Milk and Money

2017 ◽  
pp. 1-9
Author(s):  
Karl Schmedders ◽  
Patrick Johnston ◽  
Charlotte Snyder
Keyword(s):  
Regression Model ◽  
Prediction Interval ◽  
Linear Regression Analysis ◽  
Dairy Farm ◽  
Dairy Farms ◽  
Price Risk ◽  
Strike Price ◽  
Put Options ◽  
Milk Products ◽  
Chicago Mercantile Exchange

The financial success of dairy farms depends critically on the price of their main output, milk. Large volatility in the price of milk poses a considerable business risk to dairy farms. This is particularly true for family-run dairy farms. The question then arises: how can a farm owner hedge the milk price risk? The standard approach to establish a price floor for a commodity such as milk is to purchase put options on commodity futures. At the Chicago Mercantile Exchange, farmers can buy put options on the price of a variety of milk products. However, the price a farm receives for its milk depends on many factors and is unique to the farm. Thus, a farmer cannot directly buy put options on the price he receives for the milk his farm produces. Instead the farmer needs to determine which of the options available for trade at the Chicago Mercantile Exchange offer the best hedge for his own milk price. The assignment in this case is to examine historical data on several prices of milk products and the milk price received by a family-run dairy farm in California. Students need to find the price that is most closely correlated to the farm's milk price and to then choose options with the appropriate strike price that serve as the best hedge for the farm's price risk.The objective is to expose students to an interesting but simple finance application of linear regression analysis. To solve the case, students must run several simple linear regressions, then use the best regression model they find to make a prediction for the dependent price variable and analyze the prediction interval in order to achieve the desired objective outlined in the case. By completing the case, students will acquire a good understanding of their regression model and its usefulness.

scholarly journals Diyarbakır İli Süt Sığırcılığı İşletmelerinin Yapısal Durum Analizi

2017 ◽  
Vol 5 (5) ◽  
pp. 476 ◽  
Author(s):  
Muhittin Tutkun ◽  
Muzaffer Denli ◽  
Abdullah Sessiz
Keyword(s):  
Dairy Farm ◽  
Dairy Farms ◽  
Demographic Information ◽  
Important Data ◽  
Milk Products ◽  
Face To Face ◽  
Production Values ◽  
Family Based ◽  
Milking Machine ◽  
Average Size

This study was carried out to investigate the structure of dairy farms such as demographic information, management practises, production values, marketing of milk and milk products in Diyarbakır province. In the study, face to face interviews were realized with the 192 dairy farms having 25 and more cattle including 17 district. According to the data collected, dairy enterprises in the region consisted of family based enterprises (90%), cooperative enterprises (7%) and private dairy farms (3%). Cattle breeds distribution in enterprises consist of 12% native breeds, 25% cross- breeds and 63% pure breeds. In dairy enterprises, distribution of cattle was found as 59.7 % of cow, 11.8 % of heifer, 26.1 % of calf, and 2.4 % of bull. The average number of cattle and milking cow per farm were found as 46.7 and 27.8 heads respectively. The type of dairy barns was determined as tie-stall (89%), semi-open (8%) and free-stall (%3) in the cattle enterprises. The average size of land 56% of dairy farm was found under the 50 decares. The 92% of the dairy enterprises declared that the income from dairy was insufficient and 86% were not satisfied as well. The 23% of the farms are used the artificial insemination only. In the herd, 71% of cows were milked by hand and 29% by milking machine. It was found that only 6% of farms sold their milk to dairy factories. This research is important in terms of providing an important data base relating to dairy farming in Diyarbakir province

scholarly journals The Comparison of Innovative Technology Usage Levels of Dairy Farms Supported and Non-Supported by IPARD Program; A Case Study of Konya

2018 ◽  
Vol 6 (12) ◽  
pp. 1809
Author(s):  
Aykut Örs ◽  
Cennet Oğuz
Keyword(s):  
Sampling Method ◽  
Dairy Farm ◽  
Dairy Farms ◽  
Point Of View ◽  
Innovative Technology ◽  
Technology Usage ◽  
Chi Square ◽  
Production Values ◽  
Usage Level ◽  
High Level

