Introductory description

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Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. State Space Pricing. Arbitrage and linear factor models. Stochastic Discount Factor
2. Decision making under risk. Portfolio Theory. CAPM
3. Consumption Based Asset pricing. Multi-period market equilibrium
4. Time-Inseparable Utility
5. Production-based Asset Pricing
6. Contingent claims pricing

Learning outcomes

By the end of the module, students should be able to:

• Define and explain the key issues in Asset pricing, and formalize these in the context of classic models.
• Define the concept of Stochastic Discount Factor (SDF); derive explicit representation of an SDF in a static and dynamic settings
• Define and derive from general principles (algebraic of economic theory) classic Asset Pricing models; derive and explain their key properties.
• Understand and be familiar with classic asset pricing ``puzzles/anomalies; discuss possible solutions put forward in the literature.
• Derive empirically testable implications of various asset pricing models; formally state hypotheses and tests to evaluate the validity of such implications.
• Derive optimal portfolio given the risk preferences, efficient frontier
• Construct a SDF within a given model framework, compute state prices in a complete market
• Set up and derive the equilibrium model based on general principles (algebraic of economic theory)
• Solve basic SDE in continuous time
• Dynamic optimization; Bellman equations; link between a Bellman equation and asset pricing.
• Represent real-world counter parts in abstract and mathematical forms.
• Awareness of links to other fields of research, and a strong motivation of avoiding folklore type of arguments in their future research.
• Be able to construct an SDF within a given model framework from asset price data using mathematical software (e.g. MatLab); derive key figures (moments, factor loadings ...).
• Implement the HR representation within a given asset universe from data using mathematical software; derive key implied objects (e.g. frontier, efficient portfolios, ...)
• Explain, and formally state in the context of the models considered in 4, classic asset pricing puzzles/anomalies; discuss possible solutions put forward in the literature.
• Empirically assess and critically evaluate the evidence in support of these anomalies; test whether model variants proposed in the literature indeed provide solutions.
• Explain (and formally show) how conditioning information can be used to ... (a) improve the performance of asset pricing models (b) create dynamically efficient benchmark portfolios (c) enhance the power of asset pricing model tests
• Derive empirically testable implications of various asset pricing models; formally state hypotheses and tests to evaluate the validity of such implications.
• Implement the tests developed in 6 to estimate the coefficients of asset pricing models and empirically assess their validity by evaluating their testable implications.

Indicative reading list

Reading lists can be found in Talis

Subject specific skills

Derive optimal portfolio given the risk preferences, efficient frontier
Construct a SDF within a given model framework, compute state prices in a complete market
Set up and derive the equilibrium model based on general principles (algebraic of economic theory)
Solve basic SDE in continuous time
Dynamic optimization; Bellman equations; link between a Bellman equation and asset pricing.

Transferable skills

Be able to construct an SDF within a given model framework from asset price data using mathematical software (e.g. MatLab); derive key figures (moments, factor loadings ...).
Implement the HR representation within a given asset universe from data using mathematical software; derive key implied objects (e.g. frontier, efficient portfolios, ...)
Explain, and formally state in the context of the models considered in 4, classic asset pricing puzzles/anomalies;
discuss possible solutions put forward in the literature.
Empirically assess and critically evaluate the evidence in support of these anomalies; test whether model variants
proposed in the literature indeed provide solutions.
Explain (and formally show) how conditioning information can be used to ... (a) improve the performance of asset pricing models (b) create dynamically efficient benchmark portfolios (c) enhance the power of asset pricing model tests
Derive empirically testable implications of various asset pricing models; formally state hypotheses and tests to
evaluate the validity of such implications.
Implement the tests developed in 6 to estimate the coefficients of asset pricing models and empirically assess their validity by evaluating their testable implications.