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Asset pricing
In this section, the reports deal with articles on asset pricing: asset pricing models, stochastic discount factors, equity premium puzzle, consumption-based models, habit-persistence models, multi-factor models, pricing anomalies, asset return predictability, credit spreads, credit models etc...

Household Finance

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Household Finance

This paper, by John Y. Campbell, compares what households actually do (positive household finance) with what households should do (normative household finance). Even though households invest efficiently, a minor part of poorer and less well educated households make major investment mistakes.    

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Stock Return Predictability: Is it There?

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Stock Return Predictability: Is it There?

By Andrew Ang and Geert Bekaert

Previous research has documented the predictability of aggregate stock excess returns, cash flows and interest rates by the dividend yield (see e.g. Campbell 1991 or Cochrane 1992). High dividend yield forecast high stock excess returns.

This paper by Andrew Ang and Geert Bekaert revisits this common wisdom:

- They find that the predictability is in fact quite poor at long horizon when they correct the standard errors for heteroskedasticity.
- They find predictability only on the short run using the dividend yield in conjunction with the short-term interest rate.
- They show that the dividend yield does not robustly predict future dividend growth but only high future interest rates.

They use a new non-linear present value model for stocks consistent with the predictability evidence to explain the empirical findings. They run predictability regressions and test their model not only on US data but also on international data.

The US stock data is the S&P Composite Index and comes from the S&P’s Security Price Index Record from June 1935 to December 2001. The UK and German stock data are the Financial Times Actuaries Index and the Deutsche Borse CDAX Index from June 1953 to December 2001 and come from Global Financial Data. They also use a short sample data from MSCI and manage to cover the US, UK, Germany and France.

They first investigate the return predictability in the US and run the regression of annualised stock market excess returns at annual or quarterly horizon on a set of independent variables. To compute the standard errors they follow Hodrick (1992) that they compare to the methods of Newey-West (1987) and Hansen and Hodrick (1980) (Appendix A of the paper). Over the long US sample data, the Hodrick t-statistic of the dividend yield is significant only for 2 to 4 quarters. At one quarter, it needs the addition of the short-term interest rate. The short-term rate is a stronger predictor with a negative coefficient (higher rate means lower future returns).

 In their model, the risk free rate and the log dividend growth follow a VAR. The discount rate depends on its lagged value and the contemporaneous value of the risk free rate and the log dividend growth. The authors calculate the formula for the price-dividend ratio and show that it is a non-linear function of interest rates, excess returns and cash flows. They can estimate the model using Simulated Method of Moments on US data (Appendix E).

They consider five different specifications for the discount rate process: two nulls and three alternatives. The Third alternative is the most general specification without any restrictions on the parameters.

After estimation of the model, the authors investigate the predictive regressions that have been run in the literature. They compare the regression coefficients (expected excess return regressions, dividend growth regressions and risk-free rate regressions) implied by their model to the values in the data. The most general model (Alternative 3) is the most consistent with the data.

They revisit the earnings yield and excess return predictability but find that the predictability is not robust however they find that dividend and earning yields predict dividend growth.

The paper is available at  http://www2.gsb.columbia.edu/faculty/aang/papers/pred.pdf and http://rfs.oxfordjournals.org/cgi/content/abstract/hhl021v1


References:

Campbell, J. Y., 1991, “A Variance Decomposition for Stock Returns,” Economic Journal, 101, 157-179.

Cochrane, J. H., 2001, Asset Pricing, Princeton University Press.

Fama, E., and F. French, 1988, “Dividend Yields and Expected Stock Returns,” Journal of Financial Economics,22, 3-26.

Fama, E., and G. W. Schwert, 1977, “Asset Returns and Inflation,” Journal of Financial Economics, 5, 115-146.

Hansen, L., and R. Hodrick, 1980, “Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis,” Journal of Political Economy, 88, 829-853.

Hodrick, R. J., 1992, “Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement,” Review of Financial Studies, 5, 3, 357-386.

Lettau, M., and S. Ludvigson, 2001, “Consumption, AggregateWealth and Expected Stock Returns,” Journal of Finance, 56, 3, 815-849.

