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Michael Sørensen
(Michael Sorensen)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Mogens Bladt & Samuel Finch & Michael Sørensen, 2014. "Simulation of multivariate diffusion bridges," CREATES Research Papers 2014-16, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Martin Glanzer & Georg Ch. Pflug, 2020. "Multiscale stochastic optimization: modeling aspects and scenario generation," Computational Optimization and Applications, Springer, vol. 75(1), pages 1-34, January.
    2. Pierre E. Jacob & John O’Leary & Yves F. Atchadé, 2020. "Unbiased Markov chain Monte Carlo methods with couplings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 543-600, July.
    3. 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.

  2. Michael Sørensen, 2011. "Prediction-based estimating functions: review and new developments," CREATES Research Papers 2011-05, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Enrico Bibbona & Ilia Negri, 2015. "Higher Moments and Prediction-Based Estimation for the COGARCH(1,1) Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 891-910, December.
    2. Anne Brix & Asger Lunde, 2015. "Prediction-based estimating functions for stochastic volatility models with noisy data: comparison with a GMM alternative," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 433-465, October.
    3. Asger Lunde & Anne Floor Brix, 2013. "Estimating Stochastic Volatility Models using Prediction-based Estimating Functions," CREATES Research Papers 2013-23, Department of Economics and Business Economics, Aarhus University.

  3. Fernando Baltazar-Larios & Michael Sørensen, 2010. "Maximum likelihood estimation for integrated diffusion processes," CREATES Research Papers 2010-33, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Shu, Huisheng & Jiang, Ziwei & Zhang, Xuekang, 2023. "Parameter estimation for integrated Ornstein–Uhlenbeck processes with small Lévy noises," Statistics & Probability Letters, Elsevier, vol. 199(C).

  4. Michael Sørensen, 2008. "Efficient estimation for ergodic diffusions sampled at high frequency," CREATES Research Papers 2007-46, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Kengo Kamatani & Masayuki Uchida, 2015. "Hybrid multi-step estimators for stochastic differential equations based on sampled data," Statistical Inference for Stochastic Processes, Springer, vol. 18(2), pages 177-204, July.

  5. Bent Jesper Christensen & Michael Sørensen, 2008. "Optimal inference in dynamic models with conditional moment restrictions," CREATES Research Papers 2008-51, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465, September.
    2. Michael Sørensen, 2008. "Efficient estimation for ergodic diffusions sampled at high frequency," CREATES Research Papers 2007-46, Department of Economics and Business Economics, Aarhus University.
    3. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.

  6. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Yuichi Nagahara, 2008. "A Method of Calculating the Downside Risk by Multivariate Nonnormal Distributions," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 15(3), pages 175-184, December.