The purpose of this study is to compare innovative technology usage levels of dairy farms, supported and non-supported by The Instrument for Pre-accession Assistance-Rural Development (IPARD) program, by scoring their usage level of 10 innovative technologies in their dairy farms. Another purpose of the study is to determine the factors associated with the innovative technology usage levels of dairy farms. The main material of the study is dairy farms supported and not supported by the IPARD program in Konya. Full count sampling method was used when determining the dairy farms supported by IPARD Program and Neyman allocation sampling method was used when determining the dairy farm non-supported by IPARD program. Research data were collected from 50 dairy farms supported by IPARD program and 100 dairy farms non-supported by IPARD program by administering a questionnaire filled during the face-to-face interviews conducted with each individual respondent. As a result of the study, it was determined that the average gross production values and gross profits of dairy farms supported by IPARD program were 4 times higher than those non-supported by IPARD program. While innovative technology usage level of dairy farms non-supported by IPARD program were entirely low level, 90% of dairy farms supported by IPARD program were high level. From the point of view of dairy farm scale, it was determined that innovative technology usage levels were high (69.84%) in dairy farms that had 51 and more milking cows. As a result of chi-square independence test, statistically significant relationship was found between innovative technology usage level of dairy farm and 12 of 13 factors.

scholarly journals The influence on biogas production of three slurry-handling systems in dairy farms

2015 ◽  
Vol 46 (1) ◽  
pp. 30 ◽  
Author(s):  
Damiano Coppolecchia ◽  
Davide Gardoni ◽  
Cecilia Baldini ◽  
Federica Borgonovo ◽  
Marcella Guarino
Keyword(s):  
Organic Matter ◽  
Biogas Production ◽  
Dairy Farm ◽  
Dairy Farms ◽  
Organic Matter Concentration ◽  
Deep Pit ◽  
Volatile Solids ◽  
Methane Potential ◽  
Solid Liquid ◽  
Solid Liquid Separation

Handling systems can influence the production of biogas and methane from dairy farm manures. A comparative work performed in three different Italian dairy farms showed how the most common techniques (scraper, slatted floor, flushing) can change the characteristics of collected manure. Scraper appears to be the most <em>neutral</em> choice, as it does not significantly affect the original characteristics of manure. Slatted floor produces a manure that has a lower methane potential in comparison with scraper, due to: a lower content of volatile solids caused by the biodegradation occurring in the deep pit, and a lower specific biogas production caused by the change in the characteristics of organic matter. Flushing can produce three different fluxes: diluted flushed manure, solid separated manure and liquid separated manure. The diluted fraction appears to be unsuitable for conventional anaerobic digestion in completely stirred reactors (CSTR), since its content of organic matter is too low to be worthwhile. The liquid separated fraction could represent an interesting material, as it appears to accumulate the most biodegradable organic fraction, but not as primary substrate in CSTR as the organic matter concentration is too low. Finally, the solid-liquid separation process tends to accumulate inert matter in the solid separated fraction and, therefore, its specific methane production is low.

A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications

1992 ◽  
Vol 36 ◽  
pp. 1-10
Author(s):  
Anthony J. Klimasara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Mathematical Meaning ◽  
Matrix Correction ◽  
Independent Variables ◽  
Xrf Analysis

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

scholarly journals Cluster and regression analysis for predicting salinity in groundwater

2018 ◽  
Vol 192 ◽  
pp. 02007
Author(s):  
Phiraphat Aphiphan ◽  
Uma Seeboonruang ◽  
Somyot Kaitwanidvilai
Keyword(s):  
Electrical Conductivity ◽  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Water Levels ◽  
Groundwater Salinity ◽  
Groundwater Levels

Groundwater salinity is a major problem particularly in the northeastern region of Thailand. Saline groundwater can cause widespread saline soil problem resulting in reducing agricultural productivity as in the Lower Nam Kam River Basin. In order to better manage the salinity problem, it is important to be able to predict the groundwater salinity. The objective of this research was to create a cluster-regression model for predicting the groundwater salinity. The indicator of groundwater salinity in this study was electrical conductivity because it was simple to measure in field. Ninety-eight parameters were measured including precipitation, surface water levels, groundwater levels and electrical conductivity. In this study, the highest groundwater salinity at 3 wells was predicted using the combined cluster and multiple linear regression analysis. Cross correlation and cluster analysis were applied in order to reduce the number of parameters to effectively predict the quality. After the parameter selection, multiple linear regression was applied and the modeling results obtained were R2 of 0.888, 0.918, and 0.692, respectively. This linear regression model technique can be applied elsewhere in the similar situation.

scholarly journals Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Adewale F. Lukman ◽  
Kayode Ayinde ◽  
B. M. Golam Kibria ◽  
Segun L. Jegede
Keyword(s):  
Monte Carlo Simulation ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Ordinary Least Squares ◽  
M Estimator ◽  
Ordinary Least Squares Estimator ◽  
General Linear Regression Model ◽  
Two Parameter

The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter. The problems of the OLS estimator for linear regression analysis include that of multicollinearity and outliers, which lead to unfavourable results. This study proposed a two-parameter ridge-type modified M-estimator (RTMME) based on the M-estimator to deal with the combined problem resulting from multicollinearity and outliers. Through theoretical proofs, Monte Carlo simulation, and a numerical example, the proposed estimator outperforms the modified ridge-type estimator and some other considered existing estimators.

