Modélisation de la volatilité des rendements Bitcoin par les modèles GARCH non paramétriques

Objectif: L’objectif de cet article est de mettre en évidence l'efficacité du modèle GARCH non paramétrique pour la prédiction des prix futurs du Bitcoin. Méthodologie: L’utilisation de modèles GARCH paramétriques pour caractériser la volatilité des rendements Bitcoin est très utilisé dans la  littérature empirique. Alternativement, nous considérons une approche non paramétrique pour modéliser et prévoir la volatilité des rendements Bitcoin. Résultats: Nous montrons que la prévision de volatilité du modèle GARCH non paramétrique donne des performances supérieures par rapport à  une classe étendue de modèles GARCH paramétriques. Originalité/pertinence : L’amélioration de la précision des prévisions de la volatilité des rendements Bitcoin basée sur le modèle GARCH non  paramétrique suggère que cette méthode offre une alternative attrayante et viable par rapport aux modèles paramétriques GARCH couramment utilisés. English title: Modelling the volatility of Bitcoin returns using Nonparametric GARCH models Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin  prices. Methodology: The use of parametric GARCH models to characterize the volatility of Bitcoin returns iswidely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. Results: We show that the volatility forecast of the nonparametric GARCH model yields superiorperformance compared to an extended class of parametric GARCH models.Originality / relevance: The improved accuracy of forecasting the volatility of Bitcoin returns based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to commonly used GARCH parametric models..   &nbsp

Financial applications of machine learning using R software

In the last years, the financial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the financial community are implementing these models regularly. In this paper, we analyses the limitations of machine learning methods, and then provides some suggestions on the choice of methods in financial applications. We refer the reader to the R libraries that can be used to compute the Machine learning method

How to use machine learning in finance

In the last years, the financial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the financial community are implementing these models regularly. In this paper, i present the different Machine Learning techniques used, and provide some suggestions on the choice of methods in financial applications. We refer the reader to the R packages that can be used to compute the Machine learning method

Modelling the volatility of Bitcoin returns using Nonparametric GARCH models

Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterise the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving non-parametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the non-parametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the non-parametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models

How to use the R software

R is a language and software that allows statistical analysis. It includes means that make it possible to manipulate data, calculations and graphical representations. It provides a wide variety of statistical tools (modeling, statistical testing, time series analysis, classification problems, machine learning, ...) Nowadays, there is no doubt that it is the software par excellence in statistical courses for any level, for theoretical and applied subjects alike. The goal of this paper is helping to start using the statistical software R. We will cover its benefits, show how to get started and will make interesting recommendations for using R, according to my experience

The Financial Application of Machine Learning Using R Software

In the last years, the financial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the financial community are implementing these models regularly. In this paper, we analyses the limitations of machine learning methods, and then provides some suggestions on the choice of methods in financial applications. We refer the reader to the R libraries that can be used to compute the Machine learning methods

Bayesian Structural VAR Approach to Tunisian Monetary Policy Framework

In this paper we use the Bayesian Structural VAR framework to identify the major shock monetary policy shocks in Tunisia over the 1997-2015 and to provide information concerning the evolution of the economy response to these shocks. Compared with previous studies of this country, the main finding is the statistically significant effect of interest rate on the variables of the real economy. The article shows also that Bayesian Structural VAR model can explains the 2011 recession

Credit Risk Prediction based on Bayesian estimation of logistic regression model with random effects

The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using information on a sample of 528 Tunisian firms and 26 financial ratios,we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result