A Model-Free Measure of Aggregate Idiosyncratic Volatility and the Prediction of Market Returns

García, René; Mantilla-García, Daniel; Martellini, Lionel

doi:10.1017/s0022109014000489

Cited by 66 publications

(33 citation statements)

References 53 publications

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“…Similar to Bakaert et al 2012, Nartea et al (2013) and Garcia et al (2014), we fit the Markov regimeswitching model with a first-order autocorrelation structure (AR (1)) for three volatility components, and particularly interested in the idiosyncratic volatility. In this model, two regimes are indexed by a discrete state variable ( ), following a Markov-chain process with constant transition probabilities.…”

Section: Regimes Switchingmentioning

confidence: 99%

The Idiosyncratic Risk in Chinese Stock Market

Darby

Zhang

2019

SSRN Journal

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Using daily stock return data of all listed firms in Chinese stock market from 1998 to 2018, we disaggregate the volatility of common stocks at the market, industry and firm levels. We find market volatility, on average, is the highest while firm volatility tends to lead to market and industry volatility series. None long-term trend time series behaviour exists for all three volatility series and firm volatility is best described by an autoregressive process with regime shifts associated with the structural market reforms or volatile market movements. We further proceed to identify the source of volatility at the industry level and find the idiosyncratic volatility in the largest manufacturing industry not only accounts for the largest proportion in the aggregate firm volatility but also is the lead indicator for the idiosyncratic volatility of other industries. Finally, unlike Brandt et al. [Review of Financial Studies 23(2): 863-899 (2010)], we find the idiosyncratic volatility in Chinese stock market is associated with high stock trading activities by institutional investors, the result of which is also robust when using other measures of idiosyncratic volatility.

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Section: Regimes Switchingmentioning

confidence: 99%

The Idiosyncratic Risk in Chinese Stock Market

Darby

Zhang

2019

SSRN Journal

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“…Meanwhile, another strand of the literature provides ample evidence linking stock market volatility to real economic activity [4,5] and stock market volatility to future aggregate stock returns [27][28][29][30]. In a recent study, applying linear and nonlinear causality tests, Choudhry et al [6] showed that a bidirectional causal relationship exists between stock market volatility and the business cycle in a sample of four major economies without using return dispersion in their multivariate tests.…”

Section: Literature Reviewmentioning

confidence: 99%

Equity Return Dispersion and Stock Market Volatility: Evidence from Multivariate Linear and Nonlinear Causality Tests

Demirer

Gupta

et al. 2019

Sustainability

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We employ bivariate and multivariate nonlinear causality tests to document causality from equity return dispersion to stock market volatility and excess returns, even after controlling for the state of the economy. Expansionary (contractionary) market states are associated with a low (high) level of equity return dispersion, indicating asymmetries in the relationship between return dispersion and economic conditions. Our findings indicate that both return dispersion and business conditions are valid joint forecasters of stock market volatility and excess returns and that return dispersion possesses incremental information regarding future stock return dynamics beyond that which can be explained by the state of the economy.

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“…Para este estudo, a CSV foi desenvolvida usando como base Garcia et al (2014). Considerando a CSV ponderada em igualdade, a seguir denominada CSV t EW em que w it = 1/N t i e t. Assumindo que r t EW representa o retorno do portfólio igualmente ponderado, tem-se:…”

Section: Amostraunclassified

Previsão de retornos e preços das ações a partir de dados contábeis condicionado ao ciclo de vida das firmas

Mikosz

Roma

Louzada

et al. 2019

Rev. contab. organ.

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Este artigo investiga o efeito dos estágios do ciclo de vida das firmas na determinação dos retornos e preços esperados de ações brasileiras a partir do modelo contábil de avaliação desenvolvido por Lyle, Callen e Elliott (2013). Para definição dos estágios do ciclo de vida, adotou-se a metodologia de Dickinson (2011), utilizando uma proxy parcimoniosa para as firmas, estruturada a partir da demonstração do fluxo de caixa com forte aderência as teorias econômicas. Os dados foram coletados nas bases de dados Bloomberg, Quantum e do NEFIN/FEA/USP e as informações foram geradas no nível das firmas. Os resultados sugerem que o modelo para o período analisado não apresenta capacidade de previsão de retornos no Brasil, dada pela não significância das variáveis mesmo considerando os ativos em diferentes estágios de ciclo de vida da firma, já para as previsões de preços houve considerável poder explicativo, especialmente no estágio de maturidade.

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A Model-Free Measure of Aggregate Idiosyncratic Volatility and the Prediction of Market Returns

Cited by 66 publications

References 53 publications

The Idiosyncratic Risk in Chinese Stock Market

The Idiosyncratic Risk in Chinese Stock Market

Equity Return Dispersion and Stock Market Volatility: Evidence from Multivariate Linear and Nonlinear Causality Tests

Previsão de retornos e preços das ações a partir de dados contábeis condicionado ao ciclo de vida das firmas

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