Borrowing in period 1 Intertemporal Trades. Intertemporal Trades Impatient preferencesPatient preferences.

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Presentation transcript:

Borrowing in period 1 Intertemporal Trades

Intertemporal Trades Impatient preferencesPatient preferences

Optimal Holding Period for an Asset Rate of return from holding asset t* FV(t) = t + 2t 2 – 0.1t 3

Asset Markets: Debt

2007 Return:3.67% Volatility:16.02% 2008 Return: % Volatility: 41.10% 2009 Return: 23.44% Volatility: 27.27% Risky Assets: Equities S&P 500

Risky Assets: Portfolios 2007 Return: 3.67% Volatility: 16.02% Return:17.92% Volatility:18.05% 2008 Return: % Volatility: 41.10% Return: % Volatility:33.14% 2009 Return:23.44% Volatility:27.27% Return:37.35% Volatility:20.81% S&P 500 and Malkiel Portfolio

Capital Asset Pricing Model 1 Capital Market LineSecurity Market Line

Beta as a Measure of Relative Risk

Capital Asset Pricing Model 3-Year5-Year10-Year Mutual Fund NameSymbolBetaReturnsBetaReturnsBetaReturns American Century Heritage AATHAX Fidelity Advisor Equity Growth TFAEGX Fidelity MagellanFMAGX Putnam International Growth & IncomePNGAX Fidelity Diversified InternationalFDIVX Templeton Growth ATEPLX Vanguard 500 IndexVFINX Vanguard Total Stock Market IndexVTSMX Vanguard PRIMECAPVPMCX Janis Growth & IncomeJAGIX Dreyfus Premier Balanced BPRBBX Dreyfus Founders Balanced AFRIDX r i =  i

A theory that asset prices reflect all publicly available information about the value of an asset. Strong Form: Asset prices reflect all information, public and private, and no one can earn excess returns Semi-Strong Form: Asset prices adjust very rapidly to publicly available new information and in an unbiased fashion, such that no excess returns can be earned by trading on that information. Semi- strong-form efficiency implies that neither fundamental analysis nor technical analysis will be able to reliably produce excess returns. Weak Form: Future asset prices cannot be predicted by analyzing price from the past. Excess returns can not be earned in the long run by using investment strategies based on historical share prices or other historical data. Efficient Market Hypothesis

Expected Return of a Portfolio (2 investments): Expected Variance of a Portfolio (2 investments): E[r x ]= x 1 E[r 1 ] + x 2 E[r 2 ] (x 1 + x 2 = 1)  1,2 2 = x 1 2  x 2 2  x 1 x 2  1,2 = x 1 2  x 2 2  x 1 x 2  1,2  1  2 Diversification and Portfolio Theory

Portfolio Example 0.5 ABPortfolio StateProb.Return %15.00%5.00% %10.00%5.00% % %0.00%5.00% %-5.00%5.00% Expected Return:5.00% Variance:0.63% 0.00% Std. Deviation:7.91% 0.00% Covariance(A,B) Correlation(A,B) E[r x ]= x 1 E[r 1 ] + x 2 E[r 2 ]  1,2 2 = x 1 2  x 2 2  x 1 x 2  1,2 = x 1 2  x 2 2  x 1 x 2  1,2  1  2 Weights:

Was the yen a negative beta asset in 2007 – 2008? The blue line is FXY, an exchange-traded fund that tracks the yen. The red line is the S&P 500 index. Over the past year, the two time-series look like mirror images of each other. That is, holding yen seems to hedge U.S. stock-market risk.FXY Source: 29 May 2008http://gregmankiw.blogspot.com/ What does a negative beta asset look like?

Was the yen a negative beta asset in 2007 – 2008? What does a negative beta asset look like? Feb to May 2008:FXY^GSPCBLEND Average Weekly Returns0.19%0.01%0.10% Std. Dev. of Weekly Returns1.60%2.37%0.92% Annualized Returns10.51%0.33%5.29% Annualized Volatility11.56%17.06%6.62%

Was the yen a negative beta asset in 2007 – 2008? What does a negative beta asset look like?

Dealing With Risk: Diversification (Portfolio Theory) Effect of Additional Investments / Assets on Diversification

Risk and Uncertainty: “Contingent Consumption Plans” Purchase Do not purchase Lucky day Unlucky day $100 $295 $95 Case 1: A person with an endowment of $100 is considering the purchase of a lottery ticket that costs $5. The winning ticket in the lottery gets $ tickets will be sold. Case 2: A person with an endowment of $35,000 faces a 1% probability of losing $10,000. He is considering the purchase of full insurance against the loss for $100. Purchase Do not purchase Lucky day Unlucky day $35,000 $34,900 Lucky day Unlucky day $25,000 Outcome A: Outcome B: Pr(Lucky) = 0.025):

Risk and Uncertainty: “Contingent Consumption Plans” Purchase Do not purchase Lucky day Unlucky day $35,000 $34,900 Lucky day Unlucky day $25,000 K = the “expected loss” ($10,000), and  K is the insurance premium.

1. Risk aversion is defined through peoples’ choices: 2. Non-linearity in the utility of wealth. Given a choice between two options with equal expected values and different standard deviations, a risk averse person will choose the option with the lower standard deviation: Given a choice between two options with equal standard deviations and different expected values, a risk-averse person will choose the option with the higher expected value: Defining Risk Aversion

Risk Premium $99,415 B Risk Premium Risk Aversion and the Marginal Utility of Money $ Utility $100,000 $0 U1U1 A C U3U3 $50,000 U($) $99,500 U2U2 D

Modeling Different Risk Preferences $ Utility U($) Risk Aversion Risk Seeking Risk Neutral

Classification of Auctions What is the nature of the good being auctioned? What are the bidding rules?  Private values  Common value  English ascending bid  Dutch descending bid  Sealed bid  Vickrey second price

Evaluative Criteria for Auctions Pareto Efficiency Revenue or Profit Maximization Does the auction design guarantee that the item will go to the bidder with the highest value? Does the auction design guarantee the highest revenue (or profit) for the seller?

Types of Auctions and optimal bidding strategies  English (ascending bid)  Dutch (descending bid)  First-price, sealed bid  Second-price, sealed bid Independent Private Values Auctions Each bidder knows precisely how highly he/she values the item, and these values vary across all bidders.

Types of Auctions and optimal bidding strategies Common (or Correlated) Values Auctions The item being bid has an underlying objective value, but no bidder knows precisely what that value is.  Winning bids tend to come from those with the most optimistic estimates.  If estimate errors are randomly distributed around zero, then the winning bid will be greater than the true value of the item (the “winner’s curse”): True Value Winning Bid Distribution of bids: