Nonlinearities in the CAPM: Evidence from Developed and Emerging Markets

Neslihanoğlu, Serdar; Sogiakas, Vasilios; McColl, John H.; Lee, Duncan

doi:10.1002/for.2389

Cited by 13 publications

(11 citation statements)

References 47 publications

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“…Harvey & Siddique (1999) proposed an analysis of the effect of co-skewness on asset prices. Many studies confirm that higher co-moments are risk factors influencing asset returns and better predicting\returns than the mean-variance approach, both in developed and emerging markets (Fernandes, Fonseca, & Iquiapaza, 2018;Galagedera et al, 2003;Mora-Valencia, Perote, & Arias, 2017;Neslihanoglu, Sogiakas, Mccoll, & Lee, 2017;Teplova & Shutova, 2011). Chiang (2016) investigated skewness and co-skewness pricing for bond return and suggested these measures are at least conditionally significant.…”

Section: Literature Reviewmentioning

confidence: 98%

Further evidence on the validity of CAPM: The Warsaw Stock Exchange application

Markowski

2020

JEM

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Aim/purpose-The purpose of the research is to verify the Capital Asset Pricing Model (CAPM) in the Polish capital market based on a conventional and downside risk approach. Design/methodology/approach-The author in this study, using individual securities and portfolios, compares the unconditional risk-return relationships with the conditional risk, estimated in up and down market using realised returns in cross-sectional regressions. Except for a beta coefficient, the CAPM is tested with co-skewness as a higher order co-moment and downside betas as a risk measure in a downside approach. Findings-The unconditional regressions give evidence of existing risk premium associated with co-skewness and downside beta, and confirmed the validity of the downside CAPM. The author, based on conditional relations, found that risk-return relations depend on the state of the stock market. The average premium for systematic risk in term of beta coefficient is significantly positive in up market periods and significantly negative in down market periods. The use of conditional models did not explicitly confirm the suitability of co-skewness in asset pricing. Research implications/limitations-The main implications include the fact that the conventional beta coefficient is an appropriate risk measure when we consider using it separately for up and down market. A valuable extension of this research would be a benchmarking analysis to compare results for the Polish capital market against other emerging and developed markets. Originality/value/contribution-The author in this paper proposes an alternative approach to testing risk-return relationships based on the CAPM in comparison to commonly used tests founded upon joint estimations of these relationships in periods of both positive and negative market excess return. The noteworthy contribution of this study is an application of the downside beta coefficient and the co-skewness coefficient in crosssectional regressions.

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Section: Literature Reviewmentioning

confidence: 98%

Further evidence on the validity of CAPM: The Warsaw Stock Exchange application

Markowski

2020

JEM

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“…The time-varying parameter extension of the linear model (Eq. 2 ) is called the time-varying linear model (Tv-LM) in the mean reverting specification form of the state space model via Kalman filter [ 14 ] algorithm. This allows one to assess the instability and to estimate the time-varying parameters of the Tv-LM.…”

Section: Methodsmentioning

confidence: 99%

“…None of these have yet provided benchmark models for assessing the relationship between the confirmed cases of COVID-19 in individual countries and that of the world in terms of comparing the LM with its GAM and Tv-LM via Kalman filter [ 8 ] extensions in the in- and out-of-sample procedures. The Kalman filter, which is also thought to be one of the most preferred algorithms for modelling time-varying linearity due to the accuracy of the results which it yields [ 13 , 14 ], has yet to be undertaken for measuring time-varying linearity in the COVID-19 literature.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear models: a case of the COVID-19 confirmed rates in top 8 worst affected countries

Neslihanoğlu

2021

Nonlinear Dyn

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Over the last 9 months, the most prominent global health threat has been COVID-19. It first appeared in Wuhan, China, and then rapidly spread throughout the world. Since no treatment or preventative strategy has been identified until this time, millions of people across the world have been seriously affected by COVID-19. The modelling and prediction of confirmed COVID-19 cases have been given much attention by government policymakers for the purpose of combating it more effectively. For this purpose, the modelling and prediction performances of the linear model (LM), generalized additive model(GAM) and the time-varying linear model (Tv-LM) via Kalman filter are compared. This has never yet been undertaken in the literature. This comparative analysis also evaluates the linear relationship between the confirmed cases of COVID-19 in individual countries with the world. The analysis is implemented using daily COVID-19 confirmed rates of the top 8 most heavily affected countries and that of the world between 11 March and 21 December 2020 and 14-day forward predictions. The empirical findings show that the Tv-LM outperforms others in terms of model fit and predictability, suggesting that the relationship between each country’s rates with the world’s should be locally linear, not globally linear.

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“…While the Tv-LMM via the Kalman filter algorithm has been investigated extensively in several stock markets, firms, and industries, there is limited research on its use in crypto markets (e.g., Raimundo Júnior et al 2020 ; Bianchi et al 2020 ). Moreover, Neslihanoglu et al ( 2017 ) proposed the generalized additive model (GAM) for flexibility in the rigid linearity shape of the LMM in developed and emerging stock markets, but no studies have used this approach to explore crypto markets. Given the lack of existing literature in this area, this research explores extensions of prior research on crypto markets.…”

Section: Introductionmentioning

confidence: 99%

Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

Neslihanoğlu

2021

Financ Innov

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This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.

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Nonlinearities in the CAPM: Evidence from Developed and Emerging Markets

Cited by 13 publications

References 47 publications

Further evidence on the validity of CAPM: The Warsaw Stock Exchange application

Further evidence on the validity of CAPM: The Warsaw Stock Exchange application

Nonlinear models: a case of the COVID-19 confirmed rates in top 8 worst affected countries

Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

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