Portfolios that Contain Risky Assets Portfolio Models 9. Long Portfolios with a Safe Investment

Similar documents
Portfolios that Contain Risky Assets 10: Limited Portfolios with Risk-Free Assets

Modeling Portfolios that Contain Risky Assets Optimization II: Model-Based Portfolio Management

Portfolios that Contain Risky Assets 3: Markowitz Portfolios

Portfolios that Contain Risky Assets Portfolio Models 3. Markowitz Portfolios

Modeling Portfolios that Contain Risky Assets Risk and Reward II: Markowitz Portfolios

Modeling Portfolios that Contain Risky Assets Risk and Reward III: Basic Markowitz Portfolio Theory

Modeling Portfolios that Contain Risky Assets Risk and Reward III: Basic Markowitz Portfolio Theory

Portfolios that Contain Risky Assets 1: Risk and Reward

Portfolios that Contain Risky Assets Portfolio Models 1. Risk and Reward

Mean-Variance Portfolio Choice in Excel

Modeling Portfolios that Contain Risky Assets Risk and Return I: Introduction

Modeling Portfolios that Contain Risky Assets

Lecture 2: Fundamentals of meanvariance

Portfolios that Contain Risky Assets 12 Growth Rate Mean and Variance Estimators

Techniques for Calculating the Efficient Frontier

Chapter 7: Portfolio Theory

The mean-variance portfolio choice framework and its generalizations

Financial Economics: Risk Aversion and Investment Decisions, Modern Portfolio Theory

u (x) < 0. and if you believe in diminishing return of the wealth, then you would require

Quantitative Risk Management

Lecture Notes 9. Jussi Klemelä. December 2, 2014

Modeling Portfolios that Contain Risky Assets Stochastic Models I: One Risky Asset

CSCI 1951-G Optimization Methods in Finance Part 07: Portfolio Optimization

Efficient Portfolio and Introduction to Capital Market Line Benninga Chapter 9

Chapter 8. Markowitz Portfolio Theory. 8.1 Expected Returns and Covariance

Mean-Variance Analysis

Lecture IV Portfolio management: Efficient portfolios. Introduction to Finance Mathematics Fall Financial mathematics

Modeling Portfolios that Contain Risky Assets Risk and Reward I: Introduction

The Markowitz framework

Portfolios that Contain Risky Assets Portfolio Models 1. Risk and Reward

In terms of covariance the Markowitz portfolio optimisation problem is:

Mathematics in Finance

Solutions to Problem Set 1

PORTFOLIO THEORY. Master in Finance INVESTMENTS. Szabolcs Sebestyén

Economics 424/Applied Mathematics 540. Final Exam Solutions

Hedging and Pricing in the Binomial Model

This appendix discusses two extensions of the cost concepts developed in Chapter 10.

Mean-Variance Analysis

Understand general-equilibrium relationships, such as the relationship between barriers to trade, and the domestic distribution of income.

MARKOWITS EFFICIENT PORTFOLIO (HUANG LITZENBERGER APPROACH)

Lecture 10: Performance measures

Modeling Portfolios Containing Risky Assets

ECO 317 Economics of Uncertainty Fall Term 2009 Tuesday October 6 Portfolio Allocation Mean-Variance Approach

Section Linear Functions and Math Models

Final Exam Suggested Solutions

Optimizing Portfolios

You are responsible for upholding the University of Maryland Honor Code while taking this exam.

Elements of Economic Analysis II Lecture II: Production Function and Profit Maximization

Math 103: The Mean Value Theorem and How Derivatives Shape a Graph

Topic 3: The Standard Theory of Trade. Increasing opportunity costs. Community indifference curves.

Chapter 2 Portfolio Management and the Capital Asset Pricing Model

Financial Market Analysis (FMAx) Module 6

PRODUCTION COSTS. Econ 311 Microeconomics 1 Lecture Material Prepared by Dr. Emmanuel Codjoe

Some useful optimization problems in portfolio theory

Portfolio theory and risk management Homework set 2

Mean Variance Analysis and CAPM

Extend the ideas of Kan and Zhou paper on Optimal Portfolio Construction under parameter uncertainty

Eliminating Substitution Bias. One eliminate substitution bias by continuously updating the market basket of goods purchased.

