IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v12y2024i10p269-d1492160.html
   My bibliography  Save this article

Using Short Time Series of Monofractal Synthetic Fluctuations to Estimate the Foreign Exchange Rate: The Case of the US Dollar and the Chilean Peso (USD–CLP)

Author

Listed:
  • Juan L. López

    (Centro de Innovación en Ingeniería Aplicada, Universidad Católica del Maule, Av. San Miguel 3605, Talca 3460000, Chile)

  • David Morales-Salinas

    (Department of Computer Science and Industries, Universidad Católica del Maule, Av. San Miguel 3605, Talca 3460000, Chile)

  • Daniel Toral-Acosta

    (Facultad de Ciencias Físico-Matemáticas, Universidad Autónoma de Nuevo León, Pedro de Alba S/N, Ciudad Universitaria, San Nicolás de los Garza 66455, Mexico)

Abstract

Short time series are fundamental in the foreign exchange market due to their ability to provide real-time information, allowing traders to react quickly to market movements, thus optimizing profits and mitigating risks. Economic transactions show a strong connection to foreign currencies, making exchange rate prediction challenging. In this study, the exchange rate estimation between the US dollar (USD) and the Chilean peso (CLP) for a short period, from 2 August 2021 to 31 August 2022, is modeled using the nonlinear Schrödinger equation (NLSE) and calculated with the fourth-order Runge–Kutta method, respectively. Additionally, the daily fluctuations of the current exchange rate are characterized using the Hurst exponent, H , and later used to generate short synthetic fluctuations to predict the USD–CLP exchange rate. The results show that the USD–CLP exchange rate can be estimated with an error of less than 5 % , while when using short synthetic fluctuations, the exchange rate shows an error of less than 10 % .

Suggested Citation

  • Juan L. López & David Morales-Salinas & Daniel Toral-Acosta, 2024. "Using Short Time Series of Monofractal Synthetic Fluctuations to Estimate the Foreign Exchange Rate: The Case of the US Dollar and the Chilean Peso (USD–CLP)," Economies, MDPI, vol. 12(10), pages 1-15, October.
    • Handle: RePEc:gam:jecomi:v:12:y:2024:i:10:p:269-:d:1492160
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/12/10/269/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7099/12/10/269/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ladislav Kristoufek, 2012. "Fractal Markets Hypothesis And The Global Financial Crisis: Scaling, Investment Horizons And Liquidity," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-13.
    2. Zhi-Qiang Jiang & Wen-Jie Xie & Wei-Xing Zhou & Didier Sornette, 2018. "Multifractal analysis of financial markets," Papers 1805.04750, arXiv.org.
    3. Sebastián Claro & Claudio Soto, 2013. "Exchange rate policy and exchange rate interventions: the Chilean experience," BIS Papers chapters, in: Bank for International Settlements (ed.), Sovereign risk: a world without risk-free assets?, volume 73, pages 81-93, Bank for International Settlements.
    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. Tan, Zhengxun & Liu, Juan & Chen, Juanjuan, 2021. "Detecting stock market turning points using wavelet leaders method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. Zhai, Lu-Sheng & Liu, Ruo-Yu, 2019. "Local detrended cross-correlation analysis for non-stationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 222-233.
    3. Horta, Paulo & Lagoa, Sérgio & Martins, Luís, 2014. "The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 140-153.
    4. Aslam, Faheem & Zil-e-huma, & Bibi, Rashida & Ferreira, Paulo, 2022. "Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis," Resources Policy, Elsevier, vol. 75(C).
    5. Ginn, William & Pourroy, Marc, 2020. "Should a central bank react to food inflation? Evidence from an estimated model for Chile," Economic Modelling, Elsevier, vol. 90(C), pages 221-234.
    6. Joscha Beckmann & Mariarosaria Comunale, 2020. "Exchange rate fluctuations and the financial channel in emerging economies," Bank of Lithuania Working Paper Series 83, Bank of Lithuania.
    7. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.
    8. Ma, Pengcheng & Li, Daye & Li, Shuo, 2016. "Efficiency and cross-correlation in equity market during global financial crisis: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 163-176.
    9. Robert Gk{e}barowski & Pawe{l} O'swik{e}cimka & Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z, 2019. "Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis," Papers 1906.07491, arXiv.org, revised Oct 2019.
    10. Ferreira, Paulo & Loures, Luís & Nunes, José & Brito, Paulo, 2018. "Are renewable energy stocks a possibility to diversify portfolios considering an environmentally friendly approach? The view of DCCA correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 675-681.
    11. Yang, Yan-Hong & Shao, Ying-Hui & Shao, Hao-Lin & Stanley, H. Eugene, 2019. "Revisiting the weak-form efficiency of the EUR/CHF exchange rate market: Evidence from episodes of different Swiss franc regimes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 734-746.
    12. Rodrigo Cifuentes, Sebastián Claro and Alejandro Jara, 2017. "Macroeconomic and financial volatility and macroprudential policies in Chile," BIS Papers chapters, in: Bank for International Settlements (ed.), Macroprudential policy frameworks, implementation and relationships with other policies, volume 94, pages 87-98, Bank for International Settlements.
    13. Zhang, Guofu & Li, Jingjing, 2018. "Multifractal analysis of Shanghai and Hong Kong stock markets before and after the connect program," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 611-622.
    14. Lee, Minhyuk & Song, Jae Wook & Kim, Sondo & Chang, Woojin, 2018. "Asymmetric market efficiency using the index-based asymmetric-MFDFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1278-1294.
    15. Kristoufek, Ladislav, 2018. "Fractality in market risk structure: Dow Jones Industrial components case," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 69-75.
    16. Sobolev, Daphne, 2017. "The effect of price volatility on judgmental forecasts: The correlated response model," International Journal of Forecasting, Elsevier, vol. 33(3), pages 605-617.
    17. Rodrigo Vergara & Elías Albagli, 2015. "Tasas de Interés de Largo Plazo en Economías Desarrolladas: Tendencias Recientes e Implicancias de Política Monetaria en Chile," Economic Policy Papers Central Bank of Chile 52, Central Bank of Chile.
    18. Emrah BALKAN & Umut UYAR, 2022. "The Fractal Structure of CDS Spreads: Evidence from the OECD Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 106-121, April.
    19. Buonocore, R.J. & Aste, T. & Di Matteo, T., 2016. "Measuring multiscaling in financial time-series," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 38-47.
    20. Domino, Krzysztof & Błachowicz, Tomasz, 2014. "The use of copula functions for modeling the risk of investment in shares traded on the Warsaw Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 77-85.

    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:gam:jecomi:v:12:y:2024:i:10:p:269-:d:1492160. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.