Titlebook: Asset Pricing; Modeling and Estimat B. Philipp Kellerhals Book 2004Latest edition Springer-Verlag Berlin Heidelberg 2004 Asset Pricing.Clos

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Lecture Notes in Computer Sciencetricity prices. In chapter 16 we present an empirical adaptation of the theoretical pricing model in state space form. Using maximum likelihood estimation based on extended Kalman filtering we report empirical results on electricity data from the largely deregulated Californian electricity market. I

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Financial Modeling A common and convenient method to describe the evolution of the modeled state variables is to specify their probabilistic behavior via Itô processes. Here, the basic building block is the Brownian motion which we examine in this chapter accompanied by the necessary stochastic calculus to set up app

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Estimation Principles 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 framewo

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First Empirical Resultsned in this study exists of closed-end equity funds for a complete five year period from January 1993 to December 1997. All NYSE traded closed-end funds are included, if their date of issue lies before January 1993 in order to avoid post-offering pricing effects.. The data is collected on a weekly b

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Implications for Investment Strategies the previous chapter we infer insights into two suggested applications relevant for closed-end fund investors. .,we address the investors’ fundamental question of the forecasting power of the pricing model, i.e. we investigate the ability of the pricing model to predict the market prices of the clo