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Essays on Expectations and the Econometrics of Asset Pricing

Lof, Matthijs (2013): Essays on Expectations and the Econometrics of Asset Pricing.


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The way in which market participants form expectations affects the dynamic properties of financial asset prices and therefore the appropriateness of different econometric tools used for empirical asset pricing. In addition to standard rational expectations models, this thesis studies a class of models in which boundedly rational agents may switch between various simple expectation rules. A well-known specific example features fundamentalists, who target the fundamental value of the asset, and chartists, who try to exploit recent trends in price movements. A crucial feature of these models is that not all agents have to follow the same expectation rule, but are allowed to form heterogeneous beliefs.

Chapters 2 and 3 present empirical estimations of two specific heterogeneous agent models. Since the data generating processes are assumed to be nonlinear, due to the agents' switching between expectation rules, nonlinear regression models are applied. By framing the empirical results in a heterogeneous agent framework, these chapters provide an alternative view on important topics in asset pricing, such as the prevalence of excess volatility and the relation between financial markets and the macro-economy.

The final two chapters deal with noncausal, or forward-looking, autoregressive models. Chapter 4 shows that US stock prices are better described by noncausal autoregressions than by their causal counterparts. This implies that agents' expectations are not revealed to an outside observer such as an econometrician observing only realized market data. Simulation results show that heterogeneous agent models are able to generate noncausal asset prices.

Chapter 5 considers the estimation of a class of standard rational expectations models. It is shown that noncausality of the instrumental variables does not have an impact on the consistency of the generalized method of moments (GMM) estimator, as long as agents form rational expectations.

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