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Transcript

Portfolio Theory An Investor’s Preference Return 4 2 3 1 Risk • 2 dominates 1 - same risk, but higher return • 2 dominates 3 - same return, but lower risk • 4 dominates 3 – same risk, but higher return Indifference Curves Indifference Curve Return - Represents individual’s willingness to trade-off return and risk - Assumptions: 1) 5 Axioms 2) Prefer more to less (Greedy) => Max E[U(W)] 3) Risk aversion Increasing Expected Utility Risk Indifference Curves Indifference Curve Expected Return E(R) - Represents individual’s willingness to trade-off return and risk - Assumptions: 1) 5 Axioms 2) Prefer more to less (Greedy) => Max E[U(W)] 3) Risk aversion 4) Assets jointly normally distributed Increasing Expected Utility Standard Deviation σR The transition Indifference Curve - - Represents individual’s willingness to trade-off return and risk Assumptions: From U(RETURN, RISK) To U(expected return, standard deviation of return) • This transition means the objects of choice for an investor is normally distributed. • Because only if a distribution is normally distributed can it be described only by its mean and standard deviation. • This poses a question: “Does that mean the risky assets investors invest in have to be always normally distributed in terms of their returns?” 1) 5 Axioms 2) Prefer more to less (Greedy) => Max E[U(W)] 3) Risk aversion 4) Assets jointly normally distributed The answer: NO! Individual asset’s return does not have normal distribution. So why should we assume normally distributed returns for the objects of choice? The transition Indifference Curve - - Represents individual’s willingness to trade-off return and risk Assumptions: From U(RETURN, RISK) To U(expected return, standard deviation of return) • This transition means the objects of choice for an investor is normally distributed. • Because only if a distribution is normally distributed can it be described only by its mean and standard deviation. • This poses a question: “Does that mean the risky assets investors invest in have to be always normally distributed in terms of their returns?” 1) 5 Axioms 2) Prefer more to less (Greedy) => Max E[U(W)] 3) Risk aversion 4) Assets jointly normally distributed The answer: NO! Individual asset’s return does not have normal distribution. So why should we assume normally distributed returns for the objects of choice? BECAUSE RATIONAL INDIVIDUAL INVESTORS CHOOSE TO INVEST IN A PORTFOLIO BUT NOT A SINGLE ASSET! Jointly normally distributed? Individual stock return may not be normally distributed, but a portfolio consists of more and more stocks would have its return increasingly close to being normally distributed. NOTE: (N = No. of stocks in the portfolio) Jointly normally distributed? 1st moment: Mean = Expected return of portfolio 2nd moment: Variance = Variance of the return of portfolio (RISKNESS) Mean and Var as sole choice variables => Distribution of return can be adequately described by mean and variance only That means, distribution has to be normally distributed. 3 reasons to support mean-variance criteria 1) As the table shows, a portfolio consisting of large number of risky assets tend have a return that is very close to normally distributed. 2) The fact that investors rebalance their own portfolios frequently will act so as to make higher moments (3rd, 4th, etc) unimportant (Samuelson 1970). 