Volatilidade de dados intradiários: comportamento multiescala do Ibovespa frente à pandemia COVID-19
DOI:
https://doi.org/10.5433/1679-0375.2021v42n1Suplp25Palavras-chave:
Volatilidade, Índice Ibovespa, Modelos APARCH, Transformada de WaveletsResumo
Em mercados financeiros, a modelagem da volatilidade vem sendo uma estratégia muito utilizada por refletir as incertezas sobre as variações dos preços dos ativos. Incorporando peculiaridades de séries financeiras, este estudo estimou a volatilidade para o índice intradiário do mercado acionário brasileiro (Ibovespa) por meio de modelos ARIMA-APARCH em diferentes frequências temporais com o auxílio da técnica de decomposição wavelet MODWT. Este trabalho propõe a análise dos impactos dos componentes de frequência no comportamento da volatilidade de retornos intradiários com o uso de séries de detalhes wavelet em diferentes horizontes temporais, em um período atípico nos mercados financeiros mundiais, gerado pela pandemia do COVID-19. Os resultados empíricos sugerem baixa volatilidade incondicional e fortes sinais de persistência em todas as frequências analisadas. A assimetria na volatilidade é evidenciada nas frequências maiores, com efeito alavancagem presente apenas nas séries de detalhes com variações de 15-120 min., o que é corroborado com os resultados obtidos com a série reconstruída. Os comportamentos evidenciados impactam na elaboração de estratégias de investimento de curto prazo e gerenciamento de risco, uma vez que os choques positivos e negativos, como os dados pela pandemia mundial do COVID-19, têm impactos diferenciados sobre a volatilidade dos retornos em prazos menores. As informações obtidas podem contribuir na análise de futuros eventos atípicos no mercado acionário brasileiro embasando a tomada de decisão dos agentes econômicos.Downloads
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ROSSI, M. The efficient market hypothesis and calendar anomalies: a literature review. International Journal of Managerial and Financial Accounting, Genebra, v. 7, n.3-4, p. 285-296, 2015.
SCHULMEISTER, S. Profitability of technical stock trading: Has it moved from daily to intraday data? Review of Financial Economics, New Orleans, v. 18, n. 4, p. 190-201, 2009.
SHAH, A.; TALI, A.; FAROOQ, Q. Beta through the prism of wavelets. Financial Innovation, London, v. 4, n. 1, p. 18, 2018.
WUERTZ, D.; SETZ, T.; CHALABI, Y.; BOUDT, C.; CHAUSSE, P.; MIKLOVAC, M. fGarch: Rmetrics: autoregressive conditional heteroskedastic modelling. R package version 3042.83.1. 2019. Disponível em: https://CRAN.Rproject.org/package=fGarch. Acesso em: 15 de ago. 2019.
AUDRINO, F.; HU, Y. Volatility forecasting: downside risk, jumps and leverage effect. Econometrics, Multidisciplinary Digital Publishing Institute, Basel, v. 4, n. 1, p. 8, 2016.
BAUR, D. G.; DIMPFL, T. Think again: volatility asymmetry and volatility persistence. Studies in Nonlinear Dynamics & Econometrics, Berlin, v. 23, n. 1, 2018.
BIAGE, M. Analysis of shares frequency components on daily value-at-risk in emerging and developed markets. Physica A: Statistical Mechanics and its Applications, London, v. 532, p. 121798, 2019.
BLACK, F. Studies of stock market volatility changes. In: AMERICAN STATISTICAL ASSOCIATION. Business and Economic Statistics Section, 1976. Proceedings [...]. Washington, DC: American Statistical Association, 1976. p. 171-181.
BOX, G.; JENKINS, G. Time series analysis: forecasting and control, holden day. San Francisco: Wiley, 1970.
CONSTANTINE, W.; PERCIVAL, D. Wavelet methods for time series analysis. R package version 2.0-3. Cambridge: Cambridge University Press, 2017.
CROWLEY, P. M. An intuitive guide to wavelets for economists. Journal of Economic Surveys, Clevedon, v. 21, n. 2, p. 207-267, 2007
DALY, K. Financial volatility: Issues and measuring techniques. Physica A: statistical mechanics and its applications, London, v. 387, n. 11, p. 2377-2393, 2008.
DAUBECHIES, I. Ten lectures on wavelets. Philadelphia: Siam, 1992.
DICKEY, D. A.; FULLER, W. A. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, New York, v. 74, n. 366a, p. 427-431, 1979.
DICKEY, D. A.; FULLER, W. A. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, Chicago, p. 1057-1072, 1981.
DING, Z.; GRANGER, C. W.; ENGLE, R. F. A long memory property of stock market returns and a new model. Journal of Empirical Finance, North-Holland, v. 1, n. 1, p. 83-106, 1993.