  7. Michael Sørensen & Julie Lyng Forman, 2007. "The Pearson diffusions: A class of statistically tractable diffusion processes," CREATES Research Papers 2007-28, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Rama Cont & Marvin S. Mueller, 2019. "A stochastic partial differential equation model for limit order book dynamics," Papers 1904.03058, arXiv.org, revised May 2021.
    2. Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
    3. Damir Filipovi'c & Martin Larsson, 2017. "Polynomial Jump-Diffusion Models," Papers 1711.08043, arXiv.org, revised Jul 2019.
    4. Nina Munkholt Jakobsen & Michael Sørensen, 2015. "Efficient Estimation for Diffusions Sampled at High Frequency Over a Fixed Time Interval," CREATES Research Papers 2015-33, Department of Economics and Business Economics, Aarhus University.
    5. Yuichi Nagahara, 2011. "Using Nonnormal Distributions to Analyze the Relationship Between Stock Returns in Japan and the US," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 18(4), pages 429-443, November.
    6. Hanson, Gordon H. & Lind, Nelson & Muendler, Marc-Andreas, 2015. "The Dynamics of Comparative Advantage," CAGE Online Working Paper Series 252, Competitive Advantage in the Global Economy (CAGE).
    7. Mesias Alfeus, 2019. "Stochastic Modelling of New Phenomena in Financial Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2019, January-A.
    8. Jean Jacod & Michael Sørensen, 2018. "A review of asymptotic theory of estimating functions," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 415-434, July.
    9. Giacomo Ascione & Nikolai Leonenko & Enrica Pirozzi, 2022. "Non-local Solvable Birth–Death Processes," Journal of Theoretical Probability, Springer, vol. 35(2), pages 1284-1323, June.
    10. Pierre Blanc & Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Quadratic Hawkes processes for financial prices," Papers 1509.07710, arXiv.org.
    11. Bercu, Bernard & Richou, Adrien, 2017. "Large deviations for the Ornstein–Uhlenbeck process without tears," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 45-55.
    12. Ernstsen, Rune Ramsdal & Boomsma, Trine Krogh, 2018. "Valuation of power plants," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1153-1174.
    13. Damir Filipović & Martin Larsson, 2016. "Polynomial diffusions and applications in finance," Finance and Stochastics, Springer, vol. 20(4), pages 931-972, October.
    14. Aleksandar Mijatovic & Paul Schneider, 2009. "Empirical asset pricing with nonlinear risk premia," Papers 0911.0928, arXiv.org.
    15. Sutthimat, Phiraphat & Mekchay, Khamron & Rujivan, Sanae, 2022. "Closed-form formula for conditional moments of generalized nonlinear drift CEV process," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    16. Di Nardo, Elvira & D'Onofrio, Giuseppe & Martini, Tommaso, 2024. "Orthogonal gamma-based expansion for the CIR's first passage time distribution," Applied Mathematics and Computation, Elsevier, vol. 480(C).
    17. Amorino, Chiara & Heidari, Akram & Pilipauskaitė, Vytautė & Podolskij, Mark, 2023. "Parameter estimation of discretely observed interacting particle systems," Stochastic Processes and their Applications, Elsevier, vol. 163(C), pages 350-386.
    18. Rama Cont & Marvin Muller, 2019. "A Stochastic Pde Model For Limit Order Book Dynamics," Working Papers hal-02090449, HAL.
    19. Mar'ia Fernanda del Carmen Agoitia Hurtado & Thorsten Schmidt, 2018. "Time-inhomogeneous polynomial processes," Papers 1806.03887, arXiv.org.
    20. Choi, Michael C.H. & Li, Evelyn, 2019. "A Hoeffding’s inequality for uniformly ergodic diffusion process," Statistics & Probability Letters, Elsevier, vol. 150(C), pages 23-28.
    21. Leonenko, N.N. & Papić, I. & Sikorskii, A. & Šuvak, N., 2017. "Heavy-tailed fractional Pearson diffusions," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3512-3535.
    22. Levene, Mark & Fenner, Trevor, 2021. "A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1227-1234.
    23. Christa Cuchiero & Martin Keller-Ressel & Josef Teichmann, 2012. "Polynomial processes and their applications to mathematical finance," Finance and Stochastics, Springer, vol. 16(4), pages 711-740, October.
    24. Damir Filipovi'c & Eberhard Mayerhofer & Paul Schneider, 2011. "Density Approximations for Multivariate Affine Jump-Diffusion Processes," Papers 1104.5326, arXiv.org, revised Oct 2011.
    25. A. M. Kulik & N. N. Leonenko & I. Papić & N. Šuvak, 2020. "Parameter Estimation for Non-Stationary Fisher-Snedecor Diffusion," Methodology and Computing in Applied Probability, Springer, vol. 22(3), pages 1023-1061, September.
    26. Elvira Di Nardo & Giuseppe D’Onofrio, 2021. "On the Cumulants of the First Passage Time of the Inhomogeneous Geometric Brownian Motion," Mathematics, MDPI, vol. 9(9), pages 1-17, April.
    27. Yuichi Nagahara, 2008. "A Method of Calculating the Downside Risk by Multivariate Nonnormal Distributions," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 15(3), pages 175-184, December.
    28. Di Nardo, Elvira & D’Onofrio, Giuseppe, 2021. "A cumulant approach for the first-passage-time problem of the Feller square-root process," Applied Mathematics and Computation, Elsevier, vol. 391(C).
    29. Larsson, Martin & Pulido, Sergio, 2017. "Polynomial diffusions on compact quadric sets," Stochastic Processes and their Applications, Elsevier, vol. 127(3), pages 901-926.
    30. Cuchiero, Christa & Di Persio, Luca & Guida, Francesco & Svaluto-Ferro, Sara, 2024. "Measure-valued affine and polynomial diffusions," Stochastic Processes and their Applications, Elsevier, vol. 175(C).
    31. Ahmed Nafidi & Ilyasse Makroz & Ramón Gutiérrez Sánchez, 2021. "A Stochastic Lomax Diffusion Process: Statistical Inference and Application," Mathematics, MDPI, vol. 9(1), pages 1-9, January.
    32. Asger Lunde & Anne Floor Brix, 2013. "Estimating Stochastic Volatility Models using Prediction-based Estimating Functions," CREATES Research Papers 2013-23, Department of Economics and Business Economics, Aarhus University.
    33. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
    34. Christa Cuchiero & Francesco Guida & Luca di Persio & Sara Svaluto-Ferro, 2021. "Measure-valued affine and polynomial diffusions," Papers 2112.15129, arXiv.org.
    35. Giorgos Sermaidis & Omiros Papaspiliopoulos & Gareth O. Roberts & Alexandros Beskos & Paul Fearnhead, 2013. "Markov Chain Monte Carlo for Exact Inference for Diffusions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(2), pages 294-321, June.

  8. Leah Kelly & Eckhard Platen & Michael Sorensen, 2003. "Estimating for Discretely Observed Diffusions Using Transform Functions," Research Paper Series 96, Quantitative Finance Research Centre, University of Technology, Sydney.

    Cited by:

    1. Wolfgang Breymann & Leah Kelly & Eckhard Platen, 2004. "Intraday Empirical Analysis and Modeling of Diversified World Stock Indices," Research Paper Series 125, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Eckhard Platen & Renata Rendek, 2007. "Empirical Evidence on Student-t Log-Returns of Diversified World Stock Indices," Research Paper Series 194, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. Xiao Huang, 2011. "Quasi‐maximum likelihood estimation of discretely observed diffusions," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 241-256, July.
    4. Michael Sørensen, 2008. "Efficient estimation for ergodic diffusions sampled at high frequency," CREATES Research Papers 2007-46, Department of Economics and Business Economics, Aarhus University.
    5. Leah Kelly, 2004. "Inference and Intraday Analysis of Diversified World Stock Indices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2004, January-A.
    6. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
    7. Huang Xiao, 2013. "Quasi-maximum likelihood estimation of multivariate diffusions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 179-197, April.

  9. P. E. Kloeden & Eckhard Platen & H. Schurz & M. Sørensen, 1996. "On effects of discretization on estimators of drift parameters for diffusion processes," Published Paper Series 1996-2, Finance Discipline Group, UTS Business School, University of Technology, Sydney.

