This paper develops a new method for the analysis of stochastic volatility (SV) models. Since volatility is a latent variable in SV models, it is dicult to evaluate the exact likelihood. In this paper, a non-linear ยฎlter which yields the exact likelihood of SV models is employed. Solving a series of
Minimax estimation with random coefficients: Theory and application to stock returns
โ Scribed by Bernhard Schipp; Markus Brechtmann
- Publisher
- Springer Netherlands
- Year
- 1996
- Tongue
- English
- Weight
- 606 KB
- Volume
- 43
- Category
- Article
- ISSN
- 0167-8019
No coin nor oath required. For personal study only.
โฆ Synopsis
Testing the reliability of the capital asset pricing model (CAPM) for various stock market returns is an important task in capital market research. In all previous studies, a common feature consists in the application of ordinary least squares or Bayesian methods when it comes to estimation of parameters. The Bayesian approach seems to be fairly intractable by practitioners whereas the OLS approach often yields imprecise and thus doubtful results. In this paper, the CAPM is estimated by approximate minimax techniques extended to a random coefficient regression model (RCR). The method turns out to be efficient from both the economical and computational point of view.
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