Abstract
This chapter introduces the estimation principles underlying our canonical asset pricing framework. For a general financial pricing model we elaborate on the technique of state space modeling, the relevant filtering algorithms, and finally the estimation of model parameters. These building blocks are empirically employed on capital market data in parts II, III, and IV, where we concentrate on specific adaptions of the presented estimation framework.
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References
See, for example, Harvey (1989), Aoki (1990), and Hamilton (1994a). For a comprehensive treatment of the state space approach to time series see Durbin and Koopman (2001).
I.e. we treat both the original parameters and the variances of the measurement errors as part of the vector zP, as we will see further down in section 2.3 on parameter estimation.
See, for example, Hamilton (1994b, ch. 10); in the Gaussian state-space model we have strict stationarity (see, for example, Hamilton (1994b, p. 45 f.)), since the normal density is completely specified by its first two moments.
See, for example, Jazwinski (1970, ch. 5).
See, for example, Jazwinski (1970), Tanizaki (1996) and Gourieroux and Monfort (1997).
See, for example, Jazwinski (1970, p. 146 f.). s See, for example, Hamilton (1994b, p. 72 f.).
Note that these are then formally indexed with t — 1, since we denote the actual information we are working with by the time index t.
See, for example, Anderson and Moore (1979) and Tanizaki (1996).
For a detailed comparison of different non-linear filtering algorithms see Tanizaki (1996).
See Tanizaki (1996, ch. 3)
A brad treatment of maximum likelihood estimation with time-series applications can be found, for example, in Stuart and Ord (1991), Hamilton (1994b), and Gourieroux and Monfort (1995b).
As given, for example, in Harvey (1989, ch. 3.4).
See Dennis and Schnabel (1996, ch. 7.2).
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© 2004 Springer-Verlag Berlin Heidelberg
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Kellerhals, B.P. (2004). Estimation Principles. In: Asset Pricing. Springer Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24697-8_2
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DOI: https://doi.org/10.1007/978-3-540-24697-8_2
Publisher Name: Springer, Berlin, Heidelberg
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