Skip to main content
Log in

A copula-based approximation to Markov chains

  • Articles
  • Published:
Science China Mathematics Aims and scope Submit manuscript

Abstract

The Markov chain is well studied and widely applied in many areas. For some Markov chains, it is infeasible to obtain the explicit expressions of their corresponding finite-dimensional distributions and sometimes it is time-consuming for computation. In this paper, we propose an approximation method for Markov chains by applying the copula theory. For this purpose, we first discuss the checkerboard copula-based Markov chain, which is the Markov chain generated by the family of checkerboard copulas. This Markov chain has some appealing properties, such as self-similarity in copulas and having explicit forms of finite-dimensional distributions. Then we prove that each Markov chain can be approximated by a sequence of checkerboard copula-based Markov chains, and the error bounds of the approximate distributions are provided. Employing the checkerboard copula-based approximation method, we propose a sufficient condition for the geometric β-mixing of copula-based Markov chains. This condition allows copulas of Markov chains to be asymmetric. Finally, by applying the approximation method, analytical recurrence formulas are also derived for computing approximate distributions of both the first passage time and the occupation time of a Markov chain, and numerical results are listed to show the approximation errors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Abourashchi N, Veretennikov A. On exponential bounds for mixing and the rate of convergence for Student processes. Theory Probab Math Statist, 2010, 81: 1–13

    Article  MathSciNet  MATH  Google Scholar 

  2. Aït-Sahalia Y. Maximum likelihood estimation of discretely sampled diffusions: A closed-form approximation approach. Econometrica, 2002, 70: 223–262

    Article  MathSciNet  MATH  Google Scholar 

  3. Aït-Sahalia Y, Yu J. Saddlepoint approximations for continuous-time Markov processes. J Econometrics, 2006, 134: 507–551

    Article  MathSciNet  MATH  Google Scholar 

  4. Beare B K. Copulas and temporal dependence. Econometrica, 2010, 78: 395–410

    Article  MathSciNet  MATH  Google Scholar 

  5. Beare B K, Seo J. Time irreversible copula-based Markov models. Econom Theory, 2014, 30: 923–960

    Article  MathSciNet  MATH  Google Scholar 

  6. Bradley R C. Basic properties of strong mixing conditions. A survey and some open questions. Probab Surv, 2005, 2: 107–144

    MATH  Google Scholar 

  7. Chen X, Fan Y. Estimation of copula-based semiparametric time series models. J Econometrics, 2006, 130: 307–335

    Article  MathSciNet  MATH  Google Scholar 

  8. Chen X, Wu W B, Yi Y. Efficient estimation of copula-based semiparametric Markov models. Ann Statist, 2009, 37: 4214–4253

    Article  MathSciNet  MATH  Google Scholar 

  9. Cherubini U, Mulinacci S, Gobbi F, et al. Dynamic Copula Methods in Finance. New York: John Wiley & Sons, 2011

    Book  MATH  Google Scholar 

  10. Clarkson J A, Adams C R. On definitions of bounded variation for functions of two variables. Trans Amer Math Soc, 1933, 35: 824–854

    Article  MathSciNet  MATH  Google Scholar 

  11. Darsow W F, Nguyen B, Olsen E T. Copulas and Markov processes. Illinois J Math, 1992, 36: 600–642

    Article  MathSciNet  MATH  Google Scholar 

  12. Duan J C, Dudley E, Gauthier G, et al. Pricing discretely monitored barrier options by a Markov chain. J Derivatives, 2003, 10: 9–31

    Article  Google Scholar 

  13. Durante F, Kolesarova A, Mesiar R, et al. Semilinear copulas. Fuzzy Sets Systems, 2008, 159: 63–76

    Article  MathSciNet  MATH  Google Scholar 

  14. Durante F, Sempi C. Principles of Copula Theory. Boca Raton: Chapman & Hall/CRC, 2015

    Book  MATH  Google Scholar 

  15. Durrett R. Probability: Theory and Examples, 4th ed. Cambridge: Cambridge University Press, 2010

    Book  MATH  Google Scholar 

  16. Gobbi F, Mulinacci S. Mixing and moments properties of a non-stationary copula-based Markov process. Comm Statist Theory Methods, 2020, 49: 4559–4570

    Article  MathSciNet  Google Scholar 

  17. González-Barrios J M, Hoyos-Argüelles R. Estimating checkerboard approximations with sample d-copulas. Comm Statist Simulation Comput, 2021, in press

  18. Hackbarth D, Miao J, Morellec E. Capital structure, credit risk, and macroeconomic conditions. J Financ Econ, 2006, 82: 519–550

    Article  Google Scholar 

  19. Ibragimov R. Copula-based characterizations for higher order Markov processes. Econom Theory, 2009, 25: 819–846

    Article  MathSciNet  MATH  Google Scholar 

  20. Janssen P, Swanepoel J, Veraverbeke N. Large sample behavior of the Bernstein copula estimator. J Statist Plann Inference, 2012, 142: 1189–1197

    Article  MathSciNet  MATH  Google Scholar 

  21. Kolesarova A, Mesiar R, Kalicka J. On a new construction of 1-Lipschitz aggregation functions, quasi-copulas and copulas. Fuzzy Sets Systems, 2013, 226: 19–31

