SV Mixture, Classification Using EM Algorithm
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Keywords

Mixture stochastic volatitlity model, Expectation-Maximization algorithm, Clustering approach.

How to Cite

Hachicha, A. ., Hachicha, F. ., & Masmoudi, A. . (2013). SV Mixture, Classification Using EM Algorithm. Asian Economic and Financial Review, 3(4), 553–559. Retrieved from https://archive.aessweb.com/index.php/5002/article/view/1019

Abstract

The present paper presents a theoretical extension of our earlier work entitled“A comparative study of two models SV with MCMC algorithm” cited, Rev Quant Finan Acc (2012) 38:479-493 DOI 10.1007/s11156-011-0236-1 where we propose initially a mixture stochastic volatility model providing a tractable method for capturing certain market characteristics. To estimate the parameter of a mixture stochastic volatility model, we first use the Expectation-Maximisation (EM) algorithm. The second step is to adopt the clustering approach to classify the database into subsets (clusters).

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