A sufficient condition for market demand to satisfy the Law of Demand is that the mean of all households' income effect matrices be positive definite. We show how this mean income effect matrix can be estimated from cross section data under metonymy, an assumption about the distribution of households' characteristics. The estimation procedure uses the nonparametric method of average derivatives. Income effect matrices estimated this way from U.K. family expenditure data are in fact positive definite. This result can be explained by a special form of heteroskedasticity in the data: households' demands are more dispersed at higher income levels.

Abstract Supported by several recent investigations, the empirical pricing kernel (PK) puzzle might be considered as a stylized fact. Based on an economic model with reference-dependent preferences for the financial investors, we emphasize a microeconomic view that explains the puzzle via state-dependent aggregate preferences. We also investigate how the shape of the PK estimated from option and stock market index returns changes in relation to the volatility risk premium.

Any risk-return tradeoff analysis in aggregate equity markets relies on appropriate measures of risk, in most studies based on (co-)variance relations. Consequently, in integrated global markets, country-specific expected return is priced with a world price of covariance risk. This study relates domestic excess stock returns to the world downside risk. Evidence shows that downside tail risk (as a multiplier of volatility) has long memory cointegration properties; hence, the underlying risk aversion behavior in an integrated market is associated with the conditional quantile ratio, the correlation of stock returns, and the cointegrating coefficient of downside risk. Our empirical results based on G7 countries indicate that investors are averse to downside risk, which via Cornish–Fisher expansions is related to higher moment risk and interpretable in a utility-based decision framework.