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Multi-regime nonlinear capital asset pricing models
A multiple-regime threshold generalized autoregressive conditionally heteroskedastic capital asset pricing model is introduced. The model captures asymmetric risk through allowing market beta to change discretely between regimes that are driven by market information. Asymmetric volatility and mean e...
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Published in: | Quantitative finance 2011-09, Vol.11 (9), p.1421-1438 |
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container_end_page | 1438 |
container_issue | 9 |
container_start_page | 1421 |
container_title | Quantitative finance |
container_volume | 11 |
creator | Chen, Cathy W. S. Gerlach, Richard H. Lin, Ann M. H. |
description | A multiple-regime threshold generalized autoregressive conditionally heteroskedastic capital asset pricing model is introduced. The model captures asymmetric risk through allowing market beta to change discretely between regimes that are driven by market information. Asymmetric volatility and mean equation dynamics are also captured. We confirm the time-varying nature of market risk, in response to changes in the market, and that this discrete time variation can differ across assets. These findings could have important implications for optimizing investment decisions: e.g. in risk assessment, portfolio selection and hedging decisions. |
doi_str_mv | 10.1080/14697680902968013 |
format | article |
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S.</creatorcontrib><creatorcontrib>Gerlach, Richard H.</creatorcontrib><creatorcontrib>Lin, Ann M. H.</creatorcontrib><title>Multi-regime nonlinear capital asset pricing models</title><title>Quantitative finance</title><description>A multiple-regime threshold generalized autoregressive conditionally heteroskedastic capital asset pricing model is introduced. The model captures asymmetric risk through allowing market beta to change discretely between regimes that are driven by market information. Asymmetric volatility and mean equation dynamics are also captured. We confirm the time-varying nature of market risk, in response to changes in the market, and that this discrete time variation can differ across assets. These findings could have important implications for optimizing investment decisions: e.g. in risk assessment, portfolio selection and hedging decisions.</description><subject>ARCH</subject><subject>Asymmetry</subject><subject>Bayesian analysis</subject><subject>Capital assets</subject><subject>CAPM</subject><subject>Financial time series</subject><subject>Investment policy</subject><subject>Risk assessment</subject><subject>Studies</subject><subject>Time series economics</subject><subject>Value at Risk</subject><subject>Volatility</subject><subject>Volatility modelling</subject><issn>1469-7688</issn><issn>1469-7696</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LAzEQhoMoWKs_wNvifTUfmy_wIsUvqHjRc0g32Zqym90mWbX_3tRKL4Ie3swwmfeZYQA4R_ASQQGvUMUkZwJKiGUOiByAybZWcibZ4T4X4hicxLiCEFEI5QSQp7FNrgx26Tpb-N63zlsdiloPLum20DHaVAzB1c4vi643to2n4KjRbbRnP3EKXu9uX2YP5fz5_nF2My_rilepNLrimDFLKm4wopQQvmB2IQVkjBKGMaECc2EwrTE2FCGuWWOMhhxbi4UkU3Cx4w6hX482JrXqx-DzSCUE55IyhnMT2jXVoY8x2EblZTsdNgpBtT2N-nWa7JnvPMEOtt4bkm7Wo_apUe-KaITys8nC8Dt1WTJr2P5VGGUuEeotdRl3vcM53_Sh0x99aE2mbdo-NEH72kVF_tqG_2v_5VLpM5Ev7wGTKA</recordid><startdate>201109</startdate><enddate>201109</enddate><creator>Chen, Cathy W. 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source | EBSCOhost Business Source Ultimate; EBSCOhost Econlit with Full Text; Taylor and Francis Social Sciences and Humanities Collection |
subjects | ARCH Asymmetry Bayesian analysis Capital assets CAPM Financial time series Investment policy Risk assessment Studies Time series economics Value at Risk Volatility Volatility modelling |
title | Multi-regime nonlinear capital asset pricing models |
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