My Research
My Research Papers:
Paper 1:
Flexible Modeling of Multivariate Risks in Pricing Margin Protection Insurance: Modeling Portfolio Risks with Mixtures of Mixtures
Seyyed Ali Zeytoon Nejad Moosavian, Szeytoo@ncsu.edu, North Carolina State University
[Job Market Paper]
Abstract:
Margin Protection Programs (MPPs) are relatively new insurance plans, introduced by USDA’s Risk Management Agency (RMA). These programs were initially implemented for livestock and dairy producers, and subsequently were extended to cover other agricultural products such as corn, rice, soybeans, and wheat. The attractiveness of these risk management instruments lies in the fact that the financial stability of agricultural production and farming operations is more dependent on margins than solely revenues. This paper examines the structure and rating of margin protection insurance policies by considering a broad class of high-dimensional copula models that parameterize the dependence among multivariate sources of risks. To efficiently and accurately determine actuarially fair policy premiums, it is necessary to first model the joint distribution function of input and output prices. This task can be effectively carried out using copula methods. A variety of copula methods, including Archimedean Copulas (ACs), Mixture Copulas (MCs), and Vine Copulas (VCs) are used to analyze the dependence structure between revenues and input costs. In terms of methodology, flexible mixtures of parametric distributions are applied to characterize marginal densities, and likewise flexible mixtures of alternative copulas are used to model dependence. This paper also argues that the rating methodology that accounts for irregular and anomalous features of dependence such as asymmetry, non-linearity, non-ellipticity, and tail dependence between input prices and output prices can result in more accurate premiums, and therefore can increase the hedging effectiveness of the MPPs. In this paper, several reasons are identified to explain why the common methods being currently employed to determine policy premiums might not be adequate, realistic, or sufficiently flexible to take into account the multivariate aspects of risks involved in farming operations. To this end, the present paper investigates the underlying assumptions based on which the MPP policy premiums are determined. It is argued that assumptions made in pricing risks may induce important distortions in the production and marketing decisions of producers. Finally, implications for the ever-expanding offerings of publicly-subsidized agricultural insurance mechanisms are offered.
Key Words: Insurance, Mixture Distribution, Vine Copulas, Margin Protection Programs, Livestock Gross Margin, Nonlinear Time Series Models, Dependence Modeling, Tail Dependence, Output Prices, and Input Prices
JEL Classification: C58, G13, G22, Q11, Q12, Q13, Q18
Paper 2:
Eliciting Risk Attitudes, Measuring Risk Aversion, and Estimating Risk Premiums Using Indirect Utility Functions: A Laboratory Experiment
Seyyed Ali Zeytoon Nejad Moosavian, Szeytoo@ncsu.edu, North Carolina State University
Robert G. Hammond, Robert_hammond@ncsu.edu, North Carolina State University
Barry K. Goodwin, Barry_goodwin@ncsu.edu, North Carolina State University
Abstract:
Eliciting risk attitudes and estimating the degree of risk aversion are of crucial importance in the realm of economics. Duality Theory (DT) in modern microeconomics indicates that Direct Utility Function (DUF) and Indirect Utility Function (IUF) are dual to each other. As such, the DT implicitly suggests that the degree of risk-aversion (or -seeking) that a given (rational) subject exhibits in the context of DUF must be equivalent to the degree of risk-aversion (or -seeking) elicited through the context of IUF. This paper tests the accuracy of this theoretical prediction through a lab experiment. Our methodology relies on elicitations that use payoff-based lottery choices (which are based on DUF and uncertainty about payoffs) versus their equivalent price-based lottery choices (which are based on IUF and uncertainty about prices). We use the Multiple Price List (MPL) method, which has been one of the most popular sets of elicitation procedures in experimental economics to elicit risk attitudes and measure the degree of risk aversion in the experimental laboratory using non-interactive settings. Among the most well-known MPL designs are Holt & Laury (2002), Binswanger (1980), and the Certainty-versus-Uncertainty design (henceforth, the