CHAPTER 6: PORTFOLIO SELECTION

King s College London

Financial Giffen Goods: Examples and Counterexamples

Chapter 8: CAPM. 1. Single Index Model. 2. Adding a Riskless Asset. 3. The Capital Market Line 4. CAPM. 5. The One-Fund Theorem

Statistical Tables Compiled by Alan J. Terry

Mathematical Analysis II- Group Project

Firm s demand for the input. Supply of the input = price of the input.

Consumer Choice and Demand

Problem 1: Markowitz Portfolio (Risky Assets) cov([r 1, r 2, r 3 ] T ) = V =

Estimation. Focus Points 10/11/2011. Estimating p in the Binomial Distribution. Section 7.3

Game Theory and Economics Prof. Dr. Debarshi Das Department of Humanities and Social Sciences Indian Institute of Technology, Guwahati

Two Hours. Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER. 22 January :00 16:00

Consumer Budgets, Indifference Curves, and Utility Maximization 1 Instructional Primer 2

ECON Micro Foundations

The Baumol-Tobin and the Tobin Mean-Variance Models of the Demand

Portfolio Risk Management and Linear Factor Models

1 Consumer Choice. 2 Consumer Preferences. 2.1 Properties of Consumer Preferences. These notes essentially correspond to chapter 4 of the text.

Fundamental Theorems of Welfare Economics

GMM Estimation. 1 Introduction. 2 Consumption-CAPM

Mean Variance Portfolio Theory

The University of Sydney School of Mathematics and Statistics. Computer Project

LECTURE NOTES 3 ARIEL M. VIALE

MATH362 Fundamentals of Mathematical Finance. Topic 1 Mean variance portfolio theory. 1.1 Mean and variance of portfolio return

ECON FINANCIAL ECONOMICS

Portfolio Management

Markowitz portfolio theory

Section 7C Finding the Equation of a Line

Math1090 Midterm 2 Review Sections , Solve the system of linear equations using Gauss-Jordan elimination.

LYXOR Research. Managing risk exposure using the risk parity approach

INTRODUCTION TO MODERN PORTFOLIO OPTIMIZATION

SDMR Finance (2) Olivier Brandouy. University of Paris 1, Panthéon-Sorbonne, IAE (Sorbonne Graduate Business School)

Markowitz portfolio theory. May 4, 2017

FIN FINANCIAL INSTRUMENTS SPRING 2008

Aversion to Risk and Optimal Portfolio Selection in the Mean- Variance Framework

FIN 6160 Investment Theory. Lecture 7-10

Stochastic Programming and Financial Analysis IE447. Midterm Review. Dr. Ted Ralphs

E&G, Ch. 1: Theory of Choice; Utility Analysis - Certainty

Freeman School of Business Fall 2003

Using derivatives to find the shape of a graph

ECO101 PRINCIPLES OF MICROECONOMICS Notes. Consumer Behaviour. U tility fro m c o n s u m in g B ig M a c s

Smart Beta: Managing Diversification of Minimum Variance Portfolios

Professor Christina Romer SUGGESTED ANSWERS TO PROBLEM SET 5

Transcription:

Portfolios that Contain Risky Assets Portfolio Models 9. Long Portfolios with a Safe Investment C. David Levermore University of Maryland, College Park Math 420: Mathematical Modeling March 21, 2016 version c 2016 Charles David Levermore

Portfolios that Contain Risky Assets Part I: Portfolio Models 1. Risk and Reward 2. Covariance Matrices 3. Markowitz Portfolios 4. Solvent Portfolios 5. Leveraged Portfolios 6. Basic Markowitz Portfolio Theory 7. Unlimited Portfolios with Risk-Free Assets 8. Long Portfolios without Risk-Free Assets 9. Long Portfolios with a Safe Investment 10. Limited Leverage Portfolios

Portfolio Models 9. Long Portfolios with a Safe Investment 1. Efficient Long Frontier 2. Efficient Long Frontier Portfolios 3. General Portfolio with Two Risky Assets. 4. Simple Portfolio with Three Risky Assets.

Portfolio Models 9. Long Portfolios with a Safe Investment We now consider investors who will not hold a short position in any asset. Such an investor will not borrow to invest in a risky asset, so the safe investment is the only risky-free asset that we need to consider. We will use the capital allocation line construction to obtain the efficient long frontier for long portfolios that might include the safe investment. Efficient Long Frontier. We assume that the long frontier has already been constructed, and is given by σ = σ lf (µ) for µ [µ mn, µ mx ], where µ mn = min{m i : i = 1,, N}, µ mx = max{m i : i = 1,, N}. We will assume that µ si < µ mx, because otherwise the safe investment is more efficient than any portfolio of risky assets.

