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<p>The economy is rapidly becoming more dependent on digital technology. The use of digital currency has the potential to transform the financial industry, broaden financial inclusion, and promote economic development. Blockchain technology enables digital currencies like Bitcoin and Ethereum to operate decentralizedly. Investors have begun to invest in cryptocurrencies as a result of their dissatisfaction or issues with traditional financial and economic systems. It is crucial to look at the daily loss and profit behaviour to analyse the currency performance for the investment. For this, a model is proposed where the parameters get changed for the traditional AR(1) model due to loss and profit behaviour. We employed the Bayesian techniques to estimate the profit and loss behaviour through AR(1) process. We employed this model to 12 major cryptocurrencies in the market. The simulation has been conducted to get more applicability of the proposed model.</p>

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Modeling of Virtual Currencies through AR(1) Process Considering Loss-Profit Regimes

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • Jitendra Kumar,
  • Abhishek Kumar Jilowa

摘要

The economy is rapidly becoming more dependent on digital technology. The use of digital currency has the potential to transform the financial industry, broaden financial inclusion, and promote economic development. Blockchain technology enables digital currencies like Bitcoin and Ethereum to operate decentralizedly. Investors have begun to invest in cryptocurrencies as a result of their dissatisfaction or issues with traditional financial and economic systems. It is crucial to look at the daily loss and profit behaviour to analyse the currency performance for the investment. For this, a model is proposed where the parameters get changed for the traditional AR(1) model due to loss and profit behaviour. We employed the Bayesian techniques to estimate the profit and loss behaviour through AR(1) process. We employed this model to 12 major cryptocurrencies in the market. The simulation has been conducted to get more applicability of the proposed model.