This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model(CAPM)framework.The dynamic of systematic risk across time and frequency is analyzed to investigate...This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model(CAPM)framework.The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness.Furthermore,we emphasize the effect of an investor’s investment horizon on the robustness of portfolio characteristics.We use a daily panel of French stocks from 2012 to 2022.Results show that varying systematic risk varies in time and frequency,and that its short and long-run evolutions differ.We observe differences in short and long dynamics,indicating that a stock’s betas differently fluctuate to early announcements or signs of events.However,short-run and long-run betas exhibit similar dynamics during persistent shocks.Betas are more volatile during times of crisis,resulting in greater or lesser robustness of risk profiles.Significant differences exist in short-run and longrun risk profiles,implying a different asset allocation.We conclude that the standard CAPM assumes short-run investment.Then,investors should consider time–frequency CAPM to perform systematic risk analysis and portfolio allocation.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This study proposes a wavelets approach to estimating time–frequency-varying betas in the capital asset pricing model(CAPM)framework.The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness.Furthermore,we emphasize the effect of an investor’s investment horizon on the robustness of portfolio characteristics.We use a daily panel of French stocks from 2012 to 2022.Results show that varying systematic risk varies in time and frequency,and that its short and long-run evolutions differ.We observe differences in short and long dynamics,indicating that a stock’s betas differently fluctuate to early announcements or signs of events.However,short-run and long-run betas exhibit similar dynamics during persistent shocks.Betas are more volatile during times of crisis,resulting in greater or lesser robustness of risk profiles.Significant differences exist in short-run and longrun risk profiles,implying a different asset allocation.We conclude that the standard CAPM assumes short-run investment.Then,investors should consider time–frequency CAPM to perform systematic risk analysis and portfolio allocation.
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.
