Time Scale and Fractionality in Financial Time Series

Purpose: Turvey (2007, Physica A) introduced a scaled variance ratio procedure for testing the random walk hypothesis (RWH) for financial time series by estimating Hurst coefficients for a fractional Brownian motion model of asset prices. The purpose of this paper is to extend his work by making the estimation procedure robust to heteroskedasticity and by addressing the multiple hypothesis testing problem. Design/methodology/approach: Unbiased, heteroskedasticity consistent, variance ratio estimates are calculated for end of day price data for eight time lags over 12 agricultural commodity futures (front month) and 40 US equities from 2000-2014. A bootstrapped stepdown procedure is used to obtain appropriate statistical confidence for the multiplicity of hypothesis tests. The variance ratio approach is compared against regression-based testing for fractionality. Findings: Failing to account for bias, heteroskedasticity, and multiplicity of testing can lead to large numbers of erroneous rejections of the null hypothesis of efficient markets following an independent random walk. Even with these adjustments, a few futures contracts significantly violate independence for short lags at the 99 percent level, and a number of equities/lags violate independence at the 95 percent level. When testing at the asset level, futures prices are found not to contain fractional properties, while some equities do. Research limitations/implications: Only a subsample of futures and equities, and only a limited number of lags, are evaluated. It is possible that multiplicity adjustments for larger numbers of tests would result in fewer rejections of independence. Originality/value: This paper provides empirical evidence that violations of the RWH for financial time series are likely to exist, but are perhaps less common than previously thought

The Economics of Nested Insurance: The Case of SURE

Traditionally, disaster assistance was available on an ad hoc basis, but the 2008 Farm Act provides a standing disaster assistance program known as Supplemental Revenue Assistance (SURE). This paper introduces a theory of nested insurance to evaluate the impact on of SURE on intensification, acreage and adoption. The results suggest that parameters of a government program like SURE may enhance the adoption and value of crop insurance to the farm sector. A quantitative understanding of the interdependencies between programs like SURE and crop insurance, taking into account the nature of the ad hoc alternative, is important in assessing the welfare impacts on farmers, as well as insurance companies. Both our theory and simulation exercise suggest that insurance increases the volume of production and/or leads to increased intensification (substitution into higher value crops). On the other hand, the gains from insurance and from programs like SURE may be lessened by the presence and probability of ad hoc disaster assistance.Nested insurance, SURE, crops, adoption, ad hoc, disaster assistance, Crop Production/Industries, Risk and Uncertainty,

A General Equilibrium Theory of Contracts in Community Supported Agriculture

Community Supported Agriculture (CSA) contracts allow consumers to buy claims on a farm\u27s future production. In turn, the consumer provides working capital to the farm during the growing season. CSA contracts also provide risk management for farmers with limited access to Federal crop insurance by transferring part of the farm\u27s risk to the consumer. We derive a theory of CSA contract pricing for the two most prevalent types of CSA contracts: yield contracts, in which consumers receive a percentage of the farm\u27s production, and weight contracts, in which consumers receive fixed quantities. We develop a two-period model in which expected utility maximizing producers and consumers engage in CSA contracting in the first period based on anticipation of yields and spot prices in the second period. Using the model, we generate several testable hypotheses to be explored in future research. Additionally, we present an overview of the data necessary to test the propositions and potential challenges that might arise in related empirical work

Heterogeneity in loss aversion: evidence from field elicitations

Purpose: Prospect theory is now widely accepted as the dominant model of choice under risk, but has not been fully incorporated into applied research because of uncertainty about how to include population-level parameter estimates. The purpose of this paper is to characterize heterogeneity across people to lay a foundation for future applied research. Design/methodology/approach: The paper uses elicitation data from field experiments in Vietnam to fit a finite Gaussian mixture model using the expectation maximization algorithm. Applied results are simulated for investment allocations under myopic loss aversion. Findings: The authors find that about 20 percent of the sample is classified as extremely loss averse, while the rest of the population is only mildly loss averse. This implies a bimodal distribution of loss aversion in the population. Research limitations/implications: The data set is only moderately sized: 181 subjects. Future research will be needed to extend these results out of sample, and to other regions. Originality/value: This paper provides empirical evidence that heterogeneity matters in prospect theory modeling. It highlights how policy makers might be misled by assuming that average prospect theory parameters are typical within the population

