Log in

Nadaraya-Watson estimators for reflected stochastic processes

  • Published:
Acta Mathematica Scientia Aims and scope Submit manuscript

Abstract

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations. The estimates, based on either the continuously observed process or the discretely observed process, are considered. Under certain conditions, we prove the strong consistency and the asymptotic normality of the two estimators. Our method is also suitable for one-sided reflected stochastic differential equations. Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al. (Stat Sin, 2021, 31: 29–51). Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Bertola G, Svensson LEO. Stochastic devaluation risk and the empirical fit of target-zone models. Rev Econ Stud, 1993, 60: 689–712

    Article  MATH  Google Scholar 

  2. Budhiraja A, Lee C. Long time asymptotics for constrained diffusions in polyhedral domains. Stoch Process Their Appl, 2007, 117(8): 1014–1036

    Article  MathSciNet  MATH  Google Scholar 

  3. Bo L, Wang Y, Yang X. Some integral functionals of reflected SDEs and their applications in finance. Quant Financ, 2010, 11: 343–348

    Article  MathSciNet  MATH  Google Scholar 

  4. Bo L, Tang D, Wang Y, Yang X. On the conditional default probability in a regulated market: a structural approach. Quant Financ, 2011, 11: 1695–1702

    Article  MathSciNet  MATH  Google Scholar 

  5. Bo L, Wang Y, Yang X, Zhang G. Maximum likelihood estimation for reflected Ornstein-Uhlenbeck processes. J Stat Plan Infer, 2011, 141: 588–596

    Article  MathSciNet  MATH  Google Scholar 

  6. Bo L, Wang Y, Yang X. First passage times of (reflected) Ornstein-Uhlenbeck processes over a random jump boundaries. J Appl Prob, 2011, 48: 723–732

    Article  MathSciNet  MATH  Google Scholar 

  7. Cholaquidis A, Fraiman R, Mordecki E, Papalardo C. Level sets and drift estimation for reflected Brownian motion with drift. Stat Sin, 2021, 31: 29–51

    MathSciNet  MATH  Google Scholar 

  8. Fan J, Gijbels I. Variable bandwidth and local linear regression smoothers. Ann Statist, 1992, 20: 2008–2036

    Article  MathSciNet  MATH  Google Scholar 

  9. Fabienne C, Nicolas M. Nonparametric estimation in fractional SDE. Stat Infer Stoch Proc, 2019, 22: 359–382

    Article  MathSciNet  MATH  Google Scholar 

  10. Harrison J M. Brownian Motion and Stochastic Flow Systems. New York: Wiley, 1985

    MATH  Google Scholar 

  11. Harrison J M. Brownian Models of Performance and Control. New York: Cambridge University Press, 2013

    Book  MATH  Google Scholar 

  12. Hu Y, Lee C. Drift parameter estimation for a reflected fractional Brownian motion based on its local time. J Appl Probab, 2013, 50: 592–597

    Article  MathSciNet  MATH  Google Scholar 

  13. Hu Y, Lee C, Lee M H, Song J. Parameter estimation for reflected Ornstein-Uhlenbeck processes with discrete observations. Stat Infer Stoch Proc, 2015, 18: 279–291

    Article  MathSciNet  MATH  Google Scholar 

  14. Han Z, Hu Y, Lee C. Optimal pricing barriers in a regulated market using reflected diffusion processes. Quant Financ, 2016, 16: 639–647

    Article  MathSciNet  MATH  Google Scholar 

  15. Hu Y, ** Y. Estimation of all parameters in the reflected Ornstein-Uhlenbeck process from discrete observations. Stat Probab Lett, 2021, 174: 1–8

