Work

Mining Financial Data using Mixtures of Mirrored Weibull Distributions

Year: 2024

Type: conference-paper

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.

Source: IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)

DOI: 10.1109/iciba62489.2024.10868849

Authors: Zijun Jia, Sharon X. Lee

Institutions: Inner Mongolia Agricultural University, The University of Queensland

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

Funders: China Scholarship Council, Inner Mongolia Agricultural University

OpenAlex ID: W4407361822

FWCI:

Cites: 15

Cited by:

Related to: 10