    Cited by:

    1. C. H. Skiadas & A. N. Giovanis, 1997. "A stochastic Bass innovation diffusion model for studying the growth of electricity consumption in Greece," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 13(2), pages 85-101, June.
    2. Óscar Cornejo & Sebastián Muñoz-Herrera & Felipe Baesler & Rodrigo Rebolledo, 2023. "The Application of the Random Time Transformation Method to Estimate Richards Model for Tree Growth Prediction," Mathematics, MDPI, vol. 11(20), pages 1-19, October.
    3. Ahmed Nafidi & Ghizlane Moutabir & Ramón Gutiérrez-Sánchez, 2019. "Stochastic Brennan–Schwartz Diffusion Process: Statistical Computation and Application," Mathematics, MDPI, vol. 7(11), pages 1-16, November.
    4. Kim, Myung Suk & Wang, Suojin, 2006. "Sizes of two bootstrap-based nonparametric specification tests for the drift function in continuous time models," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1793-1806, April.
    5. Fan J. & Zhang C., 2003. "A Reexamination of Diffusion Estimators With Applications to Financial Model Validation," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 118-134, January.
    6. Nafidi, A. & Gutiérrez, R. & Gutiérrez-Sánchez, R. & Ramos-Ábalos, E. & El Hachimi, S., 2016. "Modelling and predicting electricity consumption in Spain using the stochastic Gamma diffusion process with exogenous factors," Energy, Elsevier, vol. 113(C), pages 309-318.
    7. Ahmed Nafidi & Ghizlane Moutabir & Ramón Gutiérrez-Sánchez & Eva Ramos-Ábalos, 2020. "Stochastic Square of the Brennan-Schwartz Diffusion Process: Statistical Computation and Application," Methodology and Computing in Applied Probability, Springer, vol. 22(2), pages 455-476, June.
    8. Cleur, Eugene M & Manfredi, Piero, 1999. "One Dimensional SDE Models, Low Order Numerical Methods and Simulation Based Estimation: A Comparison of Alternative Estimators," Computational Economics, Springer;Society for Computational Economics, vol. 13(2), pages 177-197, April.
    9. Jianqing Fan, 2004. "A selective overview of nonparametric methods in financial econometrics," Papers math/0411034, arXiv.org.
    10. Martin J. Lenardon & Anna Amirdjanova, 2006. "Interaction between stock indices via changepoint analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(5‐6), pages 573-586, September.
    11. Kim, Myung Suk & Wang, Suojin, 2008. "Consistent estimation in regression models for the drift function in some continuous time models," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2682-2691, January.
    12. Ramaprasad Bhar, 2010. "Stochastic Filtering with Applications in Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 7736, August.
    13. Yvo Pokern & Andrew M. Stuart & Petter Wiberg, 2009. "Parameter estimation for partially observed hypoelliptic diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 49-73, January.
    14. Suk Kim, Myung & Wang, Suojin, 2006. "On the applicability of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2210-2217, December.
    15. Ramaprasad Bhar & Carl Chiarella, 1997. "Interest rate futures: estimation of volatility parameters in an arbitrage-free framework," Applied Mathematical Finance, Taylor & Francis Journals, vol. 4(4), pages 181-199.
    16. Cysne, Rubens Penha, 2004. "On the Statistical Estimation of Diffusion Processes: A Partial Survey," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 24(2), November.
    17. A. S. Hurn & K. A. Lindsay & V. L. Martin, 2003. "On the efficacy of simulated maximum likelihood for estimating the parameters of stochastic differential Equations," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 45-63, January.
    18. João Nicolau, 2002. "A new technique for simulating the likelihood of stochastic differential equations," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 91-103, June.
    19. Fan, Jianqing & Fan, Yingying & Jiang, Jiancheng, 2007. "Dynamic Integration of Time- and State-Domain Methods for Volatility Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 618-631, June.
    20. Cysne, Rubens Penha, 2004. "On the statistical estimation of diffusion processes: a survey," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 540, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).

Articles

  1. Jean Jacod & Michael Sørensen, 2018. "A review of asymptotic theory of estimating functions," Statistical Inference for Stochastic Processes, Springer, vol. 21(2), pages 415-434, July.

    Cited by:

    1. Jakobsen, Nina Munkholt & Sørensen, Michael, 2019. "Estimating functions for jump–diffusions," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3282-3318.
    2. Frédéric Lavancier & Arnaud Poinas & Rasmus Waagepetersen, 2021. "Adaptive estimating function inference for nonstationary determinantal point processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 87-107, March.

  2. Mogens Bladt & Samuel Finch & Michael Sørensen, 2016. "Simulation of multivariate diffusion bridges," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 343-369, March.
    See citations under working paper version above.
  3. Uwe Küchler & Michael Sørensen, 2010. "A simple estimator for discrete-time samples from affine stochastic delay differential equations," Statistical Inference for Stochastic Processes, Springer, vol. 13(2), pages 125-132, June.

    Cited by:

    1. Becker, Christoph & Schmidt, Wolfgang M., 2013. "Stressing correlations and volatilities — A consistent modeling approach," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 174-194.
    2. Zongwu Cai & Hongwei Mei & Rui Wang, 2024. "A model specification test for nonlinear stochastic diffusions with delay," Statistical Inference for Stochastic Processes, Springer, vol. 27(3), pages 795-812, October.