    Article  MathSciNet  MATH  Google Scholar 

  22. Kullback S, Leibler R A. On information and sufficiency. Ann Math Statistics, 1951, 22: 79–86

    Article  MathSciNet  MATH  Google Scholar 

  23. Lagerås A N. Copulas for Markovian dependence. Bernoulli, 2010, 16: 331–342

    Article  MathSciNet  MATH  Google Scholar 

  24. Li X, Mikusiński P, Sherwood H, et al. On approximation of copulas. In: Distributions with Given Marginals and Moment Problems. Dordrecht: Springer, 1997, 107–116

    Chapter  Google Scholar 

  25. Liebscher E. Towards a unified approach for proving geometric ergodicity and mixing properties of nonlinear autoregressive processes. J Time Ser Anal, 2005, 26: 669–689

    Article  MathSciNet  MATH  Google Scholar 

  26. Loaiza-Maya R, Smith M S, Maneesoonthorn W. Time series copulas for heteroskedastic data. J Appl Econometrics, 2018, 33: 332–354

    Article  MathSciNet  Google Scholar 

  27. Longla M. On mixtures of copulas and mixing coefficients. J Multivariate Anal, 2015, 139: 259–265

    Article  MathSciNet  MATH  Google Scholar 

  28. Marshall A W, Olkin I, Arnold B C. Inequalities: Theory of Majorization and Its Applications. New York: Springer, 1979

    MATH  Google Scholar 

  29. Mesiar R, Komorníková M, Komorník J. Perturbation of bivariate copulas. Fuzzy Sets Systems, 2015, 268: 127–140

    Article  MathSciNet  MATH  Google Scholar 

  30. Meyn S P, Tweedie R L. Markov Chains and Stochastic Stability. New York: Springer, 1993

    Book  MATH  Google Scholar 

  31. Mikusiński P, Taylor M D. Markov operators and n-copulas. Ann Polon Math, 2009, 96: 75–95

    Article  MathSciNet  MATH  Google Scholar 

  32. Mikusiński P, Taylor M D. Some approximations of n-copulas. Metrika, 2010, 72: 385–414

    Article  MathSciNet  MATH  Google Scholar 

  33. Nelsen R B. An Introduction to Copulas, 2nd ed. New York: Springer, 2006

    MATH  Google Scholar 

  34. Nummelin E. General Irreducible Markov Chains and Non-Negative Operators. Cambridge: Cambridge University Press, 2004

    MATH  Google Scholar 

  35. Olsen E T, Darsow W F, Nguyen B. Copulas and Markov operators. In: Distributions with Fixed Marginals and Related Topics. Lecture Notes Monograph Series, vol. 28. Bethesda: Inst Math Statist, 1996, 244–259

    Chapter  Google Scholar 

  36. Ramsey J B, Rothman P. Time irreversibility and business cycle asymmetry. J Money Credit Bank, 1996, 28: 1–21

    Article  Google Scholar 

  37. Réemillard B, Papageorgiou N, Soustra F. Copula-based semiparametric models for multivariate time series. J Multi-variate Anal, 2012, 110: 30–42

    Article  MathSciNet  MATH  Google Scholar 

  38. Rolski T, Schmidli H, Schmidt V, et al. Stochastic Processes for Insurance and Finance. Chichester: John Wiley & Sons, 1999

    Book  MATH  Google Scholar 

  39. Sancetta A, Satchell S. The Bernstein copula and its applications to modeling and approximations of multivariate distributions. Econom Theory, 2004, 20: 535–562

    Article  MathSciNet  MATH  Google Scholar 

  40. Smith M, Min A, Almeida C, et al. Modeling longitudinal data using a pair-copula decomposition of serial dependence. J Amer Statist Assoc, 2010, 105: 1467–1479

    Article  MathSciNet  MATH  Google Scholar 

  41. Storesletten K, Telmer C I, Yaron A. Consumption and risk sharing over the life cycle. J Monetary Econom, 2004, 51: 609–633

    Article  Google Scholar 

  42. Tucker H G. A generalization of the Glivenko-Cantelli theorem. Ann Math Statist, 1959, 30: 828–830

    Article  MathSciNet  MATH  Google Scholar 

  43. Vassiliou P-C. Fuzzy semi-Markov migration process in credit risk. Fuzzy Sets Systems, 2013, 223: 39–58

    Article  MathSciNet  MATH  Google Scholar 

  44. Yang J, Cheng S, Zhang L. Bivariate copula decomposition in terms of comonotonicity, countermonotonicity and independence. Insurance Math Econom, 2006, 39: 267–284

    Article  MathSciNet  MATH  Google Scholar 

  45. Zheng Y, Yang J, Huang J Z. Approximation of bivariate copulas by patched bivariate Fréechet copulas. Insurance Math Econom, 2011, 48: 246–256

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The first and the third authors were supported by the National Key R&D Program of China (Grants No. 2018YFA0703900) and National Natural Science Foundation of China (Grants No. 11671021). The second author was supported by National Natural Science Foundation of China (Grants Nos. 11761051 and 11561047) and the Natural Science Foundation of Jiangxi Province (Grants Nos. 20181BAB211003 and 20192BAB211006). The authors thank two anonymous referees for their careful reading of the manuscript and helpful comments that led to an improved version.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiehua Xie.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Z., Xie, J. & Yang, J. A copula-based approximation to Markov chains. Sci. China Math. 65, 623–654 (2022). https://doi.org/10.1007/s11425-019-1687-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11425-019-1687-2

Keywords

MSC(2020)

Navigation