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Recent results in the theory and applications of CARMA processes

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  • P. Brockwell

Abstract

Just as ARMA processes play a central role in the representation of stationary time series with discrete time parameter, $$(Y_n)_{n\in \mathbb {Z}}$$ ( Y n ) n ∈ Z , CARMA processes play an analogous role in the representation of stationary time series with continuous time parameter, $$(Y(t))_{t\in \mathbb {R}}$$ ( Y ( t ) ) t ∈ R . Lévy-driven CARMA processes permit the modelling of heavy-tailed and asymmetric time series and incorporate both distributional and sample-path information. In this article we provide a review of the basic theory and applications, emphasizing developments which have occurred since the earlier review in Brockwell ( 2001a , In D. N. Shanbhag and C. R. Rao (Eds.), Handbook of Statistics 19; Stochastic Processes: Theory and Methods (pp. 249–276), Amsterdam: Elsevier). Copyright The Institute of Statistical Mathematics, Tokyo 2014

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  • P. Brockwell, 2014. "Recent results in the theory and applications of CARMA processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(4), pages 647-685, August.
    • Handle: RePEc:spr:aistmt:v:66:y:2014:i:4:p:647-685
    DOI: 10.1007/s10463-014-0468-7
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    Cited by:

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    2. Nielsen, Mikkel Slot, 2020. "On non-stationary solutions to MSDDEs: Representations and the cointegration space," Stochastic Processes and their Applications, Elsevier, vol. 130(5), pages 3154-3173.
    3. Jürgen Kampf & Georgiy Shevchenko & Evgeny Spodarev, 2021. "Nonparametric estimation of the kernel function of symmetric stable moving average random functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(2), pages 337-367, April.
    4. Bai, Shuyang & Ginovyan, Mamikon S. & Taqqu, Murad S., 2016. "Limit theorems for quadratic forms of Lévy-driven continuous-time linear processes," Stochastic Processes and their Applications, Elsevier, vol. 126(4), pages 1036-1065.
    5. Manuel D. Ortigueira, 2022. "A New Look at the Initial Condition Problem," Mathematics, MDPI, vol. 10(10), pages 1-17, May.
    6. Müller, Gernot & Seibert, Armin, 2019. "Bayesian estimation of stable CARMA spot models for electricity prices," Energy Economics, Elsevier, vol. 78(C), pages 267-277.
    7. Peter J. Brockwell & Yasumasa Matsuda, 2017. "Continuous auto-regressive moving average random fields on ℝ-super-n," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 833-857, June.
    8. Basse-O’Connor, Andreas & Nielsen, Mikkel Slot & Pedersen, Jan & Rohde, Victor, 2019. "Multivariate stochastic delay differential equations and CAR representations of CARMA processes," Stochastic Processes and their Applications, Elsevier, vol. 129(10), pages 4119-4143.
    9. Benth, Fred Espen & Karbach, Sven, 2023. "Multivariate continuous-time autoregressive moving-average processes on cones," Stochastic Processes and their Applications, Elsevier, vol. 162(C), pages 299-337.
    10. Lingohr, Daniel & Müller, Gernot, 2019. "Stochastic modeling of intraday photovoltaic power generation," Energy Economics, Elsevier, vol. 81(C), pages 175-186.
    11. Vicky Fasen-Hartmann & Celeste Mayer, 2022. "Whittle estimation for continuous-time stationary state space models with finite second moments," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 233-270, April.
    12. Vicky Fasen‐Hartmann & Sebastian Kimmig, 2020. "Robust estimation of stationary continuous‐time arma models via indirect inference," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 620-651, September.
    13. Vladimir Andric & Sanja Nenadovic, 2024. "A note on the embeddability conditions in the case of integrated carma (2, 1) stochastic process with single and double zero roots," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 660-668, July.
    14. Reiichiro Kawai, 2017. "Sample Path Generation of Lévy-Driven Continuous-Time Autoregressive Moving Average Processes," Methodology and Computing in Applied Probability, Springer, vol. 19(1), pages 175-211, March.
    15. Chambers, MJ & McCrorie, JR & Thornton, MA, 2017. "Continuous Time Modelling Based on an Exact Discrete Time Representation," Economics Discussion Papers 20497, University of Essex, Department of Economics.
    16. Valerie Girardin & Rachid Senoussi, 2020. "Filling the gap between Continuous and Discrete Time Dynamics of Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 590-602, July.
    17. Sikora, Grzegorz & Michalak, Anna & Bielak, Łukasz & Miśta, Paweł & Wyłomańska, Agnieszka, 2019. "Stochastic modeling of currency exchange rates with novel validation techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1202-1215.
    18. Claudia Klüppelberg & Viet Son Pham, 2021. "Estimation of causal continuous‐time autoregressive moving average random fields," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 132-163, March.
    19. Szarek, Dawid & Bielak, Łukasz & Wyłomańska, Agnieszka, 2020. "Long-term prediction of the metals’ prices using non-Gaussian time-inhomogeneous stochastic process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    20. Peter J. Brockwell & Yasumasa Matsuda, 2015. "Levy-driven CARMA Random Fields on Rn," TERG Discussion Papers 339, Graduate School of Economics and Management, Tohoku University.

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