Lettau, M., and S. Ludvigson, 2005, “Expected Returns and Expected Dividend Growth,” Journal of Financial Economics, 76, 583-626.

Lewellen, J., 2004, “Predicting Returns with Financial Ratios,” Journal of Financial Economics, 74, 209-235.

Menzly, L., J. Santos, and P. Veronesi, 2004, “Understanding Predictability,” Journal of Political Economy,112, 1, 1-47.

Newey, W., and K. West, 1987, “A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica, 55, 703-708.

Polk, C., S. Thompson, and T. Vuolteenaho, 2005, “New Forecasts of the Equity Premium,” forthcoming Journal of Financial Economics.

See also {ln:How Well Do Financial and Macroeconomic Variables Predict Stock Returns}.

 

The Empirical Risk-Return Relation: A Factor Analysis Approach

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The Empirical Risk-Return Relation: A Factor Analysis Approach

This paper, by Sydney Ludvigson and Serena Ng, revisits the empirical literature on the trade off between risk and return.

Finance theory predicts that higher risk is associated with higher return. Empirically however results are mixed (it is positive in Bollerslev et al. (1988), Campbell and Hentschel (1992), Ghysels et al. (2005) but negative in Campbell (1987), Breen et al. (1989), Pagan and Hong (1991), Glosten et al. (1993), Whitelaw (1994), Lettau and Ludvigson (2003), Bradnt and Kang (2004)) . Because they depend on some sets of conditioning variables, there is always a risk of misspecification.

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Limits of Arbitrage: Theory and Evidence from the MBS...

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Limits of Arbitrage: Theory and Evidence from the Mortgage-Backed Securities Market

In this article published in the Journal of Finance, the authors Xavier Gabaix, Arvind Krishnamurthy, Olivier Vigneron, illustrate the limits of arbitrage in the Mortgage-Backed Securities Markets (MBS).


They start from the simple idea that prepayment risk should not be priced since it is a zero sum game between borrowers and investors. Using the option adjusted spreads (OAS) of CMO (Collateralized Mortgage Obligations) tranches that pass only interest payments (IO, interests only) to the investors, they show that the OAS depend on some systematic risk measure and a market price of risk proportional to prepayment risk.


The OAS is defined as the spread to be added to the risk free term structure of interest rates to recover the market price of the security.


They assume that the marginal investor is a specialized investor who is fully invested in the MBS market and therefore care about the risk in this market.
Their model is very simplified: It has two periods and uses a CAPM style-model to calculate the OAS of an IO, PO (principal only) and the collateral. One risk-averse portfolio manager is the marginal investor and has some personal investment in the fund she is managing on the behalf of investors.


The authors then test the proposition that the OAS of IOs are affected by the risk of prepayments, the risk aversion of the fund managers and a beta measure of systematic risk. In cross-section the betas explain the dispersion in OAS an in time series a measure of prepayment risk (difference in coupon rates of pool of mortgages and interest rates) explains the variation on OAS.


They use two sets of data. The first set contains daily OAS data of nine IOs and POs from August 1993 to March 1998.  The second set contains quarterly OAS data of six generic FNMA collaterals from October 1987 to July 1994. 


Their evidence is roughly consistent with their model. The risk of prepayment is priced in the market.


This should not surprise the practitioners who are making a living out of modelling prepayment risk. There is also evidence of risk premium in the equity option markets even though one could also argue that it is a zero-sum game. This probably shows how inadequate is standard asset pricing and the importance of market segmentations and limits of arbitrage.

Xavier Gabaix, Arvind Krishnamurthy, Olivier Vigneron, “Limits of Arbitrage: Theory and Evidence from the Mortgage-Backed Securities Market”, The Journal of Finance, Vol. LXII, NO. 2, April 2007

 

How Well Do Financial and Macroeconomic Variables Predict Stock Returns

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How Well Do Financial and Macroeconomic Variables Predict Stock Returns : Time –Series and Cross-Sectional Evidence   

This paper investigates a series of variables which are found to be predictors of the stock market in the financial literature.  

