IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01512431.html
   My bibliography  Save this paper

Forecasting Trends With Asset Prices

Author

Listed:
  • Ahmed Belhadjayed

    (FiQuant - Chaire de finance quantitative - MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

  • Grégoire Loeper

    (School of Mathematical Sciences [Clayton] - Monash University [Clayton])

  • Frédéric Abergel

    (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec)

Abstract

The question of interest in this paper is the estimation of the trend of a financial asset, and the impact of its misspecification on investment strategies. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein-Uhlenbeck process. Motivated by the use of Kalman filtering as a forecasting tool, we address the problem of parameters estimation, and measure the effect of parameters mis-specification. Numerical examples illustrate the difficulty of trend forecasting in financial time series.

Suggested Citation

  • Ahmed Belhadjayed & Grégoire Loeper & Frédéric Abergel, 2016. "Forecasting Trends With Asset Prices," Post-Print hal-01512431, HAL.
    • Handle: RePEc:hal:journl:hal-01512431
    DOI: 10.1080/14697688.2016.1206959
    Note: View the original document on HAL open archive server: https://hal.science/hal-01512431
    as

    Download full text from publisher

    File URL: https://hal.science/hal-01512431/document
    Download Restriction: no
    File URL: https://libkey.io/10.1080/14697688.2016.1206959?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    2. Lakner, Peter, 1998. "Optimal trading strategy for an investor: the case of partial information," Stochastic Processes and their Applications, Elsevier, vol. 76(1), pages 77-97, August.
    3. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.
    4. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737.
    5. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    6. Y. Lemp'eri`ere & C. Deremble & P. Seager & M. Potters & J. P. Bouchaud, 2014. "Two centuries of trend following," Papers 1404.3274, arXiv.org.
    7. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-417, October.
    8. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    9. Andrew C. Harvey & Thomas M. Trimbur, 2003. "General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 244-255, May.
    10. Brendle, Simon, 2006. "Portfolio selection under incomplete information," Stochastic Processes and their Applications, Elsevier, vol. 116(5), pages 701-723, May.
    11. Leroux, Brian G., 1992. "Maximum-likelihood estimation for hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 127-143, February.
    12. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    13. Babbs, Simon H. & Nowman, K. Ben, 1999. "Kalman Filtering of Generalized Vasicek Term Structure Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(1), pages 115-130, March.
    14. Maravall, Agustin, 1993. "Stochastic linear trends : Models and estimators," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 5-37, March.
    15. Roberto Casarin & Jean-Michel Marin, 2007. "Online data processing: comparison of Bayesian regularized particle filters," Working Papers 0703, University of Brescia, Department of Economics.
    16. Monahan, John F., 1983. "Fully Bayesian analysis of ARMA time series models," Journal of Econometrics, Elsevier, vol. 21(3), pages 307-331, April.
    17. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    18. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: I. Empirical facts," Post-Print hal-00621058, HAL.
    19. Mihaela Manoliu & Stathis Tompaidis, 2002. "Energy futures prices: term structure models with Kalman filter estimation," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 21-43.
    20. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
    21. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    22. Bedendo, Mascia & Hodges, Stewart D., 2009. "The dynamics of the volatility skew: A Kalman filter approach," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1156-1165, June.
    23. Pollock, D.S.G., 2007. "Wiener–Kolmogorov Filtering, Frequency-Selective Filtering, And Polynomial Regression," Econometric Theory, Cambridge University Press, vol. 23(1), pages 71-88, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmed Bel Hadj Ayed & Gr'egoire Loeper & Fr'ed'eric Abergel, 2015. "Forecasting trends with asset prices," Papers 1504.03934, arXiv.org, revised Apr 2015.
    2. Lombardi, Marco J. & Calzolari, Giorgio, 2009. "Indirect estimation of [alpha]-stable stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2298-2308, April.
    3. Asai, Manabu, 2009. "Bayesian analysis of stochastic volatility models with mixture-of-normal distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2579-2596.
    4. Gordon V. Chavez, 2019. "Dynamic tail inference with log-Laplace volatility," Papers 1901.02419, arXiv.org, revised Jul 2019.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Juan Hoyo & Guillermo Llorente & Carlos Rivero, 2020. "A Testing Procedure for Constant Parameters in Stochastic Volatility Models," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 163-186, June.
    7. Abanto-Valle, C.A. & Bandyopadhyay, D. & Lachos, V.H. & Enriquez, I., 2010. "Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2883-2898, December.
    8. Jensen, Mark J. & Maheu, John M., 2010. "Bayesian semiparametric stochastic volatility modeling," Journal of Econometrics, Elsevier, vol. 157(2), pages 306-316, August.
    9. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    10. Du, Xiaodong & Yu, Cindy L. & Hayes, Dermot J., 2011. "Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis," Energy Economics, Elsevier, vol. 33(3), pages 497-503, May.
    11. Carlos A. Abanto‐Valle & Helio S. Migon & Hedibert F. Lopes, 2010. "Bayesian modeling of financial returns: A relationship between volatility and trading volume," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(2), pages 172-193, March.
    12. Antonello Loddo & Shawn Ni & Dongchu Sun, 2011. "Selection of Multivariate Stochastic Volatility Models via Bayesian Stochastic Search," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 342-355, July.
    13. Jensen, Mark J. & Maheu, John M., 2014. "Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture," Journal of Econometrics, Elsevier, vol. 178(P3), pages 523-538.
    14. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    15. Roberto León-González, 2019. "Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 899-920, September.
    16. António A. F. Santos, 2015. "On the Forecasting of Financial Volatility Using Ultra-High Frequency Data," GEMF Working Papers 2015-17, GEMF, Faculty of Economics, University of Coimbra.
    17. Meddahi, N., 2001. "An Eigenfunction Approach for Volatility Modeling," Cahiers de recherche 2001-29, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    18. Wang, Joanna J.J. & Chan, Jennifer S.K. & Choy, S.T. Boris, 2011. "Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 852-862, January.
    19. Tsionas, Mike G., 2021. "Bayesian analysis of static and dynamic Hurst parameters under stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    20. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos & Touche, Nassim, 2019. "Integer-valued stochastic volatility," MPRA Paper 91962, University Library of Munich, Germany, revised 04 Feb 2019.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-01512431. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.