Abstract
Financial time series analysis has been one of the hottest research topics in the recent decades. In this chapter, we illustrate the stylized facts of financial time series by real financial data. To characterize these facts, new models different from the Box-Jenkins ones are needed. And for this reason, ARCH models were firstly proposed by R. F. Engle in 1982 and have been extended by a great number of scholars since then. We also demonstrate how to use Python and its libraries to implement ARCH and some extensions modeling.
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Notes
- 1.
The following all Python code in this chapter is also validated with Python of V. 3.9.7, statsmodels of V. 0.13.1, and arch of V. 5.1.0.
References
Campbell, J.Y., Lo, A.W., MacKinlay, A.C.: The Econometrics of Financial Markets, 2nd edn. Princeton University Press, Princeton, NJ (2012)
Engle, R.F.: Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987–1007 (1982)
Fan, J., Yao, Q.: Nonlinear Time Series: Nonparametric and Parametric Methods. Springer, New York (2003)
Franke, J., Härdle, W.K., Hafner, C.M.: Statistics of Financial Markets: An Introduction, 5th edn. Springer, Switzerland (2019)
Glosten, L.R., Jagannathan, R., Runkle, D.E.: On the relation between the expected value and the volatility of nominal excess return on stocks. J. Financ. 48, 1779–1801 (1993)
Hilpisch, Y.: Derivatives analytics with Python: data analysis, models, simulation, calibration and hedging. Wiley, West Sussex, UK (2015)
Nelson, D.: Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59, 347–370 (1991)
Sheppard, K.: Bashtage/arch: Release 4.15. Zenodo (2020). https://doi.org/10.5281/zenodo.593254. Cited 24 June 2020
Tsay, R.S.: Analysis of Financial Time Series, 3rd edn. Wiley, Hoboken, NJ (2010)
Zakoian, J.M.: Threshold heteroscedastic models. J. Econ. Dyn. Control 18, 931–955 (1994)
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Huang, C., Petukhina, A. (2022). Financial Time Series and Related Models. In: Applied Time Series Analysis and Forecasting with Python. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-13584-2_6
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DOI: https://doi.org/10.1007/978-3-031-13584-2_6
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