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<p>In this study, we introduce and analyze the Truncated Cauchy Power Rayleigh Weibull (TCPRW) distribution, a novel statistical model derived by integrating the Truncated Cauchy Power (TCP) family with the Rayleigh Weibull (RW) distribution. We explore the statistical properties of the proposed distribution, including moment functions, quantile functions, and actuarial risk measures. The paper provides an in-depth derivation of key actuarial measures such as Value-at-Risk (VaR), Expected Shortfall (ES), Tail Value-at-Risk (TVaR), Tail Variance (TV), and Tail Variance Premium (TVP), which are essential in risk management and financial modeling. To estimate the unknown parameters of the TCPRW model, we employ both Maximum Likelihood Estimation (MLE) and Bayesian Estimation techniques. A comprehensive simulation study is conducted to assess the performance of the estimation methods. Furthermore, we validate the flexibility and applicability of the proposed model using real-world inflation rate data across different countries. The results demonstrate that the TCPRW distribution provides a superior fit compared to existing models, making it a valuable tool for modeling inflation risks and other economic variables.</p>

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A New Statistical Model for Inflation Risk Evaluation Using Actuarial Techniques

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • Ehab M. Almetwally,
  • Diaa S. Metwally,
  • I. Elbatal

摘要

In this study, we introduce and analyze the Truncated Cauchy Power Rayleigh Weibull (TCPRW) distribution, a novel statistical model derived by integrating the Truncated Cauchy Power (TCP) family with the Rayleigh Weibull (RW) distribution. We explore the statistical properties of the proposed distribution, including moment functions, quantile functions, and actuarial risk measures. The paper provides an in-depth derivation of key actuarial measures such as Value-at-Risk (VaR), Expected Shortfall (ES), Tail Value-at-Risk (TVaR), Tail Variance (TV), and Tail Variance Premium (TVP), which are essential in risk management and financial modeling. To estimate the unknown parameters of the TCPRW model, we employ both Maximum Likelihood Estimation (MLE) and Bayesian Estimation techniques. A comprehensive simulation study is conducted to assess the performance of the estimation methods. Furthermore, we validate the flexibility and applicability of the proposed model using real-world inflation rate data across different countries. The results demonstrate that the TCPRW distribution provides a superior fit compared to existing models, making it a valuable tool for modeling inflation risks and other economic variables.