Robust Volatility Estimation with and Without the Drift Parameter

We find the closed form solution for the joint probability of the running maximum and the drawdown of the Brownian motion with a non-zero drift parameter at a random time that is exponentially distributed and independent of the Brownian motion. This characterization leads us to come up with a robust method of estimating volatility using open, high, low and closing prices. We rigorously show the independence of robust volatility estimators based on extreme values of asset prices relative to the standard robust volatility estimator based on closing price alone. We further prove that the proposed robust volatility ratio is unbiased with no drift parameter. Moreover, we find that the robust volatility ratio with a non-zero drift parameter has only a second order effect. We have shown that our proposed extreme value robust volatility estimator is 2–3 times relatively more efficient when compared to the classical robust volatility estimator based on Monte Carlo simulation experiment. On the empirical side, we test the proposed robust volatility ratio based on high and low prices on different asset classes like stock indices, exchange rate and precious metals.

     

Risk management within the cannabis industry: Building a framework for the cannabis industry

Thirty states and the District of Columbia have legalized the use of cannabis for medicinal and/or recreational use by either formally or informally de‐criminalizing its use. However, cannabis remains a Schedule 1 drug under the Federal Controlled Substances Act (21 U.S.C. Sections 801 through 812), leaving federal law in conflict with the laws of over half of the states. As a result, market participants in legal cannabis businesses face risks due to the industry's unique legal status within the United States. We examine the risks and challenges deemed by the cannabis industry as the top risks facing the industry's continued future growth and its sustainability. In addition to general risks inherent in a nascent industry, a legal cannabis business faces additional risks, such as risks in its banking and finance activity, placement of insurance, payment of taxes, and managing its supply chain. These legal businesses also face true legal risk from the possibility of being shut down by the federal government and seizure of assets and product under the CSA. This paper also examines whether the cannabis industry would benefit from a futures market to mitigate price risk.     

Globalisation and Obesity

The WHO has recently announced the global obesity epidemic. An economic model is developed in which globalisation factors generate health externalities and contribute to global obesity growth. The unbalanced panel data set contains the information for 79 countries over the period 1986–2008. Fixed‐effects panel data estimation and quantile regression analysis were used to analyse the data. The fixed‐effects panel model results indicate that the impact of trade openness and the globalisation social index (GSI) on global obesity rates is positive and significant, which is consistent with prior expectations, while surprisingly the foreign direct investments (FDI) has no impact on global obesity. While these results are interesting, they are hiding the effect of globalisation processes across the conditional distribution of the obesity variable. The use of quantile regression uncovered that the impact of the FDI and the GSI on low and average quantiles (low and average obesity rates in our sample) is positive and significant, while high quantiles are not affected. Since low and average quantiles (low and average obesity rates) are representative of the less‐ and medium‐developed countries, this result implies that social globalisation and FDI adversely impact obesity in less‐to‐medium developed countries. Trade openness generally has no impact on changes in obesity rates across quantiles.     

Estimating the Prevalence and Cost of Yield-Switching Fraud in the Federal Crop Insurance Program

Producers who manipulate and switch their reported crop-yields between separately insured units can increase their insurance indemnities substantially. A statistical model that identifies potential yield switching is developed. The unrestricted statistical model is singular and is identified by imposing a mixture of system-estimable and system-nonestimable restrictions. Lower bound estimates of yield-switching fraud incidence and costs are obtained by applying the model to 207,067 multiple unit producers who purchased crop insurance in 1998.     

Can Normality of Yields Be Assumed for Crop Insurance?

Normality of crop yield residuals pooled to the state and regional level is examined using a procedure that accounts for trend and heteroscedasticity. Normality tests are conducted using farm-level yield data from over 200,000 producers of six crops in seven U.S. states. The results indicate consistent non-normality of crop yields. The effects of assuming normality on insurance premium rates are examined. Assuming normality is found to generate premiums that can differ substantially from premiums derived using data-based empirical distributions.
Montana Agricultural Experiment Station Journal Series No. 2001-7. The Risk Management Agency, USDA, also provided support for this research. The views expressed herein are the authors' and do not necessarily represent those of Montana State University or the Risk Management Agency.
Les auteurs ont analysé la normalité du rendement résiduel des cultures à l'échelon de l'État et à l'échelon régional au moyen d'une méthode tenant compte des conjectures et de l'hétéroscédasticité. Ils ont utilisé les données sur le rendement fournies par plus de 200 000 cultivateurs produisant six cultures dans sept États américains pour effectuer des tests de normalité. Les résultats révèlent l'anormalité constante des rendements. Les auteurs s'interrogent sur les conséquences d'une normalité hypothétique sur les primes d'assurance. En supposant des rendements normaux, on obtient des primes sensiblement différentes de cedes calculées avec une distribution empirique des données.     

The impact of trade openness on technical efficiency in the agricultural sector of the European Union

The aim of this study is to analyse the impact of trade openness on technical efficiency of the European Union’s (EU) agricultural sector. There are no systematic theories linking trade policy to technical efficiency; hence, the relation between trade liberalization and technical efficiency is fundamentally ambiguous. Stochastic frontier analysis is used to model the relationship between EU’s production resources and agricultural output, as well as the importance of trade openness on technical efficiency of a country. The data for 16 of the 28 EU members were available for the period 1980–2007 including land, capital, fertilizer, labour, agricultural GDP, foreign direct investments (FDI), exports and import data. Results indicate that trade openness has an immediate, negative impact on efficiency in the EU agricultural sector. Over time, however, trade openness does increase efficiency. The FDI outflows increase efficiency. This suggests that an initial reduction in capital supply forces EU nations to utilize other factor inputs more efficiently. However, there is the unexamined potential that over time the depletion of capital results in a decrease in efficiency. Finally, formerly communist member-countries of the EU are found to have the lowest technical efficiency scores whereas Southern European nations have the highest efficiency.     

Re‐examination of production,cost, and restricted profit functions using quantile regression approach

This article estimates quantile regressions of production, cost, and restricted profit functions using a Cobb‐Douglas functional form with non‐Hicks neutral technology change. In contrast to previous studies, quantile regression estimates reveal the relationship between the independent (production, cost, and restricted profit) and dependent (input quantities and prices) variables at each quantile of the distribution. An empirical application using data from 48 states in United States from 1960 to 2004 indicates the returns to scale and aggregate technology not only differ across production, cost, and restricted profit functions but across states in different quantiles of the distribution. This suggests the traditional measures of returns to scale and aggregate technology are under‐ and overestimated in states at upper and lower quantiles, respectively.     

A Comparative Study of Alternative Approaches to Estimate Productivity

Theoretically, for single output-single input, annual productivity are expected to be identical across index, non-parametric programming and parametric statistical approaches. The following models within each approach is considered—index (Tornqvist-Theil and Ideal Fisher), the non-parametric programming (Malmquist input, output and graph; Malmquist total factor productivity) and parametric (Input and Output; total factor productivity) regression. Empirically, for single output-single input, this research show differences in annual productivity and productivity growth rate between and within each of the three approaches using Nebraska agriculture data from 1936 to 2004. The annual productivity growth rate from 1936 to 2004 was identical across non-parametric Malmquist output, input, graph and Malmquist total factor productivity, and parametric Malmquist total factor productivity. However annual productivity estimated by parametric Malmquist total factor productivity is identical to Ideal Fisher productivity.