Bayesian Downscaling Methods for Aggregated Count Data

Policy-critical, micro-level statistical data are often unavailable at the desired level of disaggregation. We present a Bayesian methodology for “downscaling” aggregated count data to the micro level, using an outside statistical sample. Our procedure combines numerical simulation with exact calculation of combinatorial probabilities. We motivate our approach with an application estimating the number of farms in a region, using count totals at higher levels of aggregation. In a simulation analysis over varying population sizes, we demonstrate both robustness to sampling variability and outperformance relative to maximum likelihood. Spatial considerations, implementation of “informative” priors, non-spatial classification problems, and best practices are discussed

Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment

The aquaculture industry has expanded to fill the gap between plateauing wild seafood supply and growing consumer seafood demand. The use of genetic modification (GM) technology has been proposed to address sustainability concerns associated with current aquaculture practices, but GM seafood has proved controversial among both industry stakeholders and producers, especially with forthcoming GM disclosure requirements for food products in the United States. We conduct a choice experiment eliciting willingness-to-pay for salmon fillets with varying characteristics, including GM technology and GM feed. We then develop a predictive model of consumer choice using LASSO (least absolute shrinkage and selection operator)-regularization applied to a mixed logit, incorporating risk perception, ambiguity preference, and other behavioral measures as potential predictors. Our findings show that health and environmental risk perceptions, confidence and concern about potential health and environmental risks, subjective knowledge, and ambiguity aversion in the domain of GM foods are all significant predictors of salmon fillet choice. These results have important implications for marketing of foods utilizing novel food technologies. In particular, people familiar with GM technology are more likely to be open to consuming GM seafood or GM-fed seafood, and effective information interventions for consumers will include details about health and environmental risks associated with GM seafood

Deductibles vs. Coinsurance in Shallow-Loss Crop Insurance

Shallow-loss policies take center-stage in many proposals for the current Farm Bill. We examine the choice of deductible coverage vs. coinsurance to show risk premiums and loss adjustment costs matter little when comparing policies. Thus, policy makers should base decisions more on costs to taxpayers than specific risk management features

The Pricing of Community Supported Agriculture Shares: Evidence from New England

Purpose: Community Supported Agriculture (CSA) programs allow consumers to buy a share of a farm’s production while providing working capital and risk management benefits for farmers. Several different types of CSA arrangements have emerged in the market with terms varying in the degree to which consumers share in the farm’s risk. No-arbitrage principles of futures and options pricing suggest that CSA shares should be priced to reflect the degree of risk transfer. Methodology: We evaluate the three most common share types using a cross-sectional dataset of 226 CSA farms from New England to determine if there is empirical evidence in support of the theoretical price relationship between share types. Findings: The degree of risk transfer from farmers to consumers has a significant effect on the share price. There are statistically significant returns to scale and higher prices for organics. Farm characteristics and product offerings predict which type of shares is offered for sale. Research limitations: The data set does not contain information pertaining to actual deliveries, expected deliveries, variance of expected deliveries, or covariance information; thus differences in share prices could be due to differences in these uncontrolled factors. Value: This paper provides empirical evidence that CSA share prices reflect the degree of risk transferred from the producer to the consumer. It also highlights challenges in conducting empirical work pertaining to CSA contracting

Prospect Theory and Tenure Reform: Impacts on Forest Management

We examine the role of risk and time preferences in how forest owners respond to forest certification. We test hypotheses from a two-period harvest model derived from prospect theory in the context of Fujian, China, where new forest certification started in 2003. Using survey and field experiment data, we find that certification resulted in reduced harvesting, and the effect was larger for households who are more risk averse and exhibited distorted probability weighting. In contrast, loss averse households increased harvesting after certification. These findings suggest that diverse individual preferences may be a source of impact heterogeneity for forest certification

Environmental Policy with Collective Waste Disposal

Centralized collection and disposal is an integral component of waste management strategies for many solid and liquid wastes, and carbon capture and storage is currently being considered for gaseous waste. In this paper we show how collective waste disposal systems introduce essential changes in the design of optimal environmental policy. Absent collective disposal, an optimal environmental policy imposes relatively stringent regulations on polluters in regions where local environmental damage functions are “high”; however, under collective waste disposal, the optimal environmental policy level increases monotonically over distance from the disposal site, and this is true irrespective of the degree of spatial heterogeneity in local environmental damage functions. We characterize the optimal spatial pattern of environmental policy levels under collective waste disposal and identify optimal membership size for waste disposal networks comprised of homogeneous producers