The capital allocation line between the safe investment and the portfolio on the long frontier with return µ is the line segment in the σµ-plane between the points (0, µ si ) and (σ lf (µ), µ). The slope of this line segment is ν ca (µ) = µ µ si σ lf (µ). The efficient long frontier is obtained by finding the capital allocation line with the greatest slope. In other words, we want to solve µ st = arg max { ν ca (µ) : µ [µ mn, µ mx ] }. (1) We set ν st = ν ca (µ st ) and σ st = σ lf (µ st ). The efficient long frontier is then given by µ = µ ef (σ) where µ ef (σ) = µ si + ν st σ for σ [0, σ st ], σ 1 lf (σ) for σ [σ st, σ mx ], where σ σ 1 lf (σ) is the inverse function of µ σ lf (µ). (2)

Let us consider the maximization problem given in (1). Recall that the function µ σ lf (µ) is positive and continuous over [µ mn, µ mx ]. This implies that the function µ ν ca (µ) is continuous over [µ mn, µ mx ], which implies that it has a maximum over [µ mn, µ mx ]. Because µ si < µ mx we see that ν ca (µ mx ) = µ mx µ si σ lf (µ mx ) = µ mx µ si σ mx > 0, which implies that the maximum must be positive. Because the function µ σ lf (µ) is strictly convex over [µ mn, µ mx ], the maximizer µ st must be unique. We will suppose that σ lf (µ mx ) > 0 and that σ lf (µ mn ) 0, which is a common situation.

Efficient Long Frontier. The tangent line to the curve σ = σ lf (µ) at the point (σ mx, µ mx ) will intersect the µ-axis at µ = η mx where η mx = µ mx σ lf (µ mx) σ lf (µ mx). We will consider the cases µ si η mx and µ si < η mx separately.

For the case when µ si η mx we will make the additional assumption that µ si < µ mx. Then the efficient long frontier is simply given by µ ef (σ) = µ si + µ mx µ si σ mx σ for σ [0, σ mx ]. Our additional assumption states that there is at least one risky asset that has a return mean greater than the return for the safe investment. This is usually the case. If it is not, the formula for µ ef (σ) can be modified by appealing to the capital allocation line construction. Remark. Notice that µ ef (σ) given above is increasing over σ [0, σ mx ]. When µ si = µ mx the capital allocation line construction would produce an expression for µ ef (σ) that is constant, but might be defined over an interval larger than [0, σ mx ]. When µ si > µ mx the capital allocation line construction would produce an expression for µ ef (σ) that is decreasing over an interval larger than [0, σ mx ].

For the case when µ si < η mx there is a frontier portfolio (σ st, µ st ) such that the capital allocation between it and (0, µ si ) lies above the efficient long frontier. This means that µ st > µ si and µ µ si µ st µ si σ st σ lf (µ) for every µ [µ mn, µ mx ]. Because σ lf (µ) is an increasing, continuous function over [µ st, µ mx ] with image [σ st, σ mx ], it has an increasing, continuous inverse function σlf 1 (σ) over [σ st, σ mx ] with image [µ st, µ mx ]. The efficient long frontier is then given by µ ef (σ) = µ si + µ st µ si σ for σ [0, σ σ st ], st σ 1 lf (σ) for σ [σ st, σ mx ]. Because σ lf (µ) can be expressed as a list function, we can also express σ 1 lf (σ) as a list function. We illustrate this below for the case f mv 0.

Suppose that f mv 0 and set µ 0 = µ mv. Then σ lf (µ) has the form σ lf (µ) = σ fk (µ) ( ) σ 2 µ µmvk 2 mvk + for µ [µ k, µ k+1 ], ν ask where σ mvk, µ mvk, and ν ask are the frontier parameters associated with the vector m k and matrix V k that determined σ fk (µ) in the k th step of our iterative construction of σ lf (µ). In particular, σ mv0 = σ mv, µ mv0 = µ mv, and ν as0 = ν as because m 0 = m and V 0 = V. Then σlf 1 (σ) has the form σ 1 lf (σ) = µ mvk + ν ask σ 2 σ 2 mv k for σ [σ k, σ k+1 ], where σ k = σ lf (µ k ) and σ k+1 = σ lf (µ k+1 ).

