The Applications of Mixtures of Normal Distributions in Empirical Finance: A Selected Survey
No 904, Working Papers from University of Waterloo, Department of Economics
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.
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. (search for similar items in EconPapers)
JEL-codes: C01 C13 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2009-09, Revised 2009-09
New Economics Papers: this item is included in nep-cfn, nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:wat:wpaper:0904
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