ECO421 Asset Pricing
Autumn 2024

Topics
The course covers key economic principles for sound valuation and investment choice, to better understand strengths and weaknesses of popular investments models. An important aim is to increase student sophistication to help avoid naive or inappropriate use of finance models.
The course sets out by illustrating how difficulties in estimating expected returns cause a need for models for expected returns (like the CAPM), and difficulties in applying portfolio choice models (like that of Markowitz). The course covers the basic principles of rational choice under uncertainty, which provides the foundation for the meanvariance portfolio choice model of Markowitz. We implement the portfolio choice model in R, and use it to develop the Capital Asset Pricing Model (CAPM). The combination of Markowitz and CAPM enables us to derive and implement the more sophisticated and robust BlackLitterman portfolio choice model, which allows us also to incorporate private views on expected returns.
The consumptionbased asset pricing model provides a link between financial markets and macroeconomic quantities, which is useful to build economic intuition. The consumptionbased framework allows us to study the determination of the riskless rate (which the CAPM takes as exogenous), and to derive and better understand multifactor models (like that of Fama and French). The consumptionbased framework moreover allows us to develop better economic intuition for the forces that shape asset risk premia.
A final module covers the relationship between equilibrium and absence of arbitrage opportunities. We study the absence or presence of arbitrage through properties of "pure securities." All assets are shown to be portfolios of pure securities: stocks, bonds, derivatives etc. Popular models like the CAPM and the BlackScholesMerton option pricing formula are special instances of the same basic economic framework.
To keep the mathematics at a minimal level, results are generally formulated in a discretetime setting, with an emphasis on oneperiod models. Topics include, but are not limited to
1. Introduction and models of choice under uncertainty
 Estimating expected returns: Why we need asset pricing models
 The link between finance and microeconomics: Investor preferences
 Measuring risk aversion: absolute and relative risk aversion
 Basic results of optimal investment choice
2. Meanvariance portfolio choice/asset allocation and pricing
 Elementary linear algebra (with illustrations and exercises in R)
 Meanvariance portfolio choice: the Markowitz model
 Constrained portfolio choice: Implementation of short sales constraints, and ESG/sustainability constraints at the individual asset level or portfolio level
 The CAPM
 Meanvariance portfolio choice with private beliefs: the BlackLitterman model
3. Consumptionbased asset pricing and multifactor asset pricing models
 The link between finance and macroeconomics: Asset prices in a pure exchange economy
 The riskless rate, risk premia, and aggregate consumption/savings
 Ross' multifactor asset pricing model (the APT)
 Understanding linear multifactor models, like the FamaFrench three factor model
4. Key properties of securities markets
 ArrowDebreu securities markets and pure securities
 Replicating portfolios and the relationship between pure and complex (real world) securities
 Spanning: Complete versus incomplete markets
 Noarbitrage conditions and pricing of securities
 Optimal consumption/investment choice
 General equilibrium
5. The "theory of everything"
 The Fundamental Theorem of Asset Pricing
 Stochastic discount factors (SDFs)
 The relationship between pure security prices, SDFs, and option pricing theory
 The binomial option pricing model
There may be changes to the above topics, for instance to accommodate student interests, new developments, or current events.

Learning outcome
After completing the course, the student:
Knowledge
 knows the fundamental theory of portfolio choice and asset pricing
 understands key simplifying assumptions behind investment models and their ideal use cases
 understands the roles of the concepts of "equilibrium" and "no arbitrage" in investments
 understands the roles of spanning and complete markets assumptions (which all models make)
 understands the relationship between portfolio choice, partial/general equilibrium, and asset prices
Skills
 can implement sophisticated portfolio choice models with and without constraints (e.g. short sales or ESG and sustainability requirements)
 can interpret and correctly apply asset pricing models across various applications (e.g. asset allocation versus capital budgeting/project analysis)
 is able to discern the important assumptions, features and empirical predictions of investments models
 can formulate and conduct quantitative analyses
General competences
 can implement finance models in R
 is able to communicate key insights from asset pricing
 can extract key insights from asset pricing research
 is able to discuss recent empirical research and current events in financial markets from an asset pricing viewpoint

Teaching
Regular lectures, class room discussions, problem solving exercises, and student presentations.

Recommended prerequisites
Students without prior knowledge of R should work through the R tutorial posted on the course home page, before the first lecture.

Required prerequisites
To take this course you should already have basic insights corresponding to
 optimization, similar to those obtained in a basic course in mathematics or ECO401,
 a general understanding of financial markets, similar to those obtained in FIE400.

Compulsory Activity
Students must finish between four and six assignments, in groups of up to three students. The number of assignments depends on class interests, needs, and available time. Students must submit an individual selfassessment report for each assignment.

Assessment
Individual four hour home exam.

Grading Scale
AF

Computer tools
The course uses R to illustrate key concepts and topics. Some of the assignments are designed to be solved with R, but can be solved in other types of software like Excel, Python, or Matlab. R is however the only formally supported software in the course. Students do not need to know R before attending the course, but should work through a short R tutorial—available on the course home page—before the course commences.

Literature
Lecture notes.
Additional readings may be assigned.
Overview
 ECTS Credits
 7.5
 Teaching language
 All course activities are conducted in English
 Semester

Autumn. Offered autumn 2024
Course responsible
Associate Professor Jørgen Haug, Department of Finance, NHH.