ENGLE, R. F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, p. 987-1007, 1982.
GALLEGATI, M.; SEMMLER, W. (ed.). Wavelet applications in economics and finance. Switzerland: Springer, 2014.
HASBROUCK, J. High frequency quoting: short-term volatility in bids and offers. Journal of Financial and Quantitative Analysis, Cambridge, 2016.
IN, F.; KIM, S. An introduction to wavelet theory in finance: a wavelet multiscale approach. Singapore: World scientific, 2013.
JENSEN, M. J.; WHITCHER, B. Measuring the impact intradaily events have on the persistent nature of volatility. In: GALLEGATI, M.; SEMMLER, W. Wavelet applications in economics and finance. New York: Springer, 2014. p. 103-129.
KOVALEVSKY, S. QuantTools: enhanced quantitative trading modelling. R package version 0.5.7. 2018. Disponível em: https://CRAN.R-project.org/package=QuantTools. Acesso em: 15 fev. 2019.
KUMAR, A. S.; ANANDARAO, S. Volatility spillover in crypto-currency markets: Some evidences from garch and wavelet analysis. Physica A: Statistical Mechanics and its Applications, London, v. 524, p. 448-458, 2019.
LAMBERT, P.; LAURENT, S. Modelling financial time series using garch-type models and a skewed student density. Liège: Université de Liège, 2001. Mimeo.
LATIF, M.; ARSHAD, S.; FATIMA, M.; FAROOQ, S. Market efficiency, market anomalies, causes, evidences, and some behavioral aspects of market anomalies. Research Journal of Finance and Accounting, [s. l.], v. 2, n. 9, p. 1-13, 2011.
MALLAT, S. Multiresolution approximations and wavelet orthonormal bases of l2(r). Transactions of the American Mathematical Society, New York, v. 315, n. 1, p. 69-87, 1989.
MORETTIN, P. A. Econometria financeira: um curso em séries temporais financeiras. 3. ed. São Paulo: Blucher, 2017.
NAVA, N.; MATTEO, T. D.; ASTE, T. Anomalous volatility scaling in high frequency financial data. Physica A: Statistical Mechanics and its Applications, London, v. 447, p. 434-445, 2016.
OMANE-ADJEPONG, M.; ABABIO, K. A.; ALAGIDEDE, P. Time-frequency analysis of behaviourally classified financial asset markets. Research in International Business and Finance, [s .l.], 2019.
PAN, Z.; LIU, L. Forecasting stock return volatility: A comparison between the roles of short-term and long-term leverage effects. Physica A: Statistical Mechanics and its Applications, London, v. 492, p. 168-180, 2018.
PATTON, A. J.; SHEPPARD, K. Good volatility, bad volatility: Signed jumps and the persistence of volatility. Review of Economics and Statistics, Cambridge, v. 97, n. 3, p. 683-697, 2015.
PERCIVAL, D. B.; WALDEN, A. T. Wavelet methods for time series analysis. Cambridge: Cambridge University Press, 2000. v. 4.
R CORE TEAM. R: A language and environment for statistical computing. 2020. Disponível em: https://www.Rproject.org/. Acesso em: 15 de ago. 2019.
RAMZAN, S.; RAMZAN, S.; ZAHID, F. M. Modeling and forecasting exchange rate dynamics in pakistan using arch family of models. Electronic Journal of Applied Statistical Analysis, Lecce, v. 5, n. 1, p. 15-29, 2012.
ROSSI, M. The efficient market hypothesis and calendar anomalies: a literature review. International Journal of Managerial and Financial Accounting, Genebra, v. 7, n.3-4, p. 285-296, 2015.
SCHULMEISTER, S. Profitability of technical stock trading: Has it moved from daily to intraday data? Review of Financial Economics, New Orleans, v. 18, n. 4, p. 190-201, 2009.
SHAH, A.; TALI, A.; FAROOQ, Q. Beta through the prism of wavelets. Financial Innovation, London, v. 4, n. 1, p. 18, 2018.
WUERTZ, D.; SETZ, T.; CHALABI, Y.; BOUDT, C.; CHAUSSE, P.; MIKLOVAC, M. fGarch: Rmetrics: autoregressive conditional heteroskedastic modelling. R package version 3042.83.1. 2019. Disponível em: https://CRAN.Rproject.org/package=fGarch. Acesso em: 15 de ago. 2019.
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Publicado
2021-04-29
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Carvalho, M. de M., Pala, L. O. de O., & Sáfadi, T. (2021). Volatilidade de dados intradiários: comportamento multiescala do Ibovespa frente à pandemia COVID-19. Semina: Ciências Exatas E Tecnológicas, 42(1Supl), 25–34. https://doi.org/10.5433/1679-0375.2021v42n1Suplp25
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