Credit risk is a fundamental aspect of financial risk management that arises from the possibility of borrowers or counterparties failing to meet their contractual obligations. It refers to the potential of a borrower defaulting on their debt repayments, leading to potential financial losses for lenders or investors. Credit risk is prevalent in various financial transactions, including loans, bonds, derivatives, and trade credits. To mitigate credit risk, financial institutions and investors conduct thorough credit assessments, analyzing the creditworthiness of borrowers based on their credit history, financial stability, and industry outlook.
Many credit models have been developed for corporate entities within traditional financial markets. However, the realm of cryptocurrency presents a different landscape, as credit risk in this domain is not well understood, and only a limited number of models have been developed. As the cryptocurrency space continues to evolve, there is a growing need for innovative and specialized credit risk models that cater to the distinct features of digital assets, ensuring more accurate risk assessments and informed decision-making for investors and stakeholders.
Reference [1] studied some credit risk models in the cryptocurrency world. The author pointed out,
The results showed that credit-scoring models and machine-learning methods using lagged trading volumes and online searches were the most effective models for short-term forecasts, up to 30 days ahead, whereas time-series models using the daily range were better suited for longer-term forecasts, up to 1 year ahead. ..
The main recommendation for investors is to use credit-scoring and machine-learning models for short-term forecasting up to 30 days ahead, particularly the cauchit and the random forest models first suggested by [11]. Meanwhile, ZPP-based models using range-based volatility estimators are a better choice for long-term forecasts up to 1 year ahead, which is the traditional horizon for credit risk management.
In short, the author successfully developed a credit risk model utilizing the open-high-low-close (OHLC) prices of cryptocurrencies. We are intrigued by the potential applicability of this method to corporate entities. If this approach can be adapted to traditional corporates, it could present a simplified and innovative solution for credit risk assessment.
Let us know what you think in the comments below or in the discussion forum.
References
[1] Dean Fantazzini, Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models, Information 2023, 14, 254.
Further questions
What's your question? Ask it in the discussion forum
Have an answer to the questions below? Post it here or in the forum