  4. Mogens Bladt & Michael SØrensen, 2009. "Efficient estimation of transition rates between credit ratings from observations at discrete time points," Quantitative Finance, Taylor & Francis Journals, vol. 9(2), pages 147-160.

    Cited by:

    1. Linda Möstel & Marius Pfeuffer & Matthias Fischer, 2020. "Statistical inference for Markov chains with applications to credit risk," Computational Statistics, Springer, vol. 35(4), pages 1659-1684, December.
    2. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    3. Lapshin, Viktor & Anton, Markov, 2022. "MCMC-based credit rating aggregation algorithm to tackle data insufficiency," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 50-72.
    4. Xiaofeng Nie & Tamer Boyacı & Mehmet Gümüş & Saibal Ray & Dan Zhang, 2017. "Joint procurement and demand-side bidding strategies under price volatility," Annals of Operations Research, Springer, vol. 257(1), pages 121-165, October.
    5. Voß, Sebastian & Weißbach, Rafael, 2014. "A score-test on measurement errors in rating transition times," Journal of Econometrics, Elsevier, vol. 180(1), pages 16-29.
    6. Alexander Kremer & Rafael Weißbach, 2013. "Consistent estimation for discretely observed Markov jump processes with an absorbing state," Statistical Papers, Springer, vol. 54(4), pages 993-1007, November.
    7. Greig Smith & Goncalo dos Reis, 2017. "Robust and Consistent Estimation of Generators in Credit Risk," Papers 1702.08867, arXiv.org, revised Oct 2017.
    8. Marius Pfeuffer & Goncalo dos Reis & Greig smith, 2018. "Capturing Model Risk and Rating Momentum in the Estimation of Probabilities of Default and Credit Rating Migrations," Papers 1809.09889, arXiv.org, revised Feb 2020.
    9. Kremer, Alexander & Weißbach, Rafael, 2014. "Asymptotic normality for discretely observed Markov jump processes with an absorbing state," Statistics & Probability Letters, Elsevier, vol. 90(C), pages 136-139.
    10. David Azriel & Paul D. Feigin & Avishai Mandelbaum, 2019. "Erlang-S: A Data-Based Model of Servers in Queueing Networks," Management Science, INFORMS, vol. 65(10), pages 4607-4635, October.

  5. Gloter, Arnaud & Sørensen, Michael, 2009. "Estimation for stochastic differential equations with a small diffusion coefficient," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 679-699, March.

    Cited by:

    1. Ma, Chunhua & Yang, Xu, 2014. "Small noise fluctuations of the CIR model driven by α-stable noises," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 1-11.
    2. Long, Hongwei & Ma, Chunhua & Shimizu, Yasutaka, 2017. "Least squares estimators for stochastic differential equations driven by small Lévy noises," Stochastic Processes and their Applications, Elsevier, vol. 127(5), pages 1475-1495.
    3. Ren, Panpan & Wu, Jiang-Lun, 2021. "Least squares estimation for path-distribution dependent stochastic differential equations," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    4. Alessandro Gregorio & Francesco Iafrate, 2021. "Regularized bridge-type estimation with multiple penalties," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(5), pages 921-951, October.
    5. Ma, Chunhua, 2010. "A note on "Least squares estimator for discretely observed Ornstein-Uhlenbeck processes with small Lévy noises"," Statistics & Probability Letters, Elsevier, vol. 80(19-20), pages 1528-1531, October.
    6. Anna Melnykova, 2020. "Parametric inference for hypoelliptic ergodic diffusions with full observations," Statistical Inference for Stochastic Processes, Springer, vol. 23(3), pages 595-635, October.
    7. Guangjun Shen & Qian Yu, 2019. "Least squares estimator for Ornstein–Uhlenbeck processes driven by fractional Lévy processes from discrete observations," Statistical Papers, Springer, vol. 60(6), pages 2253-2271, December.
    8. De Gregorio, A. & Iacus, S.M., 2013. "On a family of test statistics for discretely observed diffusion processes," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 292-316.
    9. Yasutaka Shimizu, 2017. "Threshold Estimation for Stochastic Processes with Small Noise," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 951-988, December.
    10. Genon-Catalot, Valentine & Larédo, Catherine, 2021. "Probabilistic properties and parametric inference of small variance nonlinear self-stabilizing stochastic differential equations," Stochastic Processes and their Applications, Elsevier, vol. 142(C), pages 513-548.
    11. Long, Hongwei & Shimizu, Yasutaka & Sun, Wei, 2013. "Least squares estimators for discretely observed stochastic processes driven by small Lévy noises," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 422-439.
    12. Tetsuya Kawai & Masayuki Uchida, 2023. "Adaptive inference for small diffusion processes based on sampled data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(6), pages 643-696, August.
    13. Mitsuki Kobayashi & Yasutaka Shimizu, 2023. "Threshold estimation for jump-diffusions under small noise asymptotics," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 361-411, July.
    14. Yang, Xu, 2017. "Maximum likelihood type estimation for discretely observed CIR model with small α-stable noises," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 18-27.
    15. Guy, Romain & Larédo, Catherine & Vergu, Elisabeta, 2014. "Parametric inference for discretely observed multidimensional diffusions with small diffusion coefficient," Stochastic Processes and their Applications, Elsevier, vol. 124(1), pages 51-80.

