Smallholders download and share videos from the Internet to learn about sustainable agriculture

Of 1,211 farmers and their representatives registered on www.accessagriculture.org, 142 participated in an on-line survey in November 2017, designed to learn farmer’s opinions of Access Agriculture, an NGO which hosts a digital platform where anyone can watch or download videos and other information for free. These farmer learning videos all convey practical information on sustainable agricultural innovations, to encourage farmer experiments. Previous experience showed that smallholders liked having their own copy of videos (e.g. on DVD), but this study showed that farmers are now starting to find their own way to the internet to pro-actively search for information. Although some farmers learn about on-line videos by social contacts, most of the farmers found the videos on www.accessagriculture.org by surfing the web. This suggests that limitations of reaching farmers with traditional forms of video distribution (e.g. DVDs and village screenings) will be partly overcome by the Internet. Youth have become the new information brokers for communities, as elders may lack the digital technology skills needed to use the Internet to get agricultural information. To share videos with other community members, youth will benefit from additional tools, such as an app, to allow easy download and sharing with limited data consumption.     

Variance bounds on the permanent and transitory components of stochastic discount factors

In this paper, we develop lower bounds on the variance of the permanent component and the transitory component, and on the variance of the ratio of the permanent to the transitory components of SDFs. Exactly solved eigenfunction problems are then used to study the empirical attributes of asset pricing models that incorporate long-run risk, external habit persistence, and rare disasters. Specific quantitative implications are developed for the variance of the permanent and the transitory components, the return behavior of the long-term bond, and the comovement between the transitory and the permanent components of SDFs.     

Conditioning Information and Variance Bounds on Pricing Kernels with Higher- Order Moments: Theory and Evidence

We develop a strategy for utilizing higher moments, variancerisk premia, and conditioning information efficiently, and henceimprove on the variance bounds computed by Hansen and Jagannathan(1991); Gallant, Hansen, and Tauchen (1990); and Bekaert andLiu (2004). Our bounds reach existing bounds when nonlinearitiesin returns are not priced. We also use higher moments, variancerisk premia, and conditioning information to provide distancemeasures that improve on the Hansen and Jagannathan (1997) distancemeasure. Empirical results indicate that when accounting forthe impact of higher moments and variance risk premia, the existingpricing kernels have difficulty in explaining returns on theassets and derivatives.     

State Dependence Can Explain the Risk Aversion Puzzle

Risk aversion functions extracted from observed stock and optionprices can be negative, as shown by Aït-Sahalia and Lo(2000), Journal of Econometrics 94: 9–51; and Jackwerth(2000), The Review of Financial Studies 13(2), 433–51.We rationalize this puzzle by a lack of conditioning on latentstate variables. Once properly conditioned, risk aversion functionsand pricing kernels are consistent with economic theory. Todifferentiate between the various theoretical explanations interms of heterogeneity of beliefs or preferences, market sentiment,state-dependent utility, or regimes in fundamentals, we calibrateseveral consumption-based asset pricing models to match theempirical pricing kernel and risk aversion functions at differentdates and over several years.     

Explaining the idiosyncratic volatility puzzle using Stochastic Discount Factors

I use Stochastic Discount Factors to examine the sources of the idiosyncratic volatility premium. I find that non-zero risk aversion and firms’ non-systematic coskewness determine the premium on idiosyncratic volatility risk. The firm’s non-systematic coskewness measures the comovement of the asset’s volatility with the market return. When I control for the non-systematic coskewness factor, I find no significant relation between idiosyncratic volatility and stock expected returns. My results are robust across different sample periods and firm characteristics.