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Natural Forecasting, Asset Pricing, and Macroeconomic Dynamics Andreas Fuster David Laibson Brock Mendel Harvard University May 2010.

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Presentation on theme: "Natural Forecasting, Asset Pricing, and Macroeconomic Dynamics Andreas Fuster David Laibson Brock Mendel Harvard University May 2010."— Presentation transcript:

1 Natural Forecasting, Asset Pricing, and Macroeconomic Dynamics Andreas Fuster David Laibson Brock Mendel Harvard University May 2010

2 Financial crises Key ingredients (Kindleberger 1978) Improving fundamentals Rising asset prices Rising leverage supporting consumption and investment Falling asset prices and deleveraging Banking crisis Recession/Depression

3 Financial crises Key ingredients (Kindleberger 1978) Improving fundamentals Rising asset prices (bubble) Rising leverage supporting consumption and investment Falling asset prices and deleveraging Banking crisis Recession/Depression

4 Bubbles Neo-classical economic view: – Non-rational bubbles dont exist – Non-rational bubbles only appear to exist because of hindsight bias (fundamentals sometimes unexpectedly deteriorate) – Rational bubbles may exist in special circumstances (Tirole, 1985) Today: – bubbles are (at least partially) not rational – bubbles explain macro dynamics

5

6 The Japanese Bubble

7 7 Dot com bubble Lamont and Thaler (2003) March 2000 3Com owns 95% of Palm and lots of other net assets, but... Palm has higher market capitalization than 3Com $Palm > $3Com = $Palm + $Other Net Assets

8 8 -$63 = (Share price of 3Com) - (1.5)*(Share price of Palm)

9 Four classes of explanations for the most recent crisis: Savings glut (e.g., Bernanke 2003) – But see Laibson and Mollerstrom (2010): worldwide savings did not rise Rational bubbles (e.g., Caballero et al 2006) Agency problems – But see Connor, Flavin, and OKelly 2010: Ireland did not have exotic mortgages and CMOs Non-rational bubbles

10 Housing prices and trade deficits Turkey Japan Germany Laibson and Mollerstrom, 2010

11 Four classes of explanations for the most recent crisis: Savings glut (e.g., Bernanke 2003) – But see Laibson and Mollerstrom (2010): worldwide savings did not rise Rational bubbles (e.g., Caballero et al 2006) Agency problems – But see Connor, Flavin, and OKelly 2010: Ireland did not have exotic mortgages and CMOs Non-rational bubbles

12 Lehmans forecasts in 2005 HPA = House Price Appreciation Source: Gerardi et al (BPEA, 2008)

13 Alan Greenspan While local economies may experience significant speculative price imbalances, a national severe price distortion seems most unlikely in the United States, given its size and diversity. (October, 2004) If home prices do decline, that likely would not have substantial macroeconomic implications. (June, 2005) Though housing prices are likely to be lower than the year before, I think the worst of this may well be over. (October, 2006)

14 Four classes of explanations for the most recent crisis: Savings glut (e.g., Bernanke 2003) – But see Laibson and Mollerstrom (2010): worldwide savings did not rise Rational bubbles (e.g., Caballero et al 2006) Agency problems – But see Connor, Flavin, and OKelly 2010: Ireland did not have exotic mortgages and CMOs Non-rational bubbles

15 Bubbles form: 1995-2007 Ill focus on the US Related bubbles existed in many other countries The US bubble had two main components: – Prices of publicly traded companies – Prices of residential real estate And many minor contributors: – Prices of private equity – Commodities – Hedge funds

16 Fundamental Catalysts: 1990s End of the cold war Deregulation High productivity growth Weak labor unions Low energy prices ($11 per barrel avg. in 1998) IT revolution Low nominal and real interest rates Congestion and supply restrictions in coastal cities

17 P/E ratio: Cambell and Shiller (1998a,b) Real index value divided by 10-year average of real earnings Jan 1881 to April 2010 Dec 1920 Sept 1929 July 1982 Jan 1966 Dec 1999 Average: 16.34 Source: Robert Shiller web page

18 P/E ratio: Cambell and Shiller (1998a,b) Real index value divided by 10-year average of real earnings Jan 1881 to April 2010 Dec 1920 Sept 1929 July 1982 Jan 1966 Dec 1999 Average: 16.34 Source: Robert Shiller

19 Real Estate in Phoenix and Las Vegas Jan 1987 – January 2010

20 Long-run horizontal supply curve Phoenix

21 Long-run horizontal supply curve Phoenix

22 Long-run horizontal supply curve 8 miles

23 Demand Bubble Demand Long-run horizontal supply curve LR Supply SR Supply Arbitrage: Buy your house now for $400,000 or in 3 years at $300,000 Price Quantity

24 Demand Bubble Demand Over-shooting LR Supply SR Supply Arbitrage: Buy your house now for $400,000 or in 3 years at $200,000 Price Quantity DWL

25 S&P 500 Case-Shiller Index January 1987-January 2010 226.7 April 2006 January 1987 January 2010 May 2009 January 2000

26 Housing Prices Source: Robert Shiller

27 Household net worth divided by GDP 1952 Q1 – 2008 Q4 Source: Flow of Funds, Federal Reserve Board ; GDP, BEA.

28 Today A formal model of non-rational bubbles Microfoundations Testable predictions Goal: Study non-rational expectations with a parsimonious and generalizable model.

29 Outline 1.Two building blocks – Natural forecasting – Hump-shaped impulse response 2.Tree model 3.Simulations, predictions, empirical evaluation 4.Counterfactuals 5.Extensions

30 Related Literature Barberis, Shleifer, and Vishny (1998): extrapolative dividend forecasts Barsky and De Long (1993): extrapolation and excess volatility Benartzi (2001): extrapolation and company stock Black (1986): noise traders Campbell and Shiller (1988a,b): P/E ratio and return predictability Choi (2006): extrapolation and asset pricing Choi, Laibson, and Madrian (2009): positive feedback in investment Cutler, Poterba, and Summers (1991): return autocorrelations De Long, et al (1990): noise traders and positive feedback Hong and Stein (1999): forecasting biases Keynes (1936): animal spirits LaPorta (1996): Growth expectations have insufficient mean reversion Leroy and Porter (1981): excess volatility in stock prices Lettau and Ludvigson (1991): W/C correlates negatively with future returns Lo and MacKinlay (1988): variance ratio tests Loewenstein, ODonoghue, and Rabin (2003): projection bias Piazessi and Schneider (2009): extrapolative beliefs in the housing market Previterro (2001): extrapolative beliefs and annuity investment Shiller (1981): excess volatility in stock prices Summers (1986): power problems in financial econometrics Tortorice (2010): extrapolative beliefs in unemployment forecasts

31 (a) Natural forecasting bias

32 Natural forecasting Natural forecasting requires minimal memory Natural forecasting has no free parameters Natural forecasting nests: o random walk: o frictionless momentum on a surface:

33 (b) True data generating process with hump-shaped impulse response Impulse response functions

34 Hump-shaped impulse response ARIMA(p,1,q) ARIMA(0,1,Q)

35 Ln(Real GDP) Four-year horizon (quarterly data) ARIMA(1,1,0) ARIMA(0,1,12) ARIMA(0,1,8) ARIMA(0,1,4)

36 Unemployment Four-year horizon (quarterly data) ARIMA(1,1,0) ARIMA(0,1,12) ARIMA(0,1,8) ARIMA(0,1,4)

37 Ln(Real earnings) Four-year horizon (quarterly data) ARIMA(1,1,0) ARIMA(0,1,12) ARIMA(0,1,8) ARIMA(0,1,4)

38 Ln(S&P Gross Return) Four-year horizon (monthly data) ARIMA(1,1,0) ARIMA(0,1,12) ARIMA(0,1,8) ARIMA(0,1,4)

39 Interacting Natural Forecasting and Hump-Shaped Impulse Responses Data generating process Natural forecasting model Best fit value for φ

40 Impulse response functions: 1 year θ = 1 θ = 0.75 θ = 0.5 θ = 0.25 θ = 0

41 Impulse response functions: 4 years θ = 1 θ = 0.75 θ = 0.5 θ = 0.25 θ = 0

42 2. Illustrative Model Equity tree, with dividends: Labor tree (non-stochastic): y t Quadratic preferences Study limit in which curvature 0 o but do not pass to the limit Discount factor δ

43 Model continued Elastic supply of foreign capital with gross return R. Assume that δR=1. Assume foreign agents dont hold domestic capital – Home bias – Moral hazard – Adverse selection – Expropriation risk Natural forecasting with weighting parameter θ