  6. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465, September.
    See citations under working paper version above.
  7. Kristian Stegenborg Larsen & Michael Sørensen, 2007. "Diffusion Models For Exchange Rates In A Target Zone," Mathematical Finance, Wiley Blackwell, vol. 17(2), pages 285-306, April.

    Cited by:

    1. Hui, Cho-Hoi & Lo, Chi-Fai & Fong, Tom Pak-Wing, 2016. "Swiss franc's one-sided target zone during 2011–2015," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 54-67.
    2. Feng, Liming & Jiang, Pingping & Wang, Yongjin, 2020. "Constant elasticity of variance models with target zones," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Taiga Saito, 2016. "Pricing Foreign Exchange Options Under Intervention by Absorption Modeling," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 23(1), pages 85-106, March.
    4. Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
    5. Mordecai Avriel & Jens Hilscher & Alon Raviv, 2012. "Inflation Derivatives Under Inflation Target Regimes," Working Papers 43, Brandeis University, Department of Economics and International Business School.
    6. Damir Filipovi'c & Martin Larsson, 2017. "Polynomial Jump-Diffusion Models," Papers 1711.08043, arXiv.org, revised Jul 2019.
    7. Nina Munkholt Jakobsen & Michael Sørensen, 2015. "Efficient Estimation for Diffusions Sampled at High Frequency Over a Fixed Time Interval," CREATES Research Papers 2015-33, Department of Economics and Business Economics, Aarhus University.
    8. Clio Ciaschini & Kateryna Tkach & Francesca Mariani & Maria Cristina Recchioni, 2019. "Speculative bubbles in agricultural commodity prices: detection and forecasting via market indicators," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 73(2), pages 63-73, April-Jun.
    9. Chen, Yu-Fu & Funke, Michael & Glanemann, Nicole, 2012. "The Signalling Channel of Central Bank Interventions: Modelling the Yen/US Dollar Exchange Rate," SIRE Discussion Papers 2012-36, Scottish Institute for Research in Economics (SIRE).
    10. C. F. Lo & C. H. Hui & S. W. Chu & T. Fong, 2012. "A Quasi-Bounded Target Zone Model - Theory and Application to Hong Kong Dollar," Working Papers 282012, Hong Kong Institute for Monetary Research.
    11. Filipović, Damir & Larsson, Martin & Pulido, Sergio, 2020. "Markov cubature rules for polynomial processes," Stochastic Processes and their Applications, Elsevier, vol. 130(4), pages 1947-1971.
    12. Damir Filipović & Martin Larsson, 2016. "Polynomial diffusions and applications in finance," Finance and Stochastics, Springer, vol. 20(4), pages 931-972, October.
    13. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465, September.
    14. C. H. Hui & C. F. Lo & T. Fong, 2015. "A Quasi-Bounded Model for Swiss Franc's One-Sided Target Zone During 2011-2015," Working Papers 152015, Hong Kong Institute for Monetary Research.
    15. Cho-Hoi Hui & Chi-Fai Lo & Po-Hon Chau, 2017. "Can Exchange Rate Dynamics in Krugman¡¯s Target-zone Model be Directly Tested?Abstract: Despite Krugman's (1991) model being a benchmark for modelling target zones, empirical support has been sparse d," Working Papers 032017, Hong Kong Institute for Monetary Research.
    16. Tim Leung & Xin Li, 2014. "Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit," Papers 1411.5062, arXiv.org, revised May 2015.
    17. Liao Wang & David D. Yao, 2021. "Risk Hedging for Production Planning," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1825-1837, June.
    18. Nicolas Bouleau & Christophe Chorro, 2015. "The impact of randomness on the distribution of wealth: Some economic aspects of the Wright-Fisher diffusion process," Post-Print halshs-01162452, HAL.
    19. Christian Gouriéroux & Eric Renault & Pascale Valery, 2007. "Diffusion Processes with Polynomial Eigenfunctions," Annals of Economics and Statistics, GENES, issue 85, pages 115-130.
    20. Choi, Michael C.H. & Li, Evelyn, 2019. "A Hoeffding’s inequality for uniformly ergodic diffusion process," Statistics & Probability Letters, Elsevier, vol. 150(C), pages 23-28.
    21. Vadim Linetsky, 2004. "The Spectral Decomposition Of The Option Value," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 337-384.
    22. Weiwei Guo & Lingfei Li, 2019. "Parametric inference for discretely observed subordinate diffusions," Statistical Inference for Stochastic Processes, Springer, vol. 22(1), pages 77-110, April.
    23. Giorgio Ferrari & Tiziano Vargiolu, 2020. "On the singular control of exchange rates," Annals of Operations Research, Springer, vol. 292(2), pages 795-832, September.
    24. Andreas Neuenkirch & Lukasz Szpruch, 2012. "First order strong approximations of scalar SDEs with values in a domain," Papers 1209.0390, arXiv.org.
    25. Bouleau, Nicolas & Chorro, Christophe, 2017. "The impact of randomness on the distribution of wealth: Some economic aspects of the Wright–Fisher diffusion process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 379-395.
    26. Peter P. Carr & Zura Kakushadze, 2017. "FX options in target zones," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1477-1486, October.
    27. Park, Ki Young & Kim, Soohyon, 2019. "Detecting currency manipulation: An application of a state-space model with Markov switching," Japan and the World Economy, Elsevier, vol. 49(C), pages 50-60.
    28. Larsson, Martin & Pulido, Sergio, 2017. "Polynomial diffusions on compact quadric sets," Stochastic Processes and their Applications, Elsevier, vol. 127(3), pages 901-926.
    29. Ferrari, Giorgio, 2018. "On a Class of Singular Stochastic Control Problems for Reflected Diffusions," Center for Mathematical Economics Working Papers 592, Center for Mathematical Economics, Bielefeld University.
    30. Hui, Cho-Hoi & Lo, Chi-Fai & Liu, Chi-Hei, 2022. "Exchange rate dynamics with crash risk and interventions," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 18-37.
    31. Zhenxi Chen & Thomas Lux, 2018. "Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 711-744, October.
    32. Nicolas Bouleau & Christophe Chorro, 2017. "The impact of randomness on the distribution of wealth: Some economic aspects of the Wright-Fisher diffusion process," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01138383, HAL.
    33. Jeff Hamrick & Murad Taqqu, 2009. "Testing diffusion processes for non-stationarity," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 509-551, July.
    34. Damir Filipovi'c & Martin Larsson & Sergio Pulido, 2017. "Markov cubature rules for polynomial processes," Papers 1707.06849, arXiv.org, revised Jun 2019.
    35. Nicolas Bouleau & Christophe Chorro, 2015. "The impact of randomness on the distribution of wealth: Some economic aspects of the Wright-Fisher diffusion process," Documents de travail du Centre d'Economie de la Sorbonne 15024r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jul 2015.
    36. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
    37. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.
    38. Hui, Cho-Hoi & Lo, Chi-Fai & Chau, Po-Hon & Wong, Andrew, 2020. "Does Bitcoin behave as a currency?: A standard monetary model approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
    39. Thomas Krabichler & Josef Teichmann, 2020. "The Jarrow & Turnbull setting revisited," Papers 2004.12392, arXiv.org.
    40. Cho-Hoi Hui & Chi-Fai Lo & Chi-Hei Liu, 2023. "Equity Price Dynamics under Shocks: In Distress or Short Squeeze," Risks, MDPI, vol. 12(1), pages 1-19, December.
    41. Nicolas Bouleau & Christophe Chorro, 2017. "The impact of randomness on the distribution of wealth: Some economic aspects of the Wright-Fisher diffusion process," Post-Print hal-01138383, HAL.

