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The Applications of Mixtures of Normal Distributions in Empirical Finance: A Selected Survey

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  • Dinghai Xu

    (Department of Economics, University of Waterloo)

Abstract

This paper provides a selected review of the recent developments and applications of mixtures of normal (MN) distribution models in empirical finance. Once attractive property of the MN model is that it is flexible enough to accommodate various shapes of continuous distributions, and able to capture leptokurtic, skewed and multimodal characteristics of financial time series data. In addition, the MN-based analysis fits well with the related regime-switching literature. The survey is conducted under two broad themes: (1) minimum-distance estimation methods, and (2) financial modeling and its applications.

Suggested Citation

  • Dinghai Xu, 2009. "The Applications of Mixtures of Normal Distributions in Empirical Finance: A Selected Survey," Working Papers 0904, University of Waterloo, Department of Economics, revised Sep 2009.
    • Handle: RePEc:wat:wpaper:0904
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    File URL: http://economics.uwaterloo.ca/documents/mn-review-paper-CES.pdf
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    2. Jun Lu & Shao Yi, 2022. "Reducing overestimating and underestimating volatility via the augmented blending-ARCH model," Papers 2203.12456, arXiv.org.
    3. Yu Mei & Zhiping Chen & Jia Liu & Bingbing Ji, 2022. "Multi-stage portfolio selection problem with dynamic stochastic dominance constraints," Journal of Global Optimization, Springer, vol. 83(3), pages 585-613, July.
    4. Jin Wang & Michael R. Taaffe, 2015. "Multivariate Mixtures of Normal Distributions: Properties, Random Vector Generation, Fitting, and as Models of Market Daily Changes," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 193-203, May.
    5. Assoc. Prof. Leon Li, 2022. "The Pricing of Discretionary Accruals Revisited: The Application of Mixtures of Regressions Based on Asymmetric Investor Behavior," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 8(3), pages 78-84, 09-2022.
    6. Pedro Correia S. Bezerra & Pedro Henrique M. Albuquerque, 2017. "Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels," Computational Management Science, Springer, vol. 14(2), pages 179-196, April.

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    More about this item

    Keywords

    Mixtures of Normal; Maximum Likelihood; Moment Generating Function; Characteristic Function; Switching Regression Model; (G) ARCH Model; Stochastic Volatility Model; Autoregressive Conditional Duration Model; Stochastic Duration Model; Value at Risk.;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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