
on Financial Markets 
By:  CarcelesPoveda, Eva; Giannitsarou, Chryssi 
Abstract:  We study the extent to which selfreferential adaptive learning can explain stylized asset pricing facts in a general equilibrium framework. In particular, we analyze the effects of recursive least squares and constant gain algorithms in a production economy and a Lucas type endowment economy. We find that recursive least squares learning has almost no effects on asset price behaviour, since the algorithm converges relatively fast to rational expectations. On the other hand, constant gain learning may contribute towards explaining the stock price and return volatility as well as the predictability of excess returns in the endowment economy. In the production economy, however, the effects of constant gain learning are mitigated by the persistence induced by capital accumulation. We conclude that, contrary to popular belief, standard selfreferential learning cannot fully resolve the asset pricing puzzles observed in the data. 
Keywords:  Adaptive learning; Asset pricing; Excess returns; Predictability 
JEL:  D83 D84 G12 
Date:  2007–04 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:6223&r=fmk 
By:  C.N.V. Krishnan; Peter H. Ritchken; James B. Thomson 
Abstract:  Predictions of firmbyfirm term structures of credit spreads based on current spot and forward values can be improved upon by exploiting information contained in the shape of the creditspread curve. However, the current creditspread curve is not a sufficient statistic for predicting future credit spreads; the explanatory power can be increased further by exploiting information contained in the shape of the risklessyield curve. In the presence of creditspread and riskless factors, other macroeconomic, marketwide, and firmspecific risk variables do not significantly improve predictions of credit spreads. Current creditspread and risklessyield curves impound essentially all marketwide and firmspecific information necessary for predicting future credit spreads. 
Keywords:  Corporate bond ; Rate of return 
Date:  2007 
URL:  http://d.repec.org/n?u=RePEc:fip:fedcwp:0705&r=fmk 
By:  Tim Bollerslev; Hao Zhou 
Abstract:  We find that the difference between implied and realized variances, or the variance risk premium, is able to explain more than fifteen percent of the expost time series variation in quarterly excess returns on the market portfolio over the 1990 to 2005 sample period, with high (low) premia predicting high (low) future returns. The magnitude of the return predictability of the variance risk premium easily dominates that afforded by standard predictor variables like the P/E ratio, the dividend yield, the default spread, and the consumptionwealth ratio (CAY). Moreover, combining the variance risk premium with the P/E ratio results in an R^2 for the quarterly returns of more than twentyfive percent. The results depend crucially on the use of "modelfree", as opposed to standard BlackScholes, implied variances, and realized variances constructed from highfrequency intraday, as opposed to daily, data. Our findings suggest that temporal variation in risk and riskaversion both play an important role in determining stock market returns. 
Date:  2007 
URL:  http://d.repec.org/n?u=RePEc:fip:fedgfe:200711&r=fmk 