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Mining Financial Data using Mixtures of Mirrored Weibull Distributions

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  • Zijun Jia
  • Sharon X. Lee

Abstract

Risk management is an important part of financial practice, essential for protecting assets and investments in modern-day volatile markets. This paper proposes a mixture of mirrored Weibull (MMW) distribution for modelling stock returns and estimating risk measures. Unlike common practices which are typically based on the normal distribution, the MMW model can flexibly accommodate non-normal features frequently exhibited in financial data. It also enjoys appealing properties such as having a simple density expression and fast parameter estimation. We demonstrate the effectiveness of our model by assessing its performance in Value-at-Risk (VaR) estimation of three S&P500 stocks. The MMW model compares favourably to Gaussian mixture model and t-mixture model, with significant improvements in VaR estimation and prediction.

Suggested Citation

  • Zijun Jia & Sharon X. Lee, 2026. "Mining Financial Data using Mixtures of Mirrored Weibull Distributions," Papers 2605.20142, arXiv.org.
    • Handle: RePEc:arx:papers:2605.20142
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    References listed on IDEAS

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    1. Subu Venkataraman, 1997. "Value at risk for a mixture of normal distributions: the use of quasi- Bayesian estimation techniques," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 21(Mar), pages 2-13.
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