Volatility of intraday financial data: Multiscale Ibovespa behavior under to the COVID-19 pandemic
DOI:
https://doi.org/10.5433/1679-0375.2021v42n1Suplp25Keywords:
Volatility, Ibovespa index, ARIMA-APARCH models, Wavelet transformAbstract
In financial markets, volatility modeling has been a strategy widely used because it reflects uncertainties about changes in asset prices. Incorporating peculiarities of financial series, this study estimated the volatility for the intraday index of the Brazilian stock market (Ibovespa) using ARIMA-APARCH models in different time frequencies with the aid of the wavelet MODWT decomposition technique. This work proposes an analysis of the impacts of the frequency components on the behavior of the volatility of intraday returns using the series of details wavelet in different time horizons, in an atypical period in the global financial markets, generated by the COVID-19 pandemic. The empirical results suggest low unconditional volatility and strong signs of persistence in all analyzed frequencies. The asymmetry in volatility is evidenced in the higher frequencies, the leverage effect being present only in the series of details with variations of 15-120 min., which is corroborated with the results obtained with the reconstructed series. The evidenced behaviors have an impact on the elaboration of short-term investment strategies and risk management, since the positive and negative shocks, such as those given by the world pandemic of COVID-19, have different impacts on the volatility of returns in shorter periods. The information obtained can contribute to the analysis of future atypical events in the Brazilian stock market, supporting the decision-making of economic agents.Downloads
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References
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SHAH, A.; TALI, A.; FAROOQ, Q. Beta through the prism of wavelets. Financial Innovation, London, v. 4, n. 1, p. 18, 2018.
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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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Published
2021-04-29
How to Cite
Carvalho, M. de M., Pala, L. O. de O., & Sáfadi, T. (2021). Volatility of intraday financial data: Multiscale Ibovespa behavior under to the COVID-19 pandemic. Semina: Ciências Exatas E Tecnológicas, 42(1Supl), 25–34. https://doi.org/10.5433/1679-0375.2021v42n1Suplp25
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