We introduce a new wavelet based procedure for detecting outliers in financial discrete time series.The procedure focuses on the analysis of residuals obtained from a model fit,and applied to the Generalized Autoregre...We introduce a new wavelet based procedure for detecting outliers in financial discrete time series.The procedure focuses on the analysis of residuals obtained from a model fit,and applied to the Generalized Autoregressive Conditional Heteroskedasticity(GARCH)like model,but not limited to these models.We apply the Maximal-Overlap Discrete Wavelet Transform(MODWT)to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers.Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform(DWT).The series sample size does not need to be a power of 2 and the transform can explore any wavelet filter and be run up to the desired level.Simulated wavelet quantiles from a Normal and Student t-distribution are used as threshold for the maximum of the absolute value of wavelet coefficients.The performance of the procedure is illustrated and applied to two real series:the closed price of the Saudi Stock market and the S&P 500 index respectively.The efficiency of the proposed method is demonstrated and can be considered as a distinct important addition to the existing methods.展开更多
Exposure to market risk is a core objective of the Capital Asset Pricing Model(CAPM)with a focus on systematic risk.However,traditional OLS Beta model estimations(Ordinary Least Squares)are plagued with several statis...Exposure to market risk is a core objective of the Capital Asset Pricing Model(CAPM)with a focus on systematic risk.However,traditional OLS Beta model estimations(Ordinary Least Squares)are plagued with several statistical issues.Moreover,the CAPM considers only one source of risk and supposes that investors only engage in similar behaviors.In order to analyze short and long exposures to different sources of risk,we developed a Time–Frequency Multi-Betas Model with ARMA-EGARCH errors(Auto Regressive Moving Average Exponential AutoRegressive Conditional Heteroskedasticity).Our model considers gold,oil,and Fama–French factors as supplementary sources of risk and wavelets decompositions.We used 30 French stocks listed on the CAC40(Cotations Assistées Continues 40)within a daily period from 2005 to 2015.The conjugation of the wavelet decompositions and the parameters estimates constitutes decision-making support for managers by multiplying the interpretive possibilities.In the short-run,(“Noise Trader”and“High-Frequency Trader”)only a few equities are insensitive to Oil and Gold fluctuations,and the estimated Market Betas parameters are scant different compared to the Model without wavelets.Oppositely,in the long-run,(fundamentalists investors),Oil and Gold affect all stocks but their impact varies according to the Beta(sensitivity to the market).We also observed significant differences between parameters estimated with and without wavelets.展开更多
文摘We introduce a new wavelet based procedure for detecting outliers in financial discrete time series.The procedure focuses on the analysis of residuals obtained from a model fit,and applied to the Generalized Autoregressive Conditional Heteroskedasticity(GARCH)like model,but not limited to these models.We apply the Maximal-Overlap Discrete Wavelet Transform(MODWT)to the residuals and compare their wavelet coefficients against quantile thresholds to detect outliers.Our methodology has several advantages over existing methods that make use of the standard Discrete Wavelet Transform(DWT).The series sample size does not need to be a power of 2 and the transform can explore any wavelet filter and be run up to the desired level.Simulated wavelet quantiles from a Normal and Student t-distribution are used as threshold for the maximum of the absolute value of wavelet coefficients.The performance of the procedure is illustrated and applied to two real series:the closed price of the Saudi Stock market and the S&P 500 index respectively.The efficiency of the proposed method is demonstrated and can be considered as a distinct important addition to the existing methods.
文摘Exposure to market risk is a core objective of the Capital Asset Pricing Model(CAPM)with a focus on systematic risk.However,traditional OLS Beta model estimations(Ordinary Least Squares)are plagued with several statistical issues.Moreover,the CAPM considers only one source of risk and supposes that investors only engage in similar behaviors.In order to analyze short and long exposures to different sources of risk,we developed a Time–Frequency Multi-Betas Model with ARMA-EGARCH errors(Auto Regressive Moving Average Exponential AutoRegressive Conditional Heteroskedasticity).Our model considers gold,oil,and Fama–French factors as supplementary sources of risk and wavelets decompositions.We used 30 French stocks listed on the CAC40(Cotations Assistées Continues 40)within a daily period from 2005 to 2015.The conjugation of the wavelet decompositions and the parameters estimates constitutes decision-making support for managers by multiplying the interpretive possibilities.In the short-run,(“Noise Trader”and“High-Frequency Trader”)only a few equities are insensitive to Oil and Gold fluctuations,and the estimated Market Betas parameters are scant different compared to the Model without wavelets.Oppositely,in the long-run,(fundamentalists investors),Oil and Gold affect all stocks but their impact varies according to the Beta(sensitivity to the market).We also observed significant differences between parameters estimated with and without wavelets.
