Open Access

FX Hedging Algorithms for Crypto-Native Companies

https://doi.org/10.55640/ijaair-v02i10-02

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Anjali Kale ¹ ,

⁴ Ennov Solutions Inc – Software Solutions for Life Sciences, USA

Abstract

Globalization of financial reporting has increased the need of a correct, efficient and harmonized reconciliation with varying Generally Accepted Accounting Principles (GAAP). The volatility of digital assets and fiat-based financial ecosystems takes on an additional dimension of complexity to crypto-native businesses in the form of cryptocurrency exchanges, custodians, and decentralized finance (DeFi) solutions. The increase in the use of cryptocurrencies to record transactions, as well as the use of fiat-based reporting, exposes these organizations to foreign exchange (FX) and digital asset valuation risks that are compounded by each other. The pre-existing instruments of reconciliation and hedging like forwards, options and swaps do not sufficiently take care of the real-time, decentralized, and high volatility nature of crypto environments.

The current paper presents a multi-GAAP reconciliation model that is AI-enabled to efficiently manage compliance in different jurisdictions by using machine learning, algorithmic FX hedging and intelligent automation. Through a comparison of the traditional process of reconciliation and the AI-powered one, as well as examination of the case-studies in the real-world scenario, the proposed model considers each financial model, technical infrastructure of technical environment, and regulatory parameters as a complex of elements that are integral parts of a single solution. The framework not only positions cross-border financial flows in the direction of greater financial transparency, but also manages to overcome the operational, legislative as well as technical turmoil to which crypto-native companies are exposed. The project advances a dynamic field, intelligent financial systems, and creates a scalable basis of future-proofed treasury and reporting processes within the digital asset economy.

Keywords

FX hedging, cryptocurrency, algorithmic trading, crypto-native companies, risk management, treasury systems, blockchain, volatility, financial technology

References

J. C. Hull, Risk Management and Financial Institutions, 5th ed. Hoboken, NJ, USA: Wiley, 2018.

A. C. Shapiro, Multinational Financial Management, 11th ed. Hoboken, NJ, USA: Wiley, 2020.

D. G. Baur, K. Hong, and A. D. Lee, “Bitcoin: Medium of exchange or speculative asset?,” J. Int. Financial Markets, Institutions and Money, vol. 54, pp. 177–189, 2018.

International Monetary Fund (IMF), “The crypto ecosystem and financial stability challenges,” Oct. 2022. [Online]. Available: https://www.imf.org

D. Gandal, J. T. Hamrick, T. Moore, and T. Oberman, “Price manipulation in the Bitcoin ecosystem,” J. Monetary Economics, vol. 95, pp. 86–96, 2018.

Deloitte, “Crypto risk management: A new frontier,” 2023. [Online]. Available: https://www2.deloitte.com

C. Chen and Y. Wu, “Algorithmic currency hedging in decentralized markets,” J. Financial Data Science, vol. 4, no. 2, pp. 25–39, 2022.

KPMG, “Digital asset management: Crypto treasury strategies,” 2022. [Online]. Available: https://home.kpmg

Organisation for Economic Co-operation and Development (OECD), “Regulatory approaches to crypto assets,” 2023. [Online]. Available: https://www.oecd.org

PwC, “Managing FX risk in crypto-native firms,” 2023. [Online]. Available: https://www.pwc.com

R. Narang, Inside the Black Box: The Simple Truth About Quantitative Trading, 2nd ed. Hoboken, NJ, USA: Wiley, 2009.

J. B. Heaton, N. G. Polson, and J. H. Witte, “Deep learning in finance,” Annu. Rev. Financial Econ., vol. 8, no. 1, pp. 357–395, 2016.

M. Chowdhury, R. Haque, and S. Kabir, “Managing crypto volatility: A macroeconomic framework,” J. Digital Asset Res., vol. 2, no. 1, pp. 45–60, 2021.

M. López and J. Kim, “Cryptocurrency payrolls and FX risk: A regulatory perspective,” Global Finance J., vol. 34, pp. 101–112, 2022.

A. Johnson and R. Patel, “Taxation, compliance, and the crypto economy,” Int. J. Financial Regulation, vol. 5, no. 2, pp. 78–91, 2023.

H. Smith, “Automation in treasury management: Case studies from digital firms,” Fintech Insights Quarterly, vol. 11, no. 3, pp. 23–39, 2022.

J. Lee, S. Park, and K. Tang, “AI in FX risk: An operational perspective,” J. Financial Automation, vol. 6, no. 2, pp. 55–71, 2023.

T. Brown and M. Taylor, “Backtesting hedge strategies with high-frequency data,” J. Algorithmic Finance, vol. 10, no. 1, pp. 12–29, 2021.

M. Johnson, Manual vs. Algorithmic Hedging: A Comparative Framework, Cambridge, UK: FinPress, 2020.

V. Buterin, “Composability and the future of DeFi,” Ethereum Foundation Blog, Sep. 2020. [Online]. Available: https://blog.ethereum.org

A. Narayanan, J. Bonneau, E. Felten, A. Miller, and S. Goldfeder, Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton, NJ, USA: Princeton Univ. Press, 2021.

J. Zhang and R. Singh, “AI-driven risk management in crypto treasury,” J. Blockchain Intelligence, vol. 4, no. 2, pp. 66–80, 2022.

Bank for International Settlements (BIS), “Markets and payment systems report: Implications of digital innovation,” Jan. 2023. [Online]. Available: https://www.bis.org

G. Gorton and J. Zhang, “DeFi, Central Bank Digital Currencies, and the future of monetary stability,” Yale J. Regulation, vol. 40, no. 1, pp. 123–154, 2023.

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Abstract

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References

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