  8. Mogens Bladt & Michael Sørensen, 2005. "Statistical inference for discretely observed Markov jump processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 395-410, June.

    Cited by:

    1. Alan Riva-Palacio & Ramsés H. Mena & Stephen G. Walker, 2023. "On the estimation of partially observed continuous-time Markov chains," Computational Statistics, Springer, vol. 38(3), pages 1357-1389, September.
    2. Yu Luo & David A. Stephens & Aman Verma & David L. Buckeridge, 2021. "Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records," Biometrics, The International Biometric Society, vol. 77(1), pages 78-90, March.
    3. Linda Möstel & Marius Pfeuffer & Matthias Fischer, 2020. "Statistical inference for Markov chains with applications to credit risk," Computational Statistics, Springer, vol. 35(4), pages 1659-1684, December.
    4. Mogens Bladt & Michael SØrensen, 2009. "Efficient estimation of transition rates between credit ratings from observations at discrete time points," Quantitative Finance, Taylor & Francis Journals, vol. 9(2), pages 147-160.
    5. Lapshin, Viktor & Anton, Markov, 2022. "MCMC-based credit rating aggregation algorithm to tackle data insufficiency," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 50-72.
    6. D'Amico, Guglielmo & Singh, Shakti & Selvamuthu, Dharmaraja, 2024. "Optimal investment-disinvestment choices in health-dependent variable annuity," Insurance: Mathematics and Economics, Elsevier, vol. 117(C), pages 1-15.
    7. Guglielmo D'Amico & Riccardo De Blasis & Philippe Regnault, 2020. "Confidence sets for dynamic poverty indexes," Papers 2006.06595, arXiv.org.
    8. Yong Chen & Jianmin Chen, 2011. "On the Imbedding Problem for Three-State Time Homogeneous Markov Chains with Coinciding Negative Eigenvalues," Journal of Theoretical Probability, Springer, vol. 24(4), pages 928-938, December.
    9. Guglielmo D’Amico & Philippe Regnault, 2018. "Dynamic Measurement of Poverty: Modeling and Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 305-340, November.
    10. Yasunari Inamura, 2006. "Estimating Continuous Time Transition Matrices From Discretely Observed Data," Bank of Japan Working Paper Series 06-E-7, Bank of Japan.
    11. Jia, Chen, 2016. "A solution to the reversible embedding problem for finite Markov chains," Statistics & Probability Letters, Elsevier, vol. 116(C), pages 122-130.
    12. Voß, Sebastian & Weißbach, Rafael, 2014. "A score-test on measurement errors in rating transition times," Journal of Econometrics, Elsevier, vol. 180(1), pages 16-29.
    13. Alexander Kremer & Rafael Weißbach, 2013. "Consistent estimation for discretely observed Markov jump processes with an absorbing state," Statistical Papers, Springer, vol. 54(4), pages 993-1007, November.
    14. Greig Smith & Goncalo dos Reis, 2017. "Robust and Consistent Estimation of Generators in Credit Risk," Papers 1702.08867, arXiv.org, revised Oct 2017.
    15. R. A. Hubbard & L. Y. T. Inoue & J. R. Fann, 2008. "Modeling Nonhomogeneous Markov Processes via Time Transformation," Biometrics, The International Biometric Society, vol. 64(3), pages 843-850, September.
    16. Ross, J.V. & Pagendam, D.E. & Pollett, P.K., 2009. "On parameter estimation in population models II: Multi-dimensional processes and transient dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 123-132.
    17. Huang, Jia-Ping & Sumita, Ushio, 2015. "Development of computational algorithms for pricing European bond options under the influence of macro-economic conditions," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 453-468.
    18. Marius Pfeuffer & Goncalo dos Reis & Greig smith, 2018. "Capturing Model Risk and Rating Momentum in the Estimation of Probabilities of Default and Credit Rating Migrations," Papers 1809.09889, arXiv.org, revised Feb 2020.
    19. Kremer, Alexander & Weißbach, Rafael, 2014. "Asymptotic normality for discretely observed Markov jump processes with an absorbing state," Statistics & Probability Letters, Elsevier, vol. 90(C), pages 136-139.
    20. Azam Asanjarani & Yoni Nazarathy & Peter Taylor, 2021. "A survey of parameter and state estimation in queues," Queueing Systems: Theory and Applications, Springer, vol. 97(1), pages 39-80, February.
    21. David Azriel & Paul D. Feigin & Avishai Mandelbaum, 2019. "Erlang-S: A Data-Based Model of Servers in Queueing Networks," Management Science, INFORMS, vol. 65(10), pages 4607-4635, October.
    22. Guglielmo D’Amico & Shakti Singh & Dharmaraja Selvamuthu, 2023. "Analysis of fair fee in guaranteed lifelong withdrawal and Markovian health benefits," Annals of Finance, Springer, vol. 19(3), pages 383-400, September.

