Abstract
For a stationary multiple time series, the one-step-ahead conditional density represents the underlying stochastic process. The conditional density incorporates all information about various characteristics of the time series, including conditional heteroskedasticity, non-normality, time irreversibility, and other forms of nonlinearity. These properties are now widely considered to be important features of many time series processes. Since the conditional density completely characterizes a stochastic process, it is thus naturally viewed as the fundamental statistical object of interest.
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(2006). Seminonparametric Conditional Density Models. In: Modeling Financial Time Series with S-PLUS®. Springer, New York, NY. https://doi.org/10.1007/978-0-387-32348-0_22
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DOI: https://doi.org/10.1007/978-0-387-32348-0_22
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