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Flexible Modeling of Dependence in Volatility Processes

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  • Maria Kalli
  • Jim Griffin

Abstract

This article proposes a novel stochastic volatility (SV) model that draws from the existing literature on autoregressive SV models, aggregation of autoregressive processes, and Bayesian nonparametric modeling to create a SV model that can capture long-range dependence. The volatility process is assumed to be the aggregate of autoregressive processes, where the distribution of the autoregressive coefficients is modeled using a flexible Bayesian approach. The model provides insight into the dynamic properties of the volatility. An efficient algorithm is defined which uses recently proposed adaptive Monte Carlo methods. The proposed model is applied to the daily returns of stocks.

Suggested Citation

  • Maria Kalli & Jim Griffin, 2015. "Flexible Modeling of Dependence in Volatility Processes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 102-113, January.
  • Handle: RePEc:taf:jnlbes:v:33:y:2015:i:1:p:102-113
    DOI: 10.1080/07350015.2014.925457
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    References listed on IDEAS

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    1. Griffin, J. E. & Steel, M. F. J., 2004. "Semiparametric Bayesian inference for stochastic frontier models," Journal of Econometrics, Elsevier, vol. 123(1), pages 121-152, November.
    2. Nakajima, Jouchi & Omori, Yasuhiro, 2009. "Leverage, heavy-tails and correlated jumps in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2335-2353, April.
    3. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    4. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    5. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    6. Jensen, Mark J. & Maheu, John M., 2010. "Bayesian semiparametric stochastic volatility modeling," Journal of Econometrics, Elsevier, vol. 157(2), pages 306-316, August.
    7. Arteche, Josu, 2004. "Gaussian semiparametric estimation in long memory in stochastic volatility and signal plus noise models," Journal of Econometrics, Elsevier, vol. 119(1), pages 131-154, March.
    8. Omori, Yasuhiro & Chib, Siddhartha & Shephard, Neil & Nakajima, Jouchi, 2007. "Stochastic volatility with leverage: Fast and efficient likelihood inference," Journal of Econometrics, Elsevier, vol. 140(2), pages 425-449, October.
    9. Clifford M. Hurvich & Eric Moulines & Philippe Soulier, 2005. "Estimating Long Memory in Volatility," Econometrica, Econometric Society, vol. 73(4), pages 1283-1328, July.
    10. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
    11. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    12. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    13. Zaffaroni, Paolo, 2004. "Contemporaneous aggregation of linear dynamic models in large economies," Journal of Econometrics, Elsevier, vol. 120(1), pages 75-102, May.
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    Cited by:

    1. Asger Lunde & Anne Floor Brix & Wei Wei, 2015. "A Generalized Schwartz Model for Energy Spot Prices - Estimation using a Particle MCMC Method," CREATES Research Papers 2015-46, Department of Economics and Business Economics, Aarhus University.
    2. Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
    3. Jim Griffin & Maria Kalli & Mark Steel, 2018. "Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 207-218, June.

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