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Keywords = capital asset pricing model filter

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13 pages, 3767 KB  
Article
Analyzing Asymmetric Volatility and Multifractal Behavior in Cryptocurrencies Using Capital Asset Pricing Model Filter
by Minhyuk Lee, Younghwan Cho, Seung Eun Ock and Jae Wook Song
Fractal Fract. 2023, 7(1), 85; https://doi.org/10.3390/fractalfract7010085 - 12 Jan 2023
Cited by 4 | Viewed by 3075
Abstract
This research analyzes asymmetric volatility and multifractality in four representative cryptocurrencies using index-based asymmetric multifractal detrended fluctuation analysis. We suggest investigating an idiosyncratic risk premium, which can be obtained by removing the market influence in the cryptocurrency return series. We call the process [...] Read more.
This research analyzes asymmetric volatility and multifractality in four representative cryptocurrencies using index-based asymmetric multifractal detrended fluctuation analysis. We suggest investigating an idiosyncratic risk premium, which can be obtained by removing the market influence in the cryptocurrency return series. We call the process a capital asset pricing model filter. The analyses on the original return series showed no significant sign of asymmetric volatility. However, the filter revealed a distinct asymmetric volatility, distinguishing the uptrend and downtrend fluctuations. Furthermore, the analyses on the idiosyncratic risk premium detected some cases of asymmetry in the degree and source of multifractality, whereas that on the original return series failed to detect the asymmetry. In conclusion, in a highly volatile market, the capital asset pricing model filter can improve an investigation of the asymmetric multifractality in cryptocurrencies. Full article
(This article belongs to the Special Issue Fractal and Multifractal Analysis in Financial Markets)
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15 pages, 425 KB  
Article
Contribution to the Valuation of BRVM’s Assets: A Conditional CAPM Approach
by Mamadou Cisse, Mamadou Konte, Mohamed Toure and Smael Afolabi Assani
J. Risk Financial Manag. 2019, 12(1), 27; https://doi.org/10.3390/jrfm12010027 - 6 Feb 2019
Cited by 6 | Viewed by 4472
Abstract
The conditional capital asset pricing model (CAPM) theory postulates that the systematic risk ( β ) of an asset or portfolio varies over time. Several dynamics are thus given to systematic risk in the literature. This article looks for the dynamic that seems [...] Read more.
The conditional capital asset pricing model (CAPM) theory postulates that the systematic risk ( β ) of an asset or portfolio varies over time. Several dynamics are thus given to systematic risk in the literature. This article looks for the dynamic that seems to best explain the returns of the assets of the Regional Stock Exchange of West Africa (BRVM) by comparing two dynamics: one by the Kalman filter (assuming that the β follow a random walk) and the other by the Markov switching (MS) model (assuming that β varies according to regimes) for four portfolios of the BRVM. Having found a link between the beta of the market portfolio and the size criterion (measured by capitalization), the two previous models were re-estimated with the addition of the SMB (Small Minus Big) variable. The results show according to the RMSE criterion that the estimation by the Kalman filter fits better than MS, which suggests that investors cannot anticipate systematic risk because of its high volatility. Full article
(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)
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