    Article  MathSciNet  MATH  Google Scholar 

  16. Karlin S, Taylor H M. A Second Course in Stochastic Processes. New York: Academic Press, 1981

    MATH  Google Scholar 

  17. Karatzas I, Shreve S E. Brownian Motion and Stochastic Calculus. New York: Springer, 1991

    MATH  Google Scholar 

  18. Krugman P R. Target zones and exchange rate dynamics. Q J Econ, 1991, 106: 669–682

    Article  Google Scholar 

  19. Lions P L, Sznitman A S. Stochastic differential equations with reflecting boundary conditions. Commun Pure Appl Math, 1984, 37: 511–537

    Article  MathSciNet  MATH  Google Scholar 

  20. Le**le D. Euler scheme for reflected stochastic differential equations. Math Comput Simul, 1995, 38: 119–126

    Article  MathSciNet  MATH  Google Scholar 

  21. Lee C, Bishwal J P N, Lee M H. Sequential maximum likelihood estimation for reflected Ornstein-Uhlenbeck processes. J Stat Plan Infer, 2012, 142: 1234–1242

    Article  MathSciNet  MATH  Google Scholar 

  22. Long H, Qian L. Nadaraya-Watson estimator for stochastic processes driven by stable Levy noises. Electron J Stat, 2013, 7: 1387–1418

    Article  MathSciNet  MATH  Google Scholar 

  23. Meyn S P, Tweedie R L. Markov Chains and Stochastic Stability. London: Springer, 1993

    Book  MATH  Google Scholar 

  24. Mishra M N, Prakasa Rao B L S. Nonparameteric estimation of trend for stochastic differential equations driven by fractional Brownian motion. Stat Infer Stoch Proc, 2011, 14: 101–109

    Article  MATH  Google Scholar 

  25. Nadaraya E A. On estimating regression. Theory Probab Appl, 1964, 9: 141–142

    Article  MATH  Google Scholar 

  26. Protter PE. Stochastic Integration and Differential Equations. Heidelberg: Springer, 2004

    MATH  Google Scholar 

  27. Ricciardi L M, Sacerdote L. On the probability densities of an Ornstein-Uhlenbeck process with a reflecting boundary. J Appl Probab, 1987, 24: 355–369

    Article  MathSciNet  MATH  Google Scholar 

  28. Saussereau B. Nonparametric inference for fractional diffusion. Bernoulli, 2014, 20: 878–918

    Article  MathSciNet  MATH  Google Scholar 

  29. Watson G S. Smooth regression analysis. Sankhya Ser A, 1964, 26: 359–372

    MathSciNet  MATH  Google Scholar 

  30. Whitt W. Stochastic-Process Limits. New York: Springer, 2002

    Book  MATH  Google Scholar 

  31. Ward A R, Glynn P W. A diffusion approximation for a Markovian queue with reneging. Queueing Syst, 2003, 43: 103–128

    Article  MathSciNet  MATH  Google Scholar 

  32. Ward A R, Glynn P W. Properties of the reflected Ornstein-Uhlenbeck process. Queueing Syst, 2003, 44: 109–123

    Article  MathSciNet  MATH  Google Scholar 

  33. Ward A R, Glynn P W. A diffusion approximation for a GI/GI/1 queue with balking or reneging. Queueing Syst, 2005, 50: 371–400

    Article  MathSciNet  MATH  Google Scholar 

  34. Wu Z, **ao H. Multi-dimensional reflected backward stochastic differential equations and the comparison theorem. Acta Math Sci, 2010, 30B: 1819–1836

    MathSciNet  MATH  Google Scholar 

  35. Zang Q, Zhang L. Parameter estimation for generalized diffusion processes with reflected boundary. Sci China-Math, 2016, 59: 1163–1174

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dingwen Zhang.

Ethics declarations

Conflict of Interest The authors declare no conflict of interest.

Additional information

This work was partially supported by the National Natural Science Foundation of China (11871244), and the Fundamental Research Funds for the Central Universities, JLU.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, Y., Zhang, D. Nadaraya-Watson estimators for reflected stochastic processes. Acta Math Sci 44, 143–160 (2024). https://doi.org/10.1007/s10473-024-0107-1

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10473-024-0107-1

Key words

2020 MR Subject Classification

Navigation