Finally, we must find the tangency portfolio (σ st, µ st ). The tangent line to the long frontier at the point (σ k, µ k ) intercepts the µ-axis at µ = η k where η k = µ k σ lf (µ k ) σ lf (µ k ) = µ mv k ν 2 as k σ 2 mv k µ k µ mvk. These intercepts satisfy η k < η k+1 η mx when µ k < µ k+1 µ mx. If we set η 0 = then for every µ si < η mx there is a unique j such that η j µ si < η j+1. For this value of j we have the tangency parameters ν st = ν asj 1 + ( ) µmvj µ 2 si, σ ν asj σ st = σ mvj 1 + mvj ( νasj σ mvj µ mvj µ si ) 2.

Therefore when µ si < η mx the efficient long frontier is given by µ ef (σ) = µ si + ν st σ for σ [0, σ st ], µ mvj + ν asj σ 2 σ 2 mv j for σ [σ st, σ j+1 ], µ mvk + ν ask σ 2 σ 2 mv k for σ [σ k, σ k+1 ] and k > j. Efficient Long Frontier Portfolios. Recall that the allocations associated with the efficient long frontier portfolios without the safe investment are given over [µ mv, µ mx ] by f lf (µ) = µ k+1 µ µ k+1 µ k f k + µ µ k µ k+1 µ k f k+1 for µ [µ k, µ k+1 ], where µ 0 = µ mv, f 0 = f mv, while f k is the nodal portfolio allocation associated with µ k for any k 1.

The return mean and allocation for the safe tangency portolio are µ st = µ mvj + ν 2 as j σ 2 mv j µ mvj µ si, f st = f lf (µ st ) = µ j+1 µ st µ j+1 µ j f j + µ st µ j µ j+1 µ j f j+1. The allocation of risky assets for any efficient long frontier portfolio is f ef (σ) = σ f σ st for σ [0, σ st ], st µ j+1 µ ef (σ) µ j+1 µ st f st + µ ef (σ) µ st µ j+1 µ st f j+1 for σ [σ st, σ j+1 ], µ k+1 µ ef (σ) µ k+1 µ k f k + µ ef (σ) µ k µ k+1 µ k f k+1 for σ [σ k, σ k+1 ], where k > j in the last case.

Remark. If we had also added a credit line to the portolio then we would have had to find the credit tangency portfolio and added the appropriate capital allocation line to the efficient long frontier. Typically there are two kinds of credit lines an investor might consider. One available from your broker usually requires that some of your risky assets be held as collateral. A downside of using this kind of credit line is that when the market goes down then your broker can force you either to add assets to your collateral or to sell assets in a low market to pay off your loan. Another kind of credit line might use real estate as collateral. Of course, if the price of real estate falls then you again might be forced to sell assets in a low market to pay off your loan. For investors who hold short positions in risky assets, these risks are hedged because they also make money when markets go down. Investors who hold only long positions in risky assets and use a credit line can find themselves highly exposed to large losses in a market downturn. It is not a wise position to take yet many do in a bubble.

General Portfolio with Two Risky Assets. Recall the portfolio of two risky assets with mean vector m and covarience matrix V given by m = ( m1 m 2 ), V = ( v11 v 12 v 12 v 22 Here we will assume that m 1 < m 2, so that µ mn = m 1 and µ mx = m 2. The long frontier associated with just these two risky assets is given by σ lf (µ) = σ 2 mv + ( µ µmv ν as where the frontier parameters are σ mv = v 11v 22 v 2 12 v 11 + v 22 2v 12, ν as = ) ) 2 for µ [m 1, m 2 ],. (m 2 m 1 ) 2, v 11 + v 22 2v 12 µ mv = (v 22 v 12 )m 1 + (v 11 v 12 )m 2 v 11 + v 22 2v 12.

The minimum volatility portfolio is ( ) 1 v22 v f mv = 12. v 11 + v 22 2v 12 v 11 v 12 We will assume that v 12 v 11 and v 12 v 22, so that f mv 0 and µ mv = f T mvm [m 1, m 2 ]. The efficient long frontier associated with just these two risky assets is then given by (σ lf (µ), µ) where µ [µ mv, m 2 ]. We now show how this is modified by the inclusion of a safe investment. The parameters associated with the construction of σ lf (µ) are µ 0 = µ mv, µ 1 = µ mx = m 2, σ 0 = σ mv, σ 1 = σ mx = σ 2 = v 22. The µ-intercept of the tangent line through (σ mx, µ mx ) = (σ 2, m 2 ) is η mx = µ mx σ lf (µ mx) σ lf (µ mx) = m 2 ν 2 as σ 2 2 m 2 µ mv = v 22 m 1 v 12 m 2 v 22 v 12.

