric Theory, Cambridge University Press, vol. 18(3), pages 691-721, June.
    5. Marine Carrasco & Jean-Pierre Florens, 2000. "Efficient GMM Estimation Using the Empirical Characteristic Function," Working Papers 2000-33, Center for Research in Economics and Statistics.
    6. David S. Bates, 2006. "Maximum Likelihood Estimation of Latent Affine Processes," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 909-965.
    7. Bollerslev, Tim & Zhou, Hao, 2002. "Estimating stochastic volatility diffusion using conditional moments of integrated volatility," Journal of Econometrics, Elsevier, vol. 109(1), pages 33-65, July.
    8. Carrasco, Marine & Chernov, Mikhail & Florens, Jean-Pierre & Ghysels, Eric, 2007. "Efficient estimation of general dynamic models with a continuum of moment conditions," Journal of Econometrics, Elsevier, vol. 140(2), pages 529-573, October.
    9. Heston, Steven L & Nandi, Saikat, 2000. "A Closed-Form GARCH Option Valuation Model," The Review of Financial Studies, Society for Financial Studies, vol. 13(3), pages 585-625.
    10. Anh Le & Kenneth J. Singleton & Qiang Dai, 2010. "Discrete-Time Affine-super-ℚ Term Structure Models with Generalized Market Prices of Risk," The Review of Financial Studies, Society for Financial Studies, vol. 23(5), pages 2184-2227.
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    Keywords

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    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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