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<p>With the rapid development of the global financial market, many financial institutions continue to face the impact of internationalization and liberalization, and financial institutions have changed from resource exploration in the past to internal management and innovation competition. However, compared with the relatively simple situation in the past, these changes also make enterprises face more financial risks, so financial risk management is particularly important. In this paper, the extreme value theory that can explain the phenomenon of "thick tail" is combined with Markov algorithm that can switch states under certain conditions, and a model of risk value evaluation is constructed. In this study, the data of Standard &amp; Poor’s 500 are used. The results show that the fitting degree of this model is obviously high, and the combination of Markov model and unbiased GM (1,1) model further improves the prediction accuracy. This study has reference value for financial institutions to estimate risks.</p>

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Application of Markov Process Algorithm Based on Mobile System Security in Financial Risk Model

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Guihong Su,
  • Chunshui Chang

摘要

With the rapid development of the global financial market, many financial institutions continue to face the impact of internationalization and liberalization, and financial institutions have changed from resource exploration in the past to internal management and innovation competition. However, compared with the relatively simple situation in the past, these changes also make enterprises face more financial risks, so financial risk management is particularly important. In this paper, the extreme value theory that can explain the phenomenon of "thick tail" is combined with Markov algorithm that can switch states under certain conditions, and a model of risk value evaluation is constructed. In this study, the data of Standard & Poor’s 500 are used. The results show that the fitting degree of this model is obviously high, and the combination of Markov model and unbiased GM (1,1) model further improves the prediction accuracy. This study has reference value for financial institutions to estimate risks.