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Quantum monte carlo for economics: Stress testing and macroeconomic deep learning

Author

Listed:
  • Skavysh, Vladimir
  • Priazhkina, Sofia
  • Guala, Diego
  • Bromley, Thomas R.

Abstract

Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and challenges in doing so. We provide a detailed introduction to quantum computing and especially the QMC algorithm. Then, we illustrate how to formulate and encode into quantum circuits (a) a bank stress testing model with credit shocks and fire sales, (b) a neoclassical investment model solved with deep learning, and (c) a realistic macro model solved with deep neural networks. We discuss potential computational gains of QMC versus classical computing systems and present a few innovations in benchmarking QMC.

Suggested Citation

  • Skavysh, Vladimir & Priazhkina, Sofia & Guala, Diego & Bromley, Thomas R., 2023. "Quantum monte carlo for economics: Stress testing and macroeconomic deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:dyncon:v:153:y:2023:i:c:s0165188923000866
    DOI: 10.1016/j.jedc.2023.104680
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    References listed on IDEAS

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    More about this item

    Keywords

    Monte Carlo; Quantum computing; Computational methods; Stress testing; DSGE; Machine learning; Deep learning;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • E - Macroeconomics and Monetary Economics
    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • G - Financial Economics
    • G1 - Financial Economics - - General Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G2 - Financial Economics - - Financial Institutions and Services
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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