Immersed Membrane BioReactor (IMBR) for treatment of combined domestic and dairy wastewater in an isolated farm

2005 ◽  
Vol 51 (10) ◽  
pp. 327-334 ◽  
Author(s):  
A. Bick ◽  
J.G.P. Tuttle ◽  
S. Shandalov ◽  
G. Oron
Keyword(s):  
Membrane Bioreactor ◽  
Dairy Farm ◽  
Domestic Wastewater ◽  
High Strength ◽  
Dairy Farms ◽  
Pilot Scale ◽  
Dairy Wastewater ◽  
Transmembrane Pressure ◽  
Research Approach ◽  
Permeate Flux

In many regions dairy farms and milk processing industries discharge large quantities of their wastes to the surroundings posing serious environmental risks. This problem is mostly faced in small dairy farms and isolated communities lacking both central collection and conventional wastewater treatment systems. Dairy wastewater is characterized by high concentrations of organic matter, solids, nutrients, as well as fractions of dissolved inorganic pollutants, exceeding those levels considered typical for high strength domestic wastewaters. With the purpose of treating the combined dairy and domestic wastewater from a small dairy farm in the Negev Desert of Israel, the use of a recent emerging technology of Immersed Membrane BioReactor (IMBR) was evaluated over the course of 500 test hours, under a variety of wastewater feed quality conditions (during the test periods, the feed BOD5 ranged from 315 ppm up to 4,170 ppm). The overall performance of a pilot-scale Ultrafiltration (UF) IMBR process for a combined domestic and dairy wastewater was analyzed based on the Data Envelopment Analysis (DEA) method. The IMBR performance in terms of membrane performance (permeate flux, transmembrane pressure, and organic removal) and DEA model (Technical Efficiency) was acceptable. DEA is an empirically based methodology and the research approach has been found to be effective in the depiction and analysis for complex systems, where a large number of mutual interacting variables are involved.

Implied Volatility of the KOSPI 200 Index Option Market

2015 ◽  
Vol 23 (4) ◽  
pp. 517-541
Author(s):  
Dam Cho
Keyword(s):  
Financial Crisis ◽  
Trading Volume ◽  
Implied Volatility ◽  
Spot Price ◽  
Strike Price ◽  
Market Pricing ◽  
Put Options ◽  
The Difference ◽  
Index Option ◽  
Option Market

This paper analyzes implied volatilities (IVs), which are computed from trading records of the KOSPI 200 index option market from January 2005 to December 2014, to examine major characteristics of the market pricing behavior. The data includes only daily closing prices of option transactions for which the daily trading volume is larger than 300 contracts. The IV is computed using the Black-Scholes option pricing model. The empirical findings are as follows; Firstly, daily averages of IVs have shown very similar behavior to historical volatilities computed from 60-day returns of the KOSPI 200 index. The correlation coefficient of IV of the ATM call options to historical volatility is 0.8679 and that of the ATM put options is 0.8479. Secondly, when moneyness, which is measured by the ratio of the strike price to the spot price, is very large or very small, IVs of call and put options decrease days to maturity gets longer. This is partial evidence of the jump risk inherent in the stochastic process of the spot price. Thirdly, the moneyness pattern showed heavily skewed shapes of volatility smiles, which was more apparent during the global financial crises period from 2007 to 2009. Behavioral reasons can explain the volatility smiles. When the moneyness is very small, the deep OTM puts are priced relatively higher due to investors’ crash phobia and the deep ITM calls are valued higher due to investors’ overconfidence and confirmation biases. When the moneyness is very large, the deep OTM calls are priced higher due to investors’ hike expectation and the deep ITM puts are valued higher due to overconfidence and confirmation biases. Fourthly, for almost all moneyness classes and for all sub-periods, the IVs of puts are larger than the IVs of calls. Also, the differences of IVs of deep OTM put ranges minus IVs of deep OTM calls, which is known to be a measure of crash phobia or hike expectation, shows consistent positive values for all sub-periods. The difference in the financial crisis period is much bigger than in other periods. This suggests that option traders had a stronger crash phobia in the financial crisis.

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