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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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
Como Citar
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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