Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-27T13:11:54.712Z Has data issue: false hasContentIssue false

AN ASYMPTOTIC THEORY FOR JUMP DIFFUSION MODELS

Published online by Cambridge University Press:  02 April 2024

Minsoo Jeong
Affiliation:
Yonsei University
Joon Y. Park*
Affiliation:
Indiana University
*
Address correspondence to Joon Y. Park, Department of Economics, Indiana University, Bloomington, IN, United States, joon@indiana.edu
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper presents an asymptotic theory for recurrent jump diffusion models with well-defined scale functions. The class of such models is broad, including general nonstationary as well as stationary jump diffusions with state-dependent jump sizes and intensities. The asymptotics for recurrent jump diffusion models with scale functions are largely comparable to the asymptotics for the corresponding diffusion models without jumps. For stationary jump diffusions, our asymptotics yield the usual law of large numbers and the standard central limit theory with normal limit distributions. The asymptotics for nonstationary jump diffusions, on the other hand, are nonstandard and the limit distributions are given as generalized diffusion processes.

Type
ARTICLES
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Footnotes

We thank the editor, a co-editor, and two anonymous referees for many useful comments. We are also grateful for helpful discussions with Eric Renault, Yoosoon Chang, Jihyun Kim, Bin Wang, and the seminar participants at Yonsei, Indiana, Yale, Michigan State, Michigan, Queens, Penn State, LSE, and Oxford University. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5A8034332).

References

REFERENCES

Aït-Sahalia, Y., & Park, J. Y. (2012). Stationary-based specification tests for diffusions when the process is nonstationary. Journal of Econometrics , 169, 270292.CrossRefGoogle Scholar
Aït-Sahalia, Y., & Park, J. Y. (2016). Bandwidth selection and asymptotic properties of local nonparametric estimators in possibly nonstationary continuous-time models. Journal of Econometrics , 192, 119138.CrossRefGoogle Scholar
Applebaum, D. (2009). Lévy processes and stochastic calculus . Cambridge University Press.CrossRefGoogle Scholar
Bandi, F. M., & Nguyen, T. H. (2003). On the functional estimation of jump-diffusion models. Journal of Econometrics , 116, 293328.CrossRefGoogle Scholar
Bingham, N. H., Goldie, C. M., & Teugels, J. L. (1989). Regular variation . Cambridge University Press.Google Scholar
Duffie, D., Pan, J., & Singleton, K. (2000). Transform analysis and asset pricing for affine jump-diffusions. Econometrica , 68, 13431376.CrossRefGoogle Scholar
Hall, P., & Heyde, C. C. (1980). Martingale limit theory and its application . Academic Press.Google Scholar
Höpfner, R., & Kutoyants, Y. (2003). On a problem of statistical inference in null recurrent diffusions. Statistical Inference for Stochastic Processes , 6(1), 2542.CrossRefGoogle Scholar
Höpfner, R., & Löcherbach, E. (2003). Limit theorems for null recurrent Markov processes . American Mathematical Society.CrossRefGoogle Scholar
Jacod, J., & Shiryaev, A. (2003). Limit theorems for stochastic processes . Springer.CrossRefGoogle Scholar
Jeanblanc, M., Yor, M., & Chesney, M. (2009). Mathematical methods for financial markets . Springer-Verlag.CrossRefGoogle Scholar
Jeong, M., & Park, J. Y. (2013). Asymptotic theory of maximum likelihood estimator for diffusion model. Working paper, Indiana University.Google Scholar
Kim, J., & Park, J. Y. (2017). Asymptotics for recurrent diffusions with application to high frequency regression. Journal of Econometrics , 196, 3754.CrossRefGoogle Scholar
Küchler, U., & Sørensen, M. (1999). A note on limit theorems for multivariate martingales. Bernoulli , 5(3), 483493.CrossRefGoogle Scholar
Kurtz, T. G., & Protter, P. (1991). Weak limit theorems for stochastic integrals and stochastic differential equations. Annals of Probability , 19, 10351070.CrossRefGoogle Scholar
Lamperti, J. (1958). An occupation time theorem for a class of stochastic processes. Transactions of the American Mathematical Society , 88, 380387.CrossRefGoogle Scholar
Lin, C., & Segel, L. A. (1974). Mathematics applied to deterministic problems in the natural sciences . SIAM.Google Scholar
Maruyama, G., & Tanaka, H. (1959). Ergodic property of $n$ -dimensional recurrent Markov processes. Memoirs of the Faculty of Science, Kyushu University. Series A, Mathematics , 13(2), 157172.CrossRefGoogle Scholar
Menaldi, J.-L., & Robin, M. (1999). Invariant measure for diffusions with jumps. Applied Mathematics and Optimization , 40, 105140.CrossRefGoogle Scholar
Merton, R. C. (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics , 3, 125144.CrossRefGoogle Scholar
Panloup, F. (2008). Recursive computation of the invariant measure of a stochastic differential equation driven by a Lévy process. Annals of Applied Probability , 18, 379426.CrossRefGoogle Scholar
Protter, P. E. (2005). Stochastic integration and differential equations . Springer.CrossRefGoogle Scholar
Revuz, D., & Yor, M. (1999). Continuous martingales and Brownian motion . (3rd ed.) Springer.CrossRefGoogle Scholar
Soulier, P. (2009). Some applications of regular variation in probability and statistics . Escuela Venezolana de Matemáticas.Google Scholar
Wee, I.-S. (1999). Stability for multidimensional jump-diffusion processes. Stochastic Processes and Their Applications , 80, 193209.CrossRefGoogle Scholar
Zhang, X. (2011). Computing rare-event probabilities for affine models and general state space Markov processes [Ph.D. dissertation]. Stanford University.Google Scholar