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Analysis of shares frequency components on daily value-at-risk in emerging and developed markets

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  • Biage, Milton

Abstract

Value-at-Risk was estimated using the technique of wavelet decomposition with the goal to analyze the frequency components’ impacts on variances of daily stock returns, and VaR forecasts. Daily returns of twenty-one shares of the Ibovespa and daily returns of twenty-two shares of the DJIA were used. The FIGARCH(1,d,1) model was applied to the reconstructed returns to model and establish the prediction of conditional variance, applying the rolling window technique. The Value-at-Risk was then estimated, and the results showed that the DJIA shares showed more efficient market behavior than those of Ibovespa. The differences in behavior induce to affirm that VaRs, used in the analysis of financial assets from different markets with different governance premises, should be estimated by series of returns reconstructed by aggregations of components of different frequencies. A set of back-testing was applied to confront the estimated VaRs, which demonstrated that the estimations of VaRs models are consistent.

Suggested Citation

  • Biage, Milton, 2019. "Analysis of shares frequency components on daily value-at-risk in emerging and developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 532(C).
    • Handle: RePEc:eee:phsmap:v:532:y:2019:i:c:s0378437119310568
    DOI: 10.1016/j.physa.2019.121798
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    Citations

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    Cited by:

    1. Chen, Xuehui & Zhu, Hongli & Zhang, Xinru & Zhao, Lutao, 2022. "A novel time-varying FIGARCH model for improving volatility predictions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    2. Marcela de Marillac Carvalho & Luiz Otávio de Oliveira Pala & Gabriel Rodrigo Gomes Pessanha & Thelma Sáfadi, 2021. "Asymmetric dependence of intraday frequency components in the Brazilian stock market," SN Business & Economics, Springer, vol. 1(6), pages 1-18, June.
    3. Hong Qiu & Genhua Hu & Yuhong Yang & Jeffrey Zhang & Ting Zhang, 2020. "Modeling the Risk of Extreme Value Dependence in Chinese Regional Carbon Emission Markets," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    4. Yingchao Zou & Kaijian He, 2022. "Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model," Mathematics, MDPI, vol. 10(14), pages 1-11, July.

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