A survey on risk-return analysis

This paper provides a review of the main features of asset pricing models. The review includes single-factor and multifactor models, extended forms of the Capital Asset Pricing Model with higher order co- moments, and asset pricing models conditional on time-varying volatility.Asset pricing, CAPM

Why Are Asset Returns Predictable?

Starting from an information process governed by a geometric Brownian motion we show that asset returns are predictable if the elasticity of the pricing kernel is not constant. Declining [Increasing] elasticity of the pricing kernel leads to mean reversion and negatively autocorrelated asset returns [mean aversion and positively autocorrelated asset returns]. Under nonconstant elasticity of the pricing kernel financial ratios as the price-earnings ratio have predictive power for future asset returns. In addition, it is shown that asset prices will be governed by a time-homogeneous stochastic differential equation only under the constant elasticity pricing kernel. Hence, usually asset price processes do not satisfy the assumptions needed for empirical estimation. --Pricing kernel,Diffusion processes,Stationarity,Predictability of asset returns,Autocorrelation

Prices and Portfolio Choices in Financial Markets: Theory, Econometrics, Experiments

Many tests of asset-pricing models address only the pricing predictions, but these pricing predictions rest on portfolio choice predictions that seem obviously wrong. This paper suggests a new approach to asset pricing and portfolio choices based on unobserved heterogeneity. This approach yields the standard pricing conclusions of classical models but is consistent with very different portfolio choices. Novel econometric tests link the price and portfolio predictions and take into account the general equilibrium effects of sample-size bias. This paper works through the approach in detail for the case of the classical capital asset pricing model (CAPM), producing a model called CAPM+ε. When these econometric tests are applied to data generated by large-scale laboratory asset markets that reveal both prices and portfolio choices, CAPM+εis not rejected

Small Firm Effect, Liquidity and Security Returns: Australian Evidence

Standard asset pricing models ignore the costs of liquidity. In this study we advance the ongoing debate on empirical asset pricing and test if liquidity costs (as proxied by turnover rate, turnover ratio and bid-ask spread) affect stock returns for Australian stocks. Our tests use the factor portfolio mimicking approach of Fama and French (1993, 1996). We find small and less liquid firms generate positive risk premia after controlling for market returns and firm size. We find no evidence of any seasonal effects that can explain our multifactor asset pricing model findings. In summary, our study provides support for a broader asset-pricing model with multiple risk factors.Liquidity, Turnover, Asset Pricing, and Closing Bid-Ask Spread

Incomplete Diversification and Asset Pricing

Investors in equilibrium are modeled as facing investor specific risks across the space of assets. Personalized asset pricing models reflect these risks. Averaging across the pool of investors we obtain a market asset pricing model that reflects market risk exposures. It is observed on invoking a law of large numbers applied to an infinite population of investors, that many personally relevant risk considerations can be eliminated from the market asset pricing model. Examples illustrating the effects of undiversified labor income and taste specific price indices are provided. Suggestions for future work on asset pricing include a need to focus on identifying and explaining investor specific risk exposures.Diversification, Asset Pricing, Investor specific risks

Asset Pricing Theories, Models, and Tests

An important but still partially unanswered question in the investment field is why different assets earn substantially different returns on average. Financial economists have typically addressed this question in the context of theoretically or empirically motivated asset pricing models. Since many of the proposed “risk” theories are plausible, a common practice in the literature is to take the models to the data and perform “horse races” among competing asset pricing specifications. A “good” asset pricing model should produce small pricing (expected return) errors on a set of test assets and should deliver reasonable estimates of the underlying market and economic risk premia. This chapter provides an up-to-date review of the statistical methods that are typically used to estimate, evaluate, and compare competing asset pricing models. The analysis also highlights several pitfalls in the current econometric practice and offers suggestions for improving empirical tests

Testing the asset pricing model of exchange rates with survey data

This paper proposes a new test for the asset pricing model of the exchange rate. It examines whether the way market analysts generate their forecasts is closer to the one implied by the asset pricing model, or to any of those implied by some alternative models. The asset pricing model is supported by the test since it has significantly better out-of-sample fit on survey data than simpler models including the random walk. The traditional test based on forecasting ability is applied as well. The asset pricing model proves to have better forecast accuracy in case of some exchange rates and forecast horizons than the random walk. JEL Classification: F31, F36, G13asset pricing exchange rate model, present value model of exchange rate, survey data

Testing the q-Theory of Anomalies

The q-theory explanations of asset pricing anomalies are quantitatively important. We perform a new asset pricing test by using GMM to minimize the difference between average stock returns in the data and average investment returns constructed from observable firm characteristics. Under various specifications, the model-implied average returns display similar magnitudes of dispersion across portfolios sorted on investment-to-asset and on size and book-to-market. But the predicted dispersions in average returns among portfolios sorted on earnings surprises are somewhat smaller in magnitude than those observed in the dataq-theory, asset pricing anomalies, structural estimation