Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data

Smail Tigani,Rachid Saadane,Hasna Chaibi

doi:10.3390/data4010019

Smail Tigani, Rachid Saadane + Show 1 more

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https://doi.org/10.3390/data4010019

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Abstract

This paper carried out a hybrid clustering model for foreign exchange market volatility clustering. The proposed model is built using a Gaussian Mixture Model and the inference is done using an Expectation Maximization algorithm. A mono-dimensional kernel density estimator is used in order to build a probability density based on all historical observations. That allows us to evaluate the behavior’s probability of each symbol of interest. The computation result shows that the approach is able to pinpoint risky and safe hours to trade a given currency pair.

Highlights

Many researchers build mathematical models and algorithms for price prediction [1] or trend classification [2]
The work focuses on the currency market as a special case of financial markets, but it is extensible to other components such as stocks, commodities, etc
Volatility is a statistical measure of the dispersion of returns for a given market index or foreign exchange symbol

Summary

Introduction

Many researchers build mathematical models and algorithms for price prediction [1] or trend classification [2]. Volatility is a statistical measure of the dispersion of returns for a given market index or foreign exchange symbol. Volatility shows how quick the prices move, it can either be measured by using the standard deviation or variance between returns from that same security or market index. As explained in [4], the bid–ask spreads or difference between the highest price and the lowest at a given time frame gives a dispersion measure. Volatility is influenced by many factors like liquidity, interest rate [5], real estate [6], opinions [7], and a firm’s share. The authors in [8] explain how liquidity providers of a market impact the volatility and stock returns, while the study in the paper [6] re-examine the relationship between a firm’s market share and volatility

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