We will present the two cases that arise in order of increasing complexity: η mx µ si and µ si < η mx. When η mx µ si the efficient long frontier is determined by µ ef (σ) = µ si + m 2 µ si σ 2 σ for σ [0, σ 2 ]. When µ si < η mx the tangency portfolio parameters are given by ν st = ν mv 1 + ( µmv µ si ν as σ mv ) 2, σ st = σ mv and the efficient long frontier is determined by µ ef (σ) = 1 + ( νas σ mv µ mv µ si µ si + ν st σ for σ [0, σ st ], µ mv + ν as σ 2 σ 2 mv for σ [σ st, σ 2 ]. ) 2,

Simple Portfolio with Three Risky Assets. Recall the portfolio of three risky assets with mean vector m and covarience matrix V given by m = m 1 m 2 = m 3 m d m m + d, V = s 2 1 r r r 1 r r r 1 The efficient long frontier associated with just these three risky assets is given by (σ lf (µ), µ) where µ [m, m + d] and σ lf (µ) =. 1 + 2r s + 1 r ( ) µ m 2 for µ [m, m + 2 3 2 d 3 d], s 1 + r + 1 r ( µ m 1 ) 2 d 2 for µ [m + 2 2 1 3 2 d, m + d]. We now show how this is modified by including a safe investment. 2 d

In the construction of σ lf (µ) we found that µ 0 = m, µ 1 = m + 2 3 d, µ 2 = µ mx = m + d, 1 + 2r 5 + 4r σ 0 = s, σ 3 1 = s 9 The frontier parameters for σ f0 (µ) were, σ 2 = σ mx = s. 1 + 2r σ mv0 = s, 3 µ mv0 = m, ν as0 = d s while those for σ f1 (µ) were 2 1 r, σ mv1 = s 1 + r 2, µ mv 1 = m + 1 2 d, ν as 1 = d 2s 2 1 r.

Because σ lf (µ)σ lf r (µ) = s21 2 µ m d 2, for µ [m, m + 2 3 d], µ m 1 2 d, for µ [m + 1 2 4 d2 3d, m + d], we can see that the µ-intercepts of the tangent lines through the points (σ 1, µ 1 ) and (σ 2, µ 2 ) = (σ mx, µ mx ) are respectively η 1 = µ 1 σ lf (µ 1 ) σ lf (µ 1 ) = m + 2 3 d 5 + 4r 3 3r d = m 1 + 2r 1 r d, η mx = µ mx σ lf (µ mx) σ lf (µ mx) = m + d 1 1 r d = m r 1 r d. We will present the three cases that arise in order of increasing complexity: η mx µ si, η 1 µ si < η mx, and µ si < η 1.

When η mx µ si the efficient long frontier is determined by µ ef (σ) = µ si + µ mx µ si σ mx σ for σ [0, σ mx ]. When η 1 µ si < η mx the tangency portfolio parameters are given by ν st = ν as1 1 + ( µmv1 µ si ν as1 σ mv1 ) 2, σ st = σ mv1 and the efficient long frontier is determined by µ ef (σ) = 1 + ( νas1 σ mv1 µ mv1 µ si µ si + ν st σ for σ [0, σ st ], µ mv1 + ν as1 σ 2 σ 2 mv 1 for σ [σ st, σ mx ]. ) 2,

When µ si < η 1 the tangency portfolio parameters are given by ν st = ν as0 1 + ( µmv0 µ si ν as0 σ mv0 ) 2, σ st = σ mv0 and the efficient long frontier is determined by µ ef (σ) = 1 + ( νas0 σ mv0 µ mv0 µ si µ si + ν st σ for σ [0, σ st ], µ mv0 + ν as0 σ 2 σ 2 mv 0 for σ [σ st, σ 1 ], µ mv1 + ν as1 σ 2 σ 2 mv 1 for σ [σ 1, σ mx ]. ) 2, Remark. The above formulas for µ ef (σ) can be made more explicit by replacing σ mv0, µ mv0, ν as0, σ mv1, µ mv1, ν as1, σ mx, µ mx, and σ 1 with their explicit expressions in terms of m, d, s and r.