State space modelling in finance
using S+FinMetrics
Presented: Tuesday, December 7th at 8:00AM
Pacific Time
Speaker: Eric Zivot, University of Washington
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Abstract
State space modelling in economics and finance has
become widespread over the last decade. Textbook treatments
of state space models in econometrics often lack details
on practical implementation, and until recently there
has not been much flexible software for the statistical
analysis of general models in state space form. A
modern set of state space modelling tools are available
in SsfPack which is a suite of C routines for carrying
out computations involving the statistical analysis
of univariate and multivariate models in state space
form. The routines allow for a variety of state space
forms from simple time invariant models to complicated
time varying models. Functions are available to
put standard models like ARIMA, structural time series,
and spline models in state space form. General routines
are available for Kalman filtering, smoothing, simulation
smoothing, likelihood evaluation, forecasting and
signal extraction. Full details of the statistical
analysis is provided in the book Time Series Analysis
by State Space Methods by James Durbin and Siem-Jan
Koopman. The SsfPack routines are incorporated into
S+FinMetrics. This talk will survey some common state
space models using in finance and show how to specify
and estimate these models using the SsfPack library
of functions implemented in S+FinMetrics. Examples
include recursive regression, time varying parameter
models, unobserved components models, stochastic volatility
models and term structure models.
Presenter Information
Dr. Eric Zivot is an Associate Professor and Gary
Waterman Distinguished Scholar in the Economics Department
at the University of Washington. He is co-author of
the book Modeling Financial Time Series Using S-PLUS,
and he is co-director of the nascent Professional
M.S. Program in Computational Finance at the University
of Washington.
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