3) Cost-effective consideration: data required to compute 3rd and 4th moments are very demanding. Both computational and cost inefficient. Conclude • Thus we are convinced that expected returns and variances are good enough to describe the objects of choice. • From the investor’s point of view, it is cost-effective, and more intuitive. • From the objects of choice, if the unit of choice is an investment portfolio with large number of risky assets, it is close to normally distributed. • So we are cool with the indifference curves. • Now, we’ll work on the computations of expected return and standard deviation (i.e., the square root of variance) of an investment portfolio. • We make our lives easy by assuming individual asset’s return is normally distributed to illustrate. We don’t buy this assumption, but for the purpose of math, we’ll stick with it. Math Review I • Asset j’s return in State s: Rjs = (Ws – W0) / W0 • Expected return on asset j: E(Rj) = ∑sProb(State=s)xRjs • Asset j’s variance: σ2j = ∑sProb(State=s)x[Rjs- E(Rj)]2 • Asset j’s standard deviation: σj = √σ2j • Thus, Rj ~ N(E(Rj), σ2j) Math Review I • Covariance of asset i’s return & j’s return: Cov(Ri, Rj)= E[(Ris- E(Ri))x(Rjs- E(Rj))] =∑sProb(State=s)x[Ris- E(Ri)]x[Rjs- E(Rj)] • Correlation of asset i’s return & j’s return: ρij = Cov(Ri, Rj) / (σiσj) -1 ≤ ρij ≤ 1 When ρij = 1 => i and j are perfectly positively correlated. They move together all the time. When ρij = -1 => i and j are perfectly negatively correlated. They move opposite to each other all the time. A simple example: Asset j 60% $150,000 Good State: rgood = ($150,000 – $100,000) / $100,000 = 50% $80,000 Bad State: $100,000 40% Expected Return: rbad = ($80,000 – $100,000) / $100,000 = -20% (Notation: Let αs denote the Prob(State=s) E(rj) = ∑sαsrjs = 60%(50%) + 40%(-20%) = 22% Variance: σ2j = ∑sαs[rjs- E(rj)]2 = 60%(50%-22%)2 + 40%(-20%-22%)2 = 11.76% Standard Deviation: σj = √σ2j = √11.76% = 34.293% Math Review II • 4 properties concerning Mean and Var Let ũ be random variable, a be a constant 1) E(ũ+a) = a + E(ũ) 2) E(aũ) = aE(ũ) 3) Var(ũ+a) = Var(ũ) 4) Var(aũ) = a2Var(ũ) Portfolio Theory – a bit of history • Modern portfolio theory (MPT)—or portfolio theory—was introduced by Harry Markowitz with his paper "Portfolio Selection," which appeared in the 1952 Journal of Finance. 38 years later, he shared a Nobel Prize with Merton Miller and William Sharpe for what has become a broad theory for portfolio selection. • Prior to Markowitz's work, investors focused on assessing the risks and rewards of individual securities in constructing their portfolios. Standard investment advice was to identify those securities that offered the best opportunities for gain with the least risk and then construct a portfolio from these. Following this advice, an investor might conclude that railroad stocks all offered good risk-reward characteristics and compile a portfolio entirely from these. Intuitively, this would be foolish. Markowitz formalized this intuition. Detailing a mathematics of diversification, he proposed that investors focus on selecting portfolios based on their overall risk-reward characteristics instead of merely compiling portfolios from securities that each individually have attractive risk-reward characteristics. In a nutshell, inventors should select portfolios not individual securities. (Source: riskglossary.com) Link to his Nobel Prize lecture if you are interested: • http://nobelprize.org/economics/laureates/1990/markowitz-lecture.pdf Illustration: A case of 2 risky assets • Assume you have 2 risky assets (x & y) to invest, both are normally distributed. Rx ~ N(E(Rx), σ2x) & Ry ~ N(E(Ry), σ2y) In your investment portfolio, you put a in x, b in y. • a + b = 1 (a and b in %) Your Portfolio’s Expected Return E(Rp) is: E(Rp) = E[aRx + bRy] =aE(Rx)+ bE(Ry) Illustration: A case of 2 risky assets • Rx ~ N(E(Rx), σ2x) & Ry ~ N(E(Ry), σ2y) To calculate your Portfolio Variance: σ 2p = E[Rp - E(Rp)]2 = E[(aRx + bRy)-E[aRx + bRy]]2 = E[(aRx - aE[Rx])+(bRy - bE[bRy])]2 = E[a2(Rx - E[Rx])2 + b2(Ry - E[Ry])2 + 2ab(Rx- E[Rx])(Ry - E[Ry])] = a2 σ2x + b2 σ2y + 2abCov(Rx, Ry) = a2 σ2x + b2 σ2y + 2abCov(Rx, Ry) Var σ2p = a2 σ2x + b2 σ2y + 2abσxσyρxy s.d. σp = √(a2 σ2x + b2 σ2y + 2abσxσyρxy) • Thus, the portfolio’s return is normally distributed too Rp ~ N(E(Rp), σ2p) Illustration: A case of 2 risky assets σp = √(a2 σ2x + b2 σ2y + 2abσxσyρxy) σp increases as ρxy increase. Implication: given a (and thus b, because they add up to 1), if ρxy is smaller, the portfolio’s variance is smaller (i.e., the risk is