portfolio optimization overview · copenhagen business school 2016{18: junior assistant in...

Portfolio OptimizationOverview

Prof. Dr. Philipp Baumann

University of Bern

Spring 2020(as of February 11, 2020)

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Prof. Dr. Philipp Baumann, Spring 2020 Portfolio Optimization Slide O2

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Curriculum VitaeContact

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CV Philipp Baumann

Born in Bern

2009: MSc in Business Administration, University of Bern

2013: PhD in Business Administration, University of Bern

2013–14: Research Scholar at IEOR Department, UC Berkeley

2014–15: Postdoc at IEOR Department, UC Berkeley

Since 2015: Professor in Quantitative Methods/OperationsResearch, University of Bern

Research:

Mathematical programming in finance and operationsMachine learning/Data mining


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CV Tamara Bigler

Born in Vinelz

2018: MSc in Business Administration, University of Bern andCopenhagen Business School

2016–18: Junior assistant in Quantitative Methods, Universityof Bern

Since 2018: Teaching assistant and PhD student inQuantitative Methods/Operations Research, University ofBern

Research:

Data mining (feature selection)Combinatorial optimization


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Contact

Department of BusinessAdministration

Chair in Quantitative Methods

Engehaldenstr. 4,3012 Bern, office 226 (TB)Schutzenmattstr. 14,3012 Bern, office 008 (PB)

E-Mail:[email protected]@pqm.unibe.ch

Website: http://www.pqm.unibe.ch

Linie 11 Henkerbrünnli

Bahnhof-parking

Parking Schützenmatte

FusswegLänggasse

T i e f e n a u s t r a s s e

N e u b r ü c k s t r a s s e

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Betriebswirtschaftslehre

Engehaldestr. 4

Engehaldestr. 8

Informatik und angew. Mathematik IAM

Neubrückstr. 10

Schützen-mattstr. 14

Bibliothek Informatik IAM /Cafeteria

Betriebswirtschaftslehre

Wirtschaftsinformatik

Uni Engehalde

Einstellhalle

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Course in context of master studies

2015 course scheme: course can be selected as

part of the Management Science module orelective course

2007 course scheme: course is

part of the choice area for MSc BA and MSc B&Ec studentsnot part of any focus area

No registration required for participation

Required knowledge: completion of Bachelor degree inBusiness Administration or Economics

Introduction to MathematicsIntroduction to StatisticsQuantitative Methods in Business Administration I


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Lecture

Time: Tuesday, 10:15am to 12:00pm

Location: main building, room 114

Start: Feb 18, 2020

Lecture notes

Download: ILIASPassword will be provided on Feb 18 during the lecture

Lecture includes

ExplanationsExamplesDiscussion of case studies


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Exercises

Time: Wednesday, 10:15am to 12:00pm

Location: main building, room 115

Download of exercises: ILIAS

Types of exercises

Review of the lecture contentFormulation of optimization problemsManual application of discussed methodsApplication of discussed methods using Software Python


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Project

Available on ILIAS on Tuesday Mar 31

Project is conducted in groups of two students

Access to Software Python required

Project tutorial: Wednesday Apr 8, 2020 (instead of exercises)

Deadline for submission of solution: Wednesday Apr 22, 2020

Grading: 8 extra points can be achieved

Discussion of solutions on Wednesday Apr 29


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Overview exam

6 ECTS

Written exam

Exam will cover lecture and exercises

Permitted aids:

Non-programmable hand-held calculatorFormulary (will be distributed before the exam)List of symbols (will be distributed before the exam)

Grading

Maximal attainable score in exam: 90 pointsMaximal attainable score in project: 8 pointsAt most 90 points required for highest gradeProject points can be credited to exams in Jun 2020 and Sep2020


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Dates for final exam

Dates (duration of final exam is 90 minutes)

1 Tuesday Jun 2, 2020 from 10:15am to 11:45am2 Tuesday Sep 8, 2020 from 10:15am to 11:45am

Location: will be announced on ILIAS after closing date forexam deregistration

Discussion of past exam questions in lecture on Tuesday May19, 2020

Q&A in lecture on Tuesday May 26, 2020


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Final exam: additional information

Exam on Jun 2, 2020

Closing date registration May 26, 2020Closing date deregistration May 31, 2020First date to access graded exam Jun 10, 2020 (2pm–3pm)Second date to access graded exam Jun 15, 2020 (9am–10am)

Exam on Sep 8, 2020

Closing date registration Sep 1, 2020Closing date deregistration Sep 6, 2020First date to access graded exam Sep 15, 2020 (11am–12pm)Second date to access graded exam Sep 25, 2020 (4pm–5pm)

Registration and deregistration only via KSL


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Structure

1 Basics of optimization

2 Portfolio selection

Mean-variance model/Mean-absolute deviation modelMinimax modelValue-at-risk model/Conditional value-at-risk modelModels for fixed income securities

3 Portfolio management

Practical portfolio constraintsPortfolio rebalancingPortfolio evaluation

4 Index tracking

Basics of market indicesIndexation modelsExchange traded funds


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Learning outcomes I

The students are able to

compute well-known risk measures for equities and fixed-incomesecurities

implement portfolio-selection models in Python and computeoptimal portfolios for real-world data

understand and apply models and methods to optimize fixed-incomeportfolios

extend portfolio-selection models to account for real-worldconstraints such as for example transaction costs

evaluate the performance of portfolios based on quantitativemethods


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Learning outcomes II

The students are able to

implement various rebalancing strategies and discuss their pros andcons

analyze structural properties of market indices

implement index-tracking models in Python and compute optimaltracking portfolios for real-world data

explain the structure and the mechanics of exchange traded funds


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Literature (textbooks)

Zenios (2008): Practical financial optimization: decisionmaking for financial engineers, Blackwell, Cambridge

Luenberger (2013): Investment science, Oxford UniversityPress, New York

McKinney (2017): Python for Data Analysis. O’Reilly


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Literature (papers on portfolio selection)

Markowitz (1952): Portfolio selection. Journal of Finance (7),77–91

Konno Yamazaki (1991): Mean-absolute deviation portfoliooptimization model and its applications to Tokyo stockmarket. Management Science (37), 519–531

Young (1998): A minimax portfolio selection rule with linearprogramming solution. Management Science (44), 673–683

Rockafellar Uryasev (2000): Optimization of conditionalvalue-at-risk. Journal of Risk (2), 21–41

Benati Rizzi (2007): A mixed integer linear programmingformulation of the optimal mean/Value-at-Risk portfolioproblem. European Journal of Operational Research (176),423–434


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Literature (papers on index tracking)

Rudolf Wolter Zimmermann (1999): A linear model fortracking error minimization. Journal of Banking and Finance(23), 85–103

Strub Baumann (2018): Optimal construction and rebalancingof index-tracking portfolios. European Journal of OperationalResearch (264), 370–387


portfolio optimization overview · copenhagen business school 2016{18: junior assistant in...

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