H&L, Bins., and CvU designs, respectively). We have adopted, designed, and calibrated six equivalent risk elicitation designs using the aforementioned MPL designs in such a way that, given Expected Utility Theory (EUT) and the DT, each should elicit the same degree of risk aversion exhibited by a given rational individual, although the designs differ in form, i.e. in terms of their approaches (i.e. DUF vs. IUF) and their MPL designs (i.e. H&L, Bins., and CvU). Our key findings indicate that the implicit suggestion of the DT concerning the degree of risk aversion being equivalent under DUF and IUF is rejected. We show that Price Risk Aversion (PrRA) is statistically significantly greater than Payoff Risk Aversion (PaRA). We also show that the risk preferences elicited under the EUT are somewhat subject to context. That is, “the degrees of risk aversion” elicited under the EUT are somewhat subject to context (here, the MPL designs), but the broadly-defined “risk attitudes” (i.e. risk loving, risk neutral, and risk averse) elicited under the EUT are not subject to context as much. Furthermore, we our findings imply that Risk Premium (RP), as a measure of willingness to pay for insuring an uncertain situation, is statistically significantly greater for stochastic prices compared to that for stochastic payoffs. We also show that although women typically exhibit higher degrees of PaRA compared to men (which is a result consistent with the mainstream literature), they exhibit lower degrees of PrRA compared to men. All of these results are robust across the different MPL designs and different approaches and various statistical tests that we have utilized.
Key Words: Risk Aversion, Risk Attitudes, Risk Premium, Multiple Price List, Direct Utility Function, Indirect Utility Function, Payoff Risk Aversion, and Price Risk Aversion
JEL Classification: C90, C91, D01, D81, D9, G4, G22
The dataset of the paper is available at: Dataset
Paper 3:
Generalizing the General: Generalizing the CES Production Function to Allow for the Flexibility of Input-Driven Output Risk and Viability of Input Thresholds
Seyyed Ali Zeytoon Nejad Moosavian, Szeytoo@ncsu.edu, North Carolina State University
Barry K. Goodwin, Barry_goodwin@ncsu.edu, North Carolina State University
Abstract:
The original specification of the Constant-Elasticity-of-Substitution (CES) production function introduced by Arrow, Chenery, Minhas, and Solow is considered to be a general production specification that nests multiple types of production functions, i.e. Leontief, Cobb-Douglas, and linear. However, even this general specification of production functions is restrictive in several ways. This paper proposes two generalized variants of the CES production function that allow for various input effects on the probability distribution of output as well as the inclusion of the minimum required levels of inputs. Not allowing for these two potential attributes are, in fact, two shortcomings of the original CES production-function specification, which in turn could result in misleading conclusions and false inferences about input-driven output risk and essential levels of inputs, respectively. Accordingly, two solutions are proposed to overcome the two mentioned shortcomings. First, input thresholds are incorporated in the CES production specification, and empirical applications are provided for irrigation and nitrogen. Afterwards, it is shown that the familiar CES formulation suffers from very restrictive structural assumptions regarding risk considerations, and that such restrictions may lead to biased and inefficient estimates of production quantity and production risk. Following Just and Pope (1979), a CES-based production-function specification that overcomes this shortcoming of the original CES production function is introduced, and a three-stage Nonlinear Least Square (NLS) estimation procedure for the estimation of the proposed functional form is presented. To illustrate the proposed approaches in this paper, several empirical applications in irrigation and fertilizer response using the famous Hexem-Heady experimental dataset are provided. Finally, implications for modeling input thresholds as well as input-driven production risks are considered and discussed.
Key Words: Constant Elasticity of Substitution, Production Function, Output Risk, and Threshold Input Requirements
JEL Classification: C10, C51, D20, Q10
Note: For an updated long list of my working papers and work in progress, please see the most recent version of my CV.