  • Dividend yield and earning yield: Fama and French (1988a) and Campbell and Shiller  (1988)  
  • Corporate bond spread: Fama and French (1989) 
  • Change in 3-month t-bill rate: Campbell (1991) and Hodrick (1992) 
  • Price-output ratio: Rangvid (2006) 
  • Price-consumption ratio: Menzly et al. (2004) 
  • Labor-income to consumption ratio: Santos and Veronesi (2005) 
  • Non-housing expenditure share: Piazzesi et al. (2006)  
  • Deviation of consumption from asset wealth and labor income (cay): Lettau and Ludvigson (2001a) 
  • Deviation of consumption from dividends and labor income (cdy): Lettau and Ludvigson (2005) 
  • “Cay” and future labor income: Julliard (2004) 
  • Dividend and labor income: Benzoni et al. (2006) 
  • Ratio of housing wealth to human wealth: Lustig and Nieuwerburgh (2005) 

The author first runs long-horizon time-series univariate regressions to study the predictability of the aggregate stock return by the different variables. She discusses in details the unit-root and cointegration properties of the variables (in particular the macro variables). She also implements mutivariate regressions to select the most signifcant variables.

She then performs a cross-sectional analysis the Fama and French portfolios (25 portfolios sorted by size and value):

A good forecasting variable should be able to better price the cross-section of returns within the framework on the conditional C-CAPM.

She runs Fama-McBeth regressions to estimate the price of risks of consumption and consumption time the factor and then analyzes the different pricing errors for each portfolio.

She finds that overall, price-dividend, price-earnings, price-output, price consumption ratios and the consumption-aggregate wealth ratio are the best predictors of future returns. 

The paper is available here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=941187

  References: 

Benzoni, L., P. Collin-Dufresne, and R. S. Goldstein (2006, Jan.), “Portfolio choice over the life-cycle when the stock and labor markets are cointegrated,” Working paper, University of Minnesota and UCLA.

Campbell, John Y. (1991), “A Variance Decomposition for Stock Returns,” Economic Journal, vol. 101, 157–179.

Campbell, John Y. and Robert J. Shiller (1988), “Stock Prices, Earnings, and Expected Dividends,” Journal of Finance, vol. 43, 661–676.

Fama, Eugene F. and Kenneth R. French (1988a), “Dividend Yields and Expected Stock Returns,” Journal of Financial Economics, vol. 22, 3–25.18

Fama, Eugene F. and Kenneth R. French (1988b), “Permanent and Temporary Components of Stock Prices,” Journal of Political Economy, vol. 96, 246–273.

Fama, Eugene F. and Kenneth R. French (1989), “Business Conditions and Expected Returns on Stocks and Bonds,” Journal of Financial Economics, vol. 25, 23–49.

Hodrick, Robert J. (1992), “Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement,” Review of Financial Studies, vol. 5, 357–386.

Julliard, C. (2004, Oct.). Labor income risk and asset returns. Working Paper, Princeton University.

Lettau, M. and S. Ludvigson (2001a), “Resurrecting the (C)CAPM: A cross-sectional test whenrisk premia are time-varying,” Journal of Political Economy 109 (6), 1238-1287.

Lettau, M. and S. C. Ludvigson (2005), “Expected returns and expected dividend growth,” Journalof Financial Economics 76, 583-626.

Lustig, H. and S. V. Nieuwerburgh (2005, June); “Housing collateral, consumption insurance andrisk premia: an empirical perspective,” The Journal of Finance 60 (3), 1167-1219.

Menzly, L., T. Santos, and P. Veronesi (2004), “ Understanding predictability,” Journal of Political Economy 112 (1), 1-47.

Piazzesi, M., M. Schneider, and S. Tuzel (2006), ”Housing, consumption, and asset prising,” WorkingPaper, University of Chicago, NYU and USC.

Rangvid, J. (2006), “Output and expected returns,” Journal of Financial Economics 81 (3), 595-624.

Santos, T. and P. Veronesi (2005, March). Labor income and predictable stock returns. WorkingPaper, Columbia University and University of Chicago. 

See also {ln:Stock Return Predictability: Is it There?}.

 


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