lower) Diversification: you want to maintain the expected return at a definite level but lower the risk you expose. Ideally, you hedge by including another asset of similar expected return but highly negatively correlated with your original asset. Qs.: Can you link this concept to the reason why we focus on expected return and s.d. of return as the objects of choice? Diversification Proposition: portfolio of less than perfectly correlated assets always offer better risk-return opportunities than the individual component assets on their own. Proof: If ρxy = 1 (perfectly positively correlated) then, σp = a σx + b σy => portfolio s.d. is merely the weighted avg. s.d. If < 1 (less than perfectly correlated) then, σp < a σx + b σy => portfolio s.d. is less than the weighted avg. s.d. Varying the portfolio weight Suppose: E(Rp) Rx ~ N(13%, (20%)2) & Ry ~ N(8%, (12%)2) E(Rp) = E[aRx + bRy]=aE(Rx)+ bE(Ry) 13% %8 0% 100% a = portfolio weight on Asset x (in %), a+b=100% a Varying the portfolio weight Suppose: σp rx ~ N(13%, (20%)2) & ry ~ N(8%, (12%)2) σp = √(a2 σ2x + b2 σ2y + 2abσxσyρxy) 20% ρxy=1 12% 0% 100% a = portfolio weight on Asset x (in %), a+b=100% a Min-Variance opportunity set with the 2 risky assets E(Rp) 13% =1 %8 σp 12% 20% Varying the portfolio weight Suppose: rx ~ N(13%, (20%)2) & ry ~ N(8%, (12%)2) σp σp = √(a2 σ2x + b2 σ2y + 2abσxσyρxy) 20% ρxy=0.3 12% 0% 100% a = portfolio weight on Asset x (in %), a+b=100% a Min-Variance opportunity set with the 2 risky assets E(Rp) 13% = .3 %8 σp 12% 20% Varying the portfolio weight Suppose: σp rx ~ N(13%, (20%)2) & ry ~ N(8%, (12%)2) σp = √(a2 σ2x + b2 σ2y + 2abσxσyρxy) 20% ρxy=-1 12% 0% 100% a = portfolio weight on Asset x (in %), a+b=100% a Min-Variance opportunity set with the 2 risky assets E(Rp) 13% = -1 = -1 σp 12% 20% %8 Min-Variance opportunity set with the Many risky assets E(Rp) Efficient frontier Individual risky assets Min-variance opp. Set - Given a level of expected return, the lowest possible risky is? σp Min-Variance opportunity set E(Rp) Min-Variance Opportunity set – the locus of risk & return combinations offered by portfolios of risky assets that yields the minimum variance for a given rate of return σp Efficient set E(Rp) Efficient set – the set of mean-variance choices from the investment opportunity set where for a given variance (or standard deviation) no other investment opportunity offers a higher mean return. σp Investors’ choices with many risky assets, no risk-free asset E(Rp) U’’’ U’’ U’ Efficient set S P Q More risk-averse investor Less risk-averse investor σp Introducing risk-free assets • Assume borrowing rate = lending rate • Then the investment opp. set will involve any straight line from the point of risk-free assets to any risky portfolio on the min-variance opp. set • However, only one line will be chosen because it dominates all the other possible lines. • The dominating line = linear efficient set • Which is the line through risk-free asset point tangent to the min-variance opp. set. • The tangency point = portfolio M (the market) Capital market line = the linear efficient set E(Rp) E(RM) M 5%=Rf σM σp Investors’ choices with many risky assets, no risk-free asset CML E(Rp) B Q M A rf σp Investors’ choices with many risky assets, no risk-free asset CML E(Rp) B Q M This guy’s investment portfolio consist of around -50% in riskfree asset, 150% on the market portfolio A rf This guy’s investment portfolio consist of around 50% in riskfree asset, 50% on the market portfolio σp 2-Fund Separation • All an investor needs to know is the combination of assets that makes up portfolio M as well as risk-free asset. This is true for any investor, regardless of his degree of risk aversion. 2-Fund Separation: • “Each investor will have a utility-maximizing portfolio that is a combination of the risk-free asset and a portfolio (or fund) of risky assets that is determined by the line drawn from the riskfree rate of return tangent to the investor’s efficient set of risky assets.”