  9. Susanne Ditlevsen & Michael Sørensen, 2004. "Inference for Observations of Integrated Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(3), pages 417-429, September.

    Cited by:

    1. Comte, F. & Genon-Catalot, V. & Rozenholc, Y., 2009. "Nonparametric adaptive estimation for integrated diffusions," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 811-834, March.
    2. Jean Jacod & Mark Podolskij, 2012. "A Test for the Rank of the Volatility Process: The Random Perturbation Approach," Global COE Hi-Stat Discussion Paper Series gd12-268, Institute of Economic Research, Hitotsubashi University.
    3. Song, Yuping & Lin, Zhengyan, 2013. "Empirical likelihood inference for the second-order jump-diffusion model," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 184-195.
    4. Jean Jacod & Mark Podolskij, 2012. "A test for the rank of the volatility process: the random perturbation approach," CREATES Research Papers 2012-57, Department of Economics and Business Economics, Aarhus University.
    5. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465, September.
    6. Nicolau, João, 2008. "Modeling financial time series through second-order stochastic differential equations," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2700-2704, November.
    7. Samson, Adeline & Thieullen, Michèle, 2012. "A contrast estimator for completely or partially observed hypoelliptic diffusion," Stochastic Processes and their Applications, Elsevier, vol. 122(7), pages 2521-2552.
    8. Friedrich Hubalek & Petra Posedel, 2008. "Asymptotic analysis for a simple explicit estimator in Barndorff-Nielsen and Shephard stochastic volatility models," Papers 0807.3479, arXiv.org.
    9. Quentin Clairon & Adeline Samson, 2022. "Optimal control for parameter estimation in partially observed hypoelliptic stochastic differential equations," Computational Statistics, Springer, vol. 37(5), pages 2471-2491, November.
    10. Susanne Ditlevsen & Adeline Samson, 2019. "Hypoelliptic diffusions: filtering and inference from complete and partial observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 361-384, April.
    11. Shu, Huisheng & Jiang, Ziwei & Zhang, Xuekang, 2023. "Parameter estimation for integrated Ornstein–Uhlenbeck processes with small Lévy noises," Statistics & Probability Letters, Elsevier, vol. 199(C).
    12. Blanke, Delphine & Vial, Céline, 2008. "Assessing the number of mean square derivatives of a Gaussian process," Stochastic Processes and their Applications, Elsevier, vol. 118(10), pages 1852-1869, October.
    13. Song Yuping & Hou Weijie & Zhou Shengyi, 2019. "Variance reduction estimation for return models with jumps using gamma asymmetric kernels," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(5), pages 1-38, December.
    14. Yunyan Wang & Lixin Zhang & Mingtian Tang, 2012. "Re-weighted functional estimation of second-order diffusion processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1129-1151, November.

  10. Bo Martin Bibby & Michael Sørensen, 2001. "Simplified Estimating Functions for Diffusion Models with a High‐dimensional Parameter," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(1), pages 99-112, March.

    Cited by:

    1. Kusuoka, Seiichiro & Tudor, Ciprian A., 2012. "Stein’s method for invariant measures of diffusions via Malliavin calculus," Stochastic Processes and their Applications, Elsevier, vol. 122(4), pages 1627-1651.
    2. Friedrich Hubalek & Petra Posedel, 2008. "Asymptotic analysis for a simple explicit estimator in Barndorff-Nielsen and Shephard stochastic volatility models," Papers 0807.3479, arXiv.org.
    3. Penev, Spiridon & Peng, Hanxiang & Schick, Anton & Wefelmeyer, Wolfgang, 2004. "Efficient estimators for functionals of Markov chains with parametric marginals," Statistics & Probability Letters, Elsevier, vol. 66(3), pages 335-345, February.
    4. Weiwei Guo & Lingfei Li, 2019. "Parametric inference for discretely observed subordinate diffusions," Statistical Inference for Stochastic Processes, Springer, vol. 22(1), pages 77-110, April.
    5. J. Jimenez & R. Biscay & T. Ozaki, 2005. "Inference Methods for Discretely Observed Continuous-Time Stochastic Volatility Models: A Commented Overview," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 12(2), pages 109-141, June.

