Time Series Modeling

  • Hans-Peter Deutsch
Part of the Finance and Capital Markets Series book series (FCMS)


Time series analysis goes a significant step further than merely determining statistical parameters from observed time series data (such as the variance, correlation, etc.) as described above. Indeed, it is primarily used as a tool for deriving models describing the time series concerned. Estimators such as those appearing in Equation 30.5 are examples of how parameters can be estimated which are subsequently used to model the stochastic process governing the time series (e.g., a random walk with drift μ and volatility σ). Building a model which “explains” and “describes” the time series data is the principal goal of time series analysis. The object is thus to interpret a series of observed data points {X t }, for example a historical price or volatility evolution (in this way acquiring a fundamental understanding of the process) and to model the processes underlying the observed historical evolution. In this sense, the historical sequence of data points is interpreted as just one realization of the time series process. The parameters of the process are then estimated from the available data and can subsequently be used in making forecasts, for example.


Time Series Random Walk Time Series Analysis Internal Model Conditional Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Hans-Peter Deutsch 2009

Authors and Affiliations

  • Hans-Peter Deutsch
    • 1
  1. 1.FrankfurtGermany

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