IDEAS home Printed from https://ideas.repec.org/a/eee/dyncon/v111y2020ics0165188919301939.html

Benchmarking machine-learning software and hardware for quantitative economics

Author

Listed:
  • Duarte, Victor
  • Duarte, Diogo
  • Fonseca, Julia
  • Montecinos, Alexis

Abstract

We investigate the performance of machine-learning software and hardware for quantitative economics. We show that the use of machine-learning software and hardware can significantly reduce computational time in compute-intensive tasks. Using a sovereign default model and the Least Squares Monte Carlo option pricing algorithm as benchmarks, we show that specialized hardware and software speed up calculations by up to four orders of magnitude when compared to programs written in popular high-level programming languages, such as MATLAB, Julia, Python/Numpy, and R, and high-performing low-level languages such as C++.

Suggested Citation

  • Duarte, Victor & Duarte, Diogo & Fonseca, Julia & Montecinos, Alexis, 2020. "Benchmarking machine-learning software and hardware for quantitative economics," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    • Handle: RePEc:eee:dyncon:v:111:y:2020:i:c:s0165188919301939
    DOI: 10.1016/j.jedc.2019.103796
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165188919301939
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jedc.2019.103796?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Susan Athey & Mohsen Bayati & Guido Imbens & Zhaonan Qu, 2019. "Ensemble Methods for Causal Effects in Panel Data Settings," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 65-70, May.
    3. Chase Coleman & Spencer Lyon & Lilia Maliar & Serguei Maliar, 2021. "Matlab, Python, Julia: What to Choose in Economics?," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1263-1288, December.
    4. Juan Carlos Hatchondo & Leonardo Martinez & Horacio Sapriza, 2010. "Quantitative properties of sovereign default models: solution methods," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 13(4), pages 919-933, October.
    5. Cristina Arellano, 2008. "Default Risk and Income Fluctuations in Emerging Economies," American Economic Review, American Economic Association, vol. 98(3), pages 690-712, June.
    6. Fernández-Villaverde, Jesús & Zarruk Valencia , David, 2018. "A Practical Guide to Parallelization in Economics," CEPR Discussion Papers 12890, Centre for Economic Policy Research.
    7. Fatih Guvenen, 2009. "A Parsimonious Macroeconomic Model for Asset Pricing," Econometrica, Econometric Society, vol. 77(6), pages 1711-1750, November.
    8. Mr. Leonardo Martinez & Horacio Sapriza & Juan Carlos Hatchondo, 2010. "Quantitative properties of sovereign default models: solution methods matter," IMF Working Papers 2010/100, International Monetary Fund.
    9. Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023. "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, vol. 91(3), pages 869-901, May.
    10. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    11. Aruoba, S. Borağan & Fernández-Villaverde, Jesús, 2015. "A comparison of programming languages in macroeconomics," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 265-273.
    12. Greg Kaplan & Giovanni L. Violante, 2014. "A Model of the Consumption Response to Fiscal Stimulus Payments," Econometrica, Econometric Society, vol. 82(4), pages 1199-1239, July.
    13. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    14. Nengjiu Ju & Jianjun Miao, 2012. "Ambiguity, Learning, and Asset Returns," Econometrica, Econometric Society, vol. 80(2), pages 559-591, March.
    15. Aldrich, Eric M. & Fernández-Villaverde, Jesús & Ronald Gallant, A. & Rubio-Ramírez, Juan F., 2011. "Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors," Journal of Economic Dynamics and Control, Elsevier, vol. 35(3), pages 386-393, March.
    16. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Joseph Farrington & Wai Keong Wong & Kezhi Li & Martin Utley, 2025. "Going faster to see further: graphics processing unit-accelerated value iteration and simulation for perishable inventory control using JAX," Annals of Operations Research, Springer, vol. 349(3), pages 1609-1638, June.
    2. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.

    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. Thomas J. Sargent & John Stachurski, 2024. "Dynamic Programming: Finite States," Papers 2401.10473, arXiv.org.
    2. Mingzhuo Deng & Pablo A. Guerron-Quintana & Lewis Tseng, 2023. "Parallel Computation of Sovereign Default Models," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1047-1085, October.
    3. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    4. Lopez-Martin, Bernabe & Leal, Julio & Martinez Fritscher, Andre, 2019. "Commodity price risk management and fiscal policy in a sovereign default model," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 304-323.
    5. Falko Juessen & Andreas Schabert, 2013. "Fiscal Policy, Sovereign Default, and Bailouts," Working Paper Series in Economics 67, University of Cologne, Department of Economics.
    6. Carré, Sylvain & Cohen, Daniel & Villemot, Sébastien, 2019. "The sources of sovereign risk: a calibration based on Lévy stochastic processes," Journal of International Economics, Elsevier, vol. 118(C), pages 31-43.
    7. Juan Carlos Hatchondo & Leonardo Martinez, 2010. "The politics of sovereign defaults," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 96(3Q), pages 291-317.
    8. Demian Pouzo & Ignacio Presno, 2012. "Sovereign default risk and uncertainty premia," Working Papers 12-11, Federal Reserve Bank of Boston.
    9. Ricardo Sabbadini, 2018. "International Reserves Management in a Model of Partial Sovereign Default," Working Papers, Department of Economics 2018_14, University of São Paulo (FEA-USP).
    10. Takefumi Yamazaki, 2018. "Accuracy and speed of the solution methods for sovereign default models: The stable performance of the Tauchen method and cubic spline interpolation," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 14(4), pages 641-662, July.
    11. Cohen, Daniel & Villemot, Sébastien, 2012. "The Sovereign Default Puzzle: Modelling Issues and Lessons for Europe," CEPR Discussion Papers 8971, Centre for Economic Policy Research.
    12. Hatchondo, Juan Carlos & Martinez, Leonardo & Onder, Yasin Kursat, 2017. "Non-defaultable debt and sovereign risk," Journal of International Economics, Elsevier, vol. 105(C), pages 217-229.
    13. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    14. Engler, Philipp & Große Steffen, Christoph, 2016. "Sovereign risk, interbank freezes, and aggregate fluctuations," European Economic Review, Elsevier, vol. 87(C), pages 34-61.
    15. repec:fip:fedreq:y:2012:i:2q:p:139-157:n:vol.98no.2 is not listed on IDEAS
    16. Francesco Pappada & Yanos Zylberberg, 2019. "Sovereign default and imperfect tax enforcement," Bristol Economics Discussion Papers 19/714, School of Economics, University of Bristol, UK.
    17. Kaas, Leo & Mellert, Jan & Scholl, Almuth, 2020. "Sovereign and private default risks over the business cycle," Journal of International Economics, Elsevier, vol. 123(C).
    18. Ricardo Sabbadini, 2017. "Overcoming the Original Sin: Gains from Local Currency External Debt," Working Papers, Department of Economics 2017_27, University of São Paulo (FEA-USP).
    19. Javier Bianchi & Juan Carlos Hatchondo & Leonardo Martinez, 2018. "International Reserves and Rollover Risk," American Economic Review, American Economic Association, vol. 108(9), pages 2629-2670, September.
    20. Ignacio Presno & Demian Pouzo, 2012. "Sovereign Default Risk and Uncertainty Premia," 2012 Meeting Papers 608, Society for Economic Dynamics.
    21. Maren Froemel, 2014. "Imperfect Financial Markets and the Cyclicality of Social Spending," Working Paper Series of the Department of Economics, University of Konstanz 2014-11, Department of Economics, University of Konstanz.

    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:eee:dyncon:v:111:y:2020:i:c:s0165188919301939. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jedc .

    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.