  11. Michael Sørensen, 2000. "Prediction-based estimating functions," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 123-147.

    Cited by:

    1. Bermudez, P. de Zea & Marín, J. Miguel & Rue, Håvard & Veiga, Helena, 2024. "Integrated nested Laplace approximations for threshold stochastic volatility models," Econometrics and Statistics, Elsevier, vol. 30(C), pages 15-35.
    2. Bannör, Karl & Kiesel, Rüdiger & Nazarova, Anna & Scherer, Matthias, 2016. "Parametric model risk and power plant valuation," Energy Economics, Elsevier, vol. 59(C), pages 423-434.
    3. Comte, F. & Genon-Catalot, V. & Rozenholc, Y., 2009. "Nonparametric adaptive estimation for integrated diffusions," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 811-834, March.
    4. A. Hurn & J. Jeisman & K. Lindsay, 2007. "Teaching an Old Dog New Tricks: Improved Estimation of the Parameters of Stochastic Differential Equations by Numerical Solution of the Fokker-Planck Equation," NCER Working Paper Series 9, National Centre for Econometric Research.
    5. Benth, Fred Espen & Kiesel, Rüdiger & Nazarova, Anna, 2012. "A critical empirical study of three electricity spot price models," Energy Economics, Elsevier, vol. 34(5), pages 1589-1616.
    6. P. de Zea Bermudez & J. Miguel Marín & Helena Veiga, 2020. "Data cloning estimation for asymmetric stochastic volatility models," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1057-1074, November.
    7. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465, September.
    8. Enrico Bibbona & Ilia Negri, 2015. "Higher Moments and Prediction-Based Estimation for the COGARCH(1,1) Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 891-910, December.
    9. Heejoon Han & Eunhee Lee, 2020. "Triple Regime Stochastic Volatility Model with Threshold and Leverage Effects," Korean Economic Review, Korean Economic Association, vol. 36, pages 481-509.
    10. Anzarut, Michelle & Mena, Ramsés H., 2019. "A Harris process to model stochastic volatility," Econometrics and Statistics, Elsevier, vol. 10(C), pages 151-169.
    11. Taufer, Emanuele & Leonenko, Nikolai & Bee, Marco, 2011. "Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2525-2539, August.
    12. Anne Brix & Asger Lunde, 2015. "Prediction-based estimating functions for stochastic volatility models with noisy data: comparison with a GMM alternative," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 433-465, October.
    13. Fred Espen Benth & Jan Kallsen & Thilo Meyer-Brandis, 2007. "A Non-Gaussian Ornstein-Uhlenbeck Process for Electricity Spot Price Modeling and Derivatives Pricing," Applied Mathematical Finance, Taylor & Francis Journals, vol. 14(2), pages 153-169.
    14. Stan Hurn & J.Jeisman & K.A. Lindsay, 2006. "Teaching an old dog new tricks: Improved estimation of the parameters of SDEs by numerical solution of the Fokker-Planck equation," Stan Hurn Discussion Papers 2006-01, School of Economics and Finance, Queensland University of Technology.
    15. Kallsen Jan & Muhle-Karbe Johannes, 2011. "Method of moment estimation in time-changed Lévy models," Statistics & Risk Modeling, De Gruyter, vol. 28(2), pages 169-194, May.
    16. Leah Kelly, 2004. "Inference and Intraday Analysis of Diversified World Stock Indices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2004, January-A.
    17. Asger Lunde & Anne Floor Brix, 2013. "Estimating Stochastic Volatility Models using Prediction-based Estimating Functions," CREATES Research Papers 2013-23, Department of Economics and Business Economics, Aarhus University.
    18. Genon-Catalot, Valentine & Laredo, Catherine, 2006. "Leroux's method for general hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 116(2), pages 222-243, February.
    19. Stan Hurn & J.Jeisman & K.A. Lindsay, 2006. "Seeing the wood for the trees: A critical evaluation of methods to estimate the parameters of stochastic differential equations," Stan Hurn Discussion Papers 2006, School of Economics and Finance, Queensland University of Technology.
    20. Stan Hurn & J.Jeisman & K.A. Lindsay, 2006. "Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations. Working paper #2," NCER Working Paper Series 2, National Centre for Econometric Research.
    21. Fernando Baltazar-Larios & Michael Sørensen, 2010. "Maximum likelihood estimation for integrated diffusion processes," CREATES Research Papers 2010-33, Department of Economics and Business Economics, Aarhus University.

  12. Barndorff-Nielsen, O. E. & Sorensen, M., 1991. "Information quantities in non-classical settings," Computational Statistics & Data Analysis, Elsevier, vol. 12(2), pages 143-158, September.

    Cited by:

    1. Küchler, Uwe & Sørensen, Michael M., 1998. "A note on limit theorems for multivariate martingales," SFB 373 Discussion Papers 1998,45, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  13. Sørensen, Michael, 1990. "On quasi likelihood for semimartingales," Stochastic Processes and their Applications, Elsevier, vol. 35(2), pages 331-346, August.

    Cited by:

    1. Florens, Danielle & Pham, Huyên, 1998. "Large deviation probabilities in estimation of Poisson random measures," Stochastic Processes and their Applications, Elsevier, vol. 76(1), pages 117-139, August.

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