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Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions?

Author

Listed:
  • Kokulo K. Lawuobahsumo

    (University of Calabria)

  • Bernardina Algieri

    (University of Calabria
    Universität Bonn)

  • Arturo Leccadito

    (University of Calabria
    LFIN/LIDAM, UCLouvain)

Abstract

This study aims to jointly predict conditional quantiles and tail expectations for the returns of the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using financial and macroeconomic indicators as explanatory variables. We adopt a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to make one- and five-steps-ahead predictions of Value-at-Risk (VaR) and Expected Shortfall (ES) based on a rolling window and compare the performance of our model against the Historical simulation and the standard ARMA(1,1)-GARCH(1,1) model used as benchmarks. The superior set of models is then chosen by backtesting VaR and ES using a Model Confidence Set procedure. Our results show that the MCQRNN performs better than both benchmark models for jointly predicting VaR and ES when considering daily data. Models with the implied volatility index, treasury yield spread and inflation expectations sharpen the extreme return predictions. The results are consistent for the two risk measures at the 1% and 5% level both, in the case of a long and short position and for all cryptocurrencies.

Suggested Citation

  • Kokulo K. Lawuobahsumo & Bernardina Algieri & Arturo Leccadito, 2024. "Forecasting cryptocurrencies returns: Do macroeconomic and financial variables improve tail expectation predictions?," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(3), pages 2647-2675, June.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:3:d:10.1007_s11135-023-01761-1
    DOI: 10.1007/s11135-023-01761-1
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    as
    1. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    2. Katsiampa, Paraskevi, 2019. "An empirical investigation of volatility dynamics in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 50(C), pages 322-335.
    3. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Bernardina Algieri & Arturo Leccadito, 2021. "Extreme price moves: an INGARCH approach to model coexceedances in commodity markets [The market response to government crop news under different release regimes]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 878-914.
    6. Caporale, Guglielmo Maria & Zekokh, Timur, 2019. "Modelling volatility of cryptocurrencies using Markov-Switching GARCH models," Research in International Business and Finance, Elsevier, vol. 48(C), pages 143-155.
    7. Kokulo K. Lawuobahsumo & Bernardina Algieri & Leonardo Iania & Arturo Leccadito, 2022. "Exploring Dependence Relationships between Bitcoin and Commodity Returns: An Assessment Using the Gerber Cross-Correlation," Commodities, MDPI, vol. 1(1), pages 1-16, August.
    8. Algieri, Bernardina & Kalkuhl, Matthias & Koch, Nicolas, 2017. "A tale of two tails: Explaining extreme events in financialized agricultural markets," Food Policy, Elsevier, vol. 69(C), pages 256-269.
    9. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    10. Borri, Nicola, 2019. "Conditional tail-risk in cryptocurrency markets," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 1-19.
    11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    12. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    13. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    14. 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.
    15. Wenjun Feng & Yiming Wang & Zhengjun Zhang, 2018. "Can cryptocurrencies be a safe haven: a tail risk perspective analysis," Applied Economics, Taylor & Francis Journals, vol. 50(44), pages 4745-4762, September.
    16. Nikita Moiseev & Alexey Mikhaylov & Hasan Dinçer & Serhat Yüksel, 2023. "Market capitalization shock effects on open innovation models in e-commerce: golden cut q-rung orthopair fuzzy multicriteria decision-making analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    17. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    18. Jong-Min Kim & Chulhee Jun & Junyoup Lee, 2021. "Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility," Mathematics, MDPI, vol. 9(14), pages 1-16, July.
    19. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    20. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
    21. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    22. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    23. Alexey Mikhaylov & Hasan Dinçer & Serhat Yüksel, 2023. "Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.
    24. Aviral Kumar Tiwari & Satish Kumar & Rajesh Pathak, 2019. "Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models," Applied Economics, Taylor & Francis Journals, vol. 51(37), pages 4073-4082, August.
    25. Tobias Fissler & Johanna F. Ziegel & Tilmann Gneiting, 2015. "Expected Shortfall is jointly elicitable with Value at Risk - Implications for backtesting," Papers 1507.00244, arXiv.org, revised Jul 2015.
    26. Bernardina Algieri, 2014. "Drivers of Export Demand: A Focus on the GIIPS Countries," The World Economy, Wiley Blackwell, vol. 37(10), pages 1454-1482, October.
    27. Adcock, Robert & Gradojevic, Nikola, 2019. "Non-fundamental, non-parametric Bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
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