Abstract
The empirical studies included in this book are done over multiple time scales, and a large part with high-frequency time series. The usual notations need to be extended in order to denote accurately the time and time interval(s) dependencies in the computed time series like returns and volatilities. For inhomogeneous time series, a set of convenient operators is introduced, which makes it possible to estimate efficiently derived quantities like returns, volatilities, and volatility changes, with definitions that are appropriate for random time series. The choice of a convenient normalization annualizes systematically the derived quantities in order to easily compare statistics across time horizons. The definitions of the average, expectation, and histogram complete the set of basic tools needed for the empirical analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breymann, W., Zumbach, G., Dacorogna, M.M., Müller, U.A.: Dynamical deseasonalization in OTC and localized exchange-traded markets. Internal document WAB.2000-01-31, Olsen & Associates, Seefeldstrasse 233, 8008 Zürich, Switzerland (31 January 2000)
Clark, P.K.: A subordinated stochastic process model with finite variance for speculative prices. Econometrica 41(1), 135–155 (1973)
Corsi, F., Zumbach, G., Müller, U.A., Dacorogna, M.M.: Consistent high-precision volatility from high-frequency data. Econ. Notes, Rev. Bank. Finance Monet. Econ. 30(2), 183–204 (2001)
Zumbach, G.: The riskmetrics 2006 methodology. Technical report, RiskMetrics Group (2006). Available at: www.riskmetrics.com and www.ssrn.com
Zumbach, G., Müller, U.A.: Operators on inhomogeneous time series. Int. J. Theor. Appl. Finance 4(1), 147–178 (2001)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Zumbach, G. (2013). Notation, Naming, and General Definitions. In: Discrete Time Series, Processes, and Applications in Finance. Springer Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31742-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-31742-2_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31741-5
Online ISBN: 978-3-642-31742-2
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)