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This is a doctoral course examining advanced topics on portfolio choice and, especially, asset pricing. It is assumed that students have an MSc-level understanding of portfolio choice under uncertainly, asset pricing and portfolio theory, allowing the course to concentrate on an advanced treatment of these topics. We will not assume, however, previous exposure to doctoral courses in quantitative finance, financial economics and/or econometrics.
The unifying theme will be the role of stochastic discount factors (or pricing kernels) in linking asset prices to asset payoffs. We will learn how they can be used to represent the implications of asset pricing models in different setting (complete and incomplete markets, with or without frictions and over one or more periods). It will be fun to discover how easily (in two lines) familiar models involving risk factor loadings (e.g. the market beta) and risk premia (e.g. the market risk premium) pop out from models expressed in terms of a given stochastic discount factor.
In the process, we will have the opportunity to build intuition and shed light on key properties of important and familiar estimators. For example, we will discover that OLS, Maximum Likelihood and panel estimators are all special cases of GMM.
A core component of the course is using suitable programming languages and software to estimate and test the asset pricing models, so please bring your laptop to class. I expect most students will have access to Matlab and, therefore, the code that I will make available will be mostly for use in Matlab but I will do my best to support also other programming languages (I know R well, which is a good substitute for Matlab).
Students are encouraged to read the reference material in the textbook before the lecture.
The unifying theme will be the role of stochastic discount factors (or pricing kernels) in linking asset prices to asset payoffs. We will learn how they can be used to represent the implications of asset pricing models in different setting (complete and incomplete markets, with or without frictions and over one or more periods). It will be fun to discover how easily (in two lines) familiar models involving risk factor loadings (e.g. the market beta) and risk premia (e.g. the market risk premium) pop out from models expressed in terms of a given stochastic discount factor.
In the process, we will have the opportunity to build intuition and shed light on key properties of important and familiar estimators. For example, we will discover that OLS, Maximum Likelihood and panel estimators are all special cases of GMM.
A core component of the course is using suitable programming languages and software to estimate and test the asset pricing models, so please bring your laptop to class. I expect most students will have access to Matlab and, therefore, the code that I will make available will be mostly for use in Matlab but I will do my best to support also other programming languages (I know R well, which is a good substitute for Matlab).
Students are encouraged to read the reference material in the textbook before the lecture.
About this Module
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Requirements, Exclusions and Recommendations
Not applicable to this module.
Module Requisites and Incompatibles
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Assessment Strategy
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Carry forward of passed components
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