44 Consumption function

45 Natural forecasting asset pricing

46 Rational expectations asset pricing

47 Calibration

48 Data and Simulations (N=5000) τDataSimDataSimDataSimDataSim 1-0.030.000.090.01-0.12-0.14-0.07-0.14 20.01-0.01-0.06-0.01-0.13-0.15-0.12-0.16 3-0.08-0.04 -0.02-0.14 -0.15-0.14 4-0.21-0.07-0.03-0.06-0.14-0.13-0.18-0.13 5-0.05 -0.06-0.05-0.13-0.12-0.22-0.14 6-0.01-0.03-0.06-0.05-0.11-0.12-0.23-0.12 7-0.13-0.03-0.13-0.06-0.10-0.11-0.25-0.11 8-0.12-0.040.01-0.03-0.08-0.10-0.27-0.10 9-0.07-0.040.01-0.03-0.08 -0.24-0.11 100.070.000.04-0.03-0.07 -0.23-0.11

49 τDataSim 1-0.030.00 20.01-0.01 3-0.08-0.04 4-0.21-0.07 5-0.05 6-0.01-0.03 7-0.13-0.03 8-0.12-0.04 9-0.07-0.04 100.070.00

50 τDataSim 10.090.01 2-0.06-0.01 3-0.04-0.02 4-0.03-0.06 5 -0.05 6-0.06-0.05 7-0.13-0.06 80.01-0.03 90.01-0.03 100.04-0.03

51 τDataSim 1-0.12-0.14 2-0.13-0.15 3-0.14 4 -0.13 5 -0.12 6-0.11-0.12 7-0.10-0.11 8-0.08-0.10 9-0.08 10-0.07

52 τDataSim 1-0.07-0.14 2-0.12-0.16 3-0.15-0.14 4-0.18-0.13 5-0.22-0.14 6-0.23-0.12 7-0.25-0.11 8-0.27-0.10 9-0.24-0.11 10-0.23-0.11

53 Summary so far: Improvement in fundamentals causes overreaction in asset prices Consumption also rises too much Then asset prices and consumption tend to fall: agents are disappointed by future realizations of fundamentals Intertemporal dependencies are very weak: correlation of 0.1 implies R 2 of 0.01. With 200 quarters of data, could not reject null hypothesis of random walk in consumption and iid asset returns. Prior dominates inference (Summers 1986).

54 Counterfactuals Suppose agents had rational expectations (θ=0)? What would economy look like? Asset returns would be iid Consumption would be a random walk Standard deviation asset returns falls by 75%. Standard deviation of ΔlnC falls by 75%.

55 Extensions Add a persistent component to dividends to better match true DGP (little changes) Add other sources of stochasticity (labor income) Add quantitatively meaningful risk aversion Add a mechanism for delayed adjustment in consumption (see next slide)

56 τDataSim 1-0.030.00 20.01-0.01 3-0.08-0.04 4-0.21-0.07 5-0.05 6-0.01-0.03 7-0.13-0.03 8-0.12-0.04 9-0.07-0.04 100.070.00 -0.02 -0.04 -0.08 -0.06 -0.07 -0.06 -0.04 -0.03 Sim Habit

57 τDataSim 10.090.01 2-0.06-0.01 3-0.04-0.02 4-0.03-0.06 5 -0.05 6-0.06-0.05 7-0.13-0.06 80.01-0.03 90.01-0.03 100.04-0.03

58 τDataSim 1-0.12-0.14 2-0.13-0.15 3-0.14 4 -0.13 5 -0.12 6-0.11-0.12 7-0.10-0.11 8-0.08-0.10 9-0.08 10-0.07 -0.18 -0.16 -0.14 -0.12 -0.11 -0.09 -0.08 -0.07 Sim Habit

59 τDataSim 1-0.07-0.14 2-0.12-0.16 3-0.15-0.14 4-0.18-0.13 5-0.22-0.14 6-0.23-0.12 7-0.25-0.11 8-0.27-0.10 9-0.24-0.11 10-0.23-0.11 -0.02 -0.18 -0.23 -0.25 -0.24 -0.21 -0.20 -0.18 Sim Habit

60 Conclusion Parsimonious model of quasi-rational expectations Portable to other settings Generates testable predictions Matches key moments – Autocorrelation of asset returns – Co-movement of wealth, asset returns and consumption Much work remains to be done! Policy implications? Comments and suggestions welcome


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