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Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience

Author

Listed:
  • David Alaminos

    (Universitat de Barcelona)

  • M. Belén Salas

    (Universidad de Málaga
    Universidad de Málaga)

  • Manuel Á. Fernández-Gámez

    (Universidad de Málaga
    Universidad de Málaga)

Abstract

The foreign exchange markets, renowned as the largest financial markets globally, also stand out as one of the most intricate due to their substantial volatility, nonlinearity, and irregular nature. Owing to these challenging attributes, various research endeavors have been undertaken to effectively forecast future currency prices in foreign exchange with precision. The studies performed have built models utilizing statistical methods, being the Monte Carlo algorithm the most popular. In this study, we propose to apply Auxiliary-Field Quantum Monte Carlo to increase the precision of the FOREX markets models from different sample sizes to test simulations in different stress contexts. Our findings reveal that the implementation of Auxiliary-Field Quantum Monte Carlo significantly enhances the accuracy of these models, as evidenced by the minimal error and consistent estimations achieved in the FOREX market. This research holds valuable implications for both the general public and financial institutions, empowering them to effectively anticipate significant volatility in exchange rate trends and the associated risks. These insights provide crucial guidance for future decision-making processes.

Suggested Citation

  • David Alaminos & M. Belén Salas & Manuel Á. Fernández-Gámez, 2023. "Quantum Monte Carlo simulations for estimating FOREX markets: a speculative attacks experience," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-21, December.
  • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01836-2
    DOI: 10.1057/s41599-023-01836-2
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    as
    1. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia & Zhang, Yi, 2019. "Exchange rate prediction redux: New models, new data, new currencies," Journal of International Money and Finance, Elsevier, vol. 95(C), pages 332-362.
    2. Nico Neureiter & Peter Ranacher & Nour Efrat-Kowalsky & Gereon A. Kaiping & Robert Weibel & Paul Widmer & Remco R. Bouckaert, 2022. "Detecting contact in language trees: a Bayesian phylogenetic model with horizontal transfer," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
    3. Ca’ Zorzi, Michele & Kolasa, Marcin & Rubaszek, Michał, 2017. "Exchange rate forecasting with DSGE models," Journal of International Economics, Elsevier, vol. 107(C), pages 127-146.
    4. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    5. Asmussen, Søren, 2018. "Conditional Monte Carlo for sums, with applications to insurance and finance," Annals of Actuarial Science, Cambridge University Press, vol. 12(2), pages 455-478, September.
    6. Flood, Robert & Marion, Nancy, 1997. "The size and timing of devaluations in capital-controlled economies," Journal of Development Economics, Elsevier, vol. 54(1), pages 123-147, October.
    7. Ince, Onur & Molodtsova, Tanya & Papell, David H., 2016. "Taylor rule deviations and out-of-sample exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 69(C), pages 22-44.
    8. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2016. "Exchange rate predictability in a changing world," Journal of International Money and Finance, Elsevier, vol. 62(C), pages 1-24.
    9. Yao Elikem Ayekple & Charles Kofi Tetteh & Prince Kwaku Fefemwole, 2018. "Markov Chain Monte Carlo Method for Estimating Implied Volatility in Option Pricing," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 10(6), pages 108-116, December.
    10. Cheung, Yin-Wong & Erlandsson, Ulf G., 2005. "Exchange Rates and Markov Switching Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 314-320, July.
    11. Zorzi, Michele Ca’ & Rubaszek, Michał, 2020. "Exchange rate forecasting on a napkin," Journal of International Money and Finance, Elsevier, vol. 104(C).
    12. Park, Cheolbeom & Park, Sookyung, 2013. "Exchange rate predictability and a monetary model with time-varying cointegration coefficients," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 394-410.
    13. Eichengreen, Barry & Rose, Andrew K & Wyplosz, Charles, 1994. "Speculative Attacks on Pegged Exchange Rates: An Empirical Exploration with Special Reference to the European Monetary System," CEPR Discussion Papers 1060, C.E.P.R. Discussion Papers.
    14. Lee, Hsiu-Yun, 2011. "Nonlinear exchange rate dynamics under stochastic official intervention," Economic Modelling, Elsevier, vol. 28(4), pages 1510-1518, July.
    15. Jacob, Punnoose & Uusküla, Lenno, 2019. "Deep habits and exchange rate pass-through," Journal of Economic Dynamics and Control, Elsevier, vol. 105(C), pages 67-89.
    16. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
    17. Eric Fournié & Jean-Michel Lasry & Pierre-Louis Lions & Jérôme Lebuchoux, 2001. "Applications of Malliavin calculus to Monte-Carlo methods in finance. II," Finance and Stochastics, Springer, vol. 5(2), pages 201-236.
    18. Clements, Kenneth W. & Lan, Yihui, 2010. "A new approach to forecasting exchange rates," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1424-1437, November.
    19. Das, Smruti Rekha & Kuhoo, & Mishra, Debahuti & Rout, Minakhi, 2019. "An optimized feature reduction based currency forecasting model exploring the online sequential extreme learning machine and krill herd strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 339-370.
    20. Molodtsova, Tanya & Papell, David H., 2009. "Out-of-sample exchange rate predictability with Taylor rule fundamentals," Journal of International Economics, Elsevier, vol. 77(2), pages 167-180, April.
    21. Rosen Valchev, 2020. "Bond Convenience Yields and Exchange Rate Dynamics," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(2), pages 124-166, April.
    22. Minh-Hoang Nguyen & Thomas E. Jones, 2022. "Building eco-surplus culture among urban residents as a novel strategy to improve finance for conservation in protected areas," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-15, December.
    23. Ron Alquist & Menzie D. Chinn, 2008. "Conventional and unconventional approaches to exchange rate modelling and assessment," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 13(1), pages 2-13.
    24. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    25. Lúcio Otávio Seixas Barbosa & Frederico G. Jayme & Fabricio José Missio, 2018. "Determinants of the real exchange rate in the long-run for developing and emerging countries: a theoretical and empirical approach," International Review of Applied Economics, Taylor & Francis Journals, vol. 32(1), pages 62-83, January.
    26. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    27. Wei, Wei & Zhu, Dan, 2022. "Generic improvements to least squares monte carlo methods with applications to optimal stopping problems," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1132-1144.
    28. Chen, Chuanglian & Yao, Shujie & Ou, Jinghua, 2017. "Exchange rate dynamics in a Taylor rule framework," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 46(C), pages 158-173.
    29. Lux, Thomas, 2018. "Estimation of agent-based models using sequential Monte Carlo methods," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 391-408.
    30. Nguyen, Minh-Hoang & Jones, Thomas E., 2022. "Building eco-surplus culture among urban inhabitants as a novel strategy to improve finance for conservation in protected areas," OSF Preprints txzr3, Center for Open Science.
    31. Drew Creal, 2012. "A Survey of Sequential Monte Carlo Methods for Economics and Finance," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 245-296.
    32. Jorge Braga de Macedo & Urho Lempinen, 2013. "Exchange rate dynamics revisited," NBER Working Papers 19718, National Bureau of Economic Research, Inc.
    33. Ismailov, Adilzhan & Rossi, Barbara, 2018. "Uncertainty and deviations from uncovered interest rate parity," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 242-259.
    34. Usha Rekha Chinthapalli, 2021. "A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies," JRFM, MDPI, vol. 14(7), pages 1-23, July.
    35. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    36. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    37. Beckmann, Joscha & Schüssler, Rainer, 2016. "Forecasting exchange rates under parameter and model uncertainty," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 267-288.
    38. Benjamin Bloem‐Reddy & Peter Orbanz, 2018. "Random‐walk models of network formation and sequential Monte Carlo methods for graphs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(5), pages 871-898, November.
    39. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
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