Risk analysis model and agricultural derivative market use (original) (raw)
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Price Uncertainty and Optimal Hedging in the Agricultural Market
Transylvanian Review of Administrative Sciences, 2014
The increased volatility of the agricultural prices has detrimental effects on the economic welfare and raises concerns regarding poverty and malnutrition at a global level. Financial risk management can be an efficient solution for limiting the effects of international agricultural price volatility. The paper analyzes the behavior of the U.S. wheat and corn prices, emphasizing their highly volatile and unpredictable nature. Given the existence of the basis risk, the estimation of the optimal hedge ratio is needed in order to provide an efficient hedging strategy against price risks. The role of public authorities in this context can consist in promoting education in the fields of hedging and understanding the agricultural price volatility risk. We estimate static and time varying optimal hedge ratios for wheat and corn through several methods. Based on the out of sample hedging effectiveness given by the variance reduction, the methods are compared and the results show that the time varying hedge ratios estimated through rolling window OLS and GARCH methods outperform the static counterparts.
Five essays on risk analysis in agriculture
2001
Lien, G., 2001. Five essays on risk analysis in agriculture. Doctor oeconomiae dissertation, Norwegian School ofEconomics and Business Administration. 124 pp. Agricultural production is typically a risky business. For many decades governments around the world have intervened in order to try to help farmers cope more effectively with risk. Both national and international developments have led many countries to reorientate their agricultural policies towards deregulation and a more marked-oriented approach. Much of the protection that farmers have had from the vagaries of the market may therefore be removed. Thus, it can be expected that in the future risk management in agriculture will receive increased attention from farmers, agricultural advisers, commercial firms, agricultural researchers, and policy makers. The objective of the three first essays in the dissertation is to contribute to the available formal methods of farm planning under uncertainty. Such methods are usually based on the propositions, not always made explicit, that farmers are risk averse and that the opportunities for them to trade away the risks they face in markets are constrained. The last two essays are studies of risk in the markets for agricultural commodities, and the objective is to improve the understanding of how the agricultural derivative markets work and to develop an option pricing model for commodity futures options. Essay 1 outlines an alternative method for estimating decision maker's risk aversion. The method uses the expected value-variance (E-V) framework and quadratic programming. An empirical illustration is given using Norwegian farm-level data. Essay 2 provides a two-stage utility-efficient programming approach to modelling integrated dairy and cash crop farming in a whole-farm context that includes both embedded and nonembedded risk. The model is used to provide insight into the impacts of degree of risk aversion, subsidy schemes and the choice of utility function on optimal farm plans in Norwegian agriculture. In essay 3 a stochastic budgeting model that simulate the business and financial risk and the performance over a medium term planning horizon is presented. Some methods to account for stochastic dependencies are outlined. In contrast with earlier studies with stochastic farm budgeting, the option aspect is included in the analysis. The objective in essay 4 is to model the spot-price process for an agricultural product, where we find that adding a jump component to a diffusion process contributes to a better fit on monthly spot wheat data from 1952 to 1998 in Atlanta. Essay 5 investigates implication that price jumps and the volatility term-structure have for option pricing of agricultural futures commodities. We extend a jump-diffusion model to include both seasonal and maturity effects in volatility. An in-sample fit to market option prices of Chicago Board of Trade wheat futures from 1989 to 1999 shows that our model outperforms models previously described in the literature.
Price Risk and Risk Management in Agriculture
Contemporary Economics, 2013
This note studies the risk-management decisions of a risk-averse farmer. The farmer faces multiple sources of price uncertainty. He sells commodities to two markets at two prices, but only one of these markets has a futures market. We show that the farmer's optimal commodity futures market position, i.e., a cross-hedge strategy, is actually an over-hedge, a full-hedge, or an under-hedge strategy, depending on whether the two prices are strongly positively correlated, uncorrelated, or negatively correlated, respectively.
Financial Risk Quantification of Indian Agro-Commodities using Value At Risk
International Journal of Engineering and Advanced Technology (IJEAT), 2019
Indian commodity traders are exposed to various risks like price risk, market risk, financial risk, credit risk, etc. To understand the risk resulting in the financial impact, this paper attempts to assess the historical trends of commodity prices and probability of loss occurrence in the commodity invested. The present study analyses five Indian agro commodities namely, Almond, Cardamom, Cotton, Guar Seed and Wheat using the data collected from secondary sources like Multi Commodity Exchange (MCX), Securities Exchange Board of India (SEBI) etc. This paper uses the Historical Simulation method for the calculation of Value at Risk (VaR) by considering spot prices of the commodities on MCX for a five year period (2013-2018). It is established that Value at Risk (VaR) is a relevant measure to arrive at risk which is useful for the commodity traders to estimate the financial risk and thus control the risk exposure.
2019
Future trading in India was first recorded around 1800s. In this regard, post-independence, the Forward Contracts (Regulation) Act, 1952 (FCRA, 1952) came into existence for regulating the market, wherein Forward Markets Commission (FMC) was set up in 1953 as the regulator. Commodity derivatives in India have witnessed turbulent history, where derivative trading was banned in the late 1960’s but was revived again in the 1980’s. However, Government of India’s successful crusade of equity market reforms of the 1990’s, similar reforms for the commodity derivatives markets were also desired to be implemented. In this respect, the idea of replacing derivatives markets, as a price-hedging instrument, with Minimum Support Price (MSP) was propagated during 1999. In view of the above, various commodity exchanges at National level were permitted to start operation. The aim of the study will be to identify the ability of the exchanges to act as platform for hedging the risk by various particip...
Measurement of Commodity Price Risk: an overview of Brazilian agricultural markets
Revista de Economia e Sociologia Rural
This study explores different procedures to estimate price risk in commodity markets. Focusing on Brazilian agricultural markets, the paper proposes to assess both dispersion and downside risk measures using five different approaches (volatility, coefficient of variation, lower partial moments, value at risk and conditional value at risk). Results suggest that some commodities have large price variability but small downside risk, while other commodities show small price variability and large downside risk. Thus, there is no single answer to the question of which commodity exhibits more price risk, but rather distinct answers depending on how risk is perceived by different individuals. These findings are relevant for agents in the agricultural industry as they affect marketing and risk management decisions and for policy makers involved in support programs to agriculture.
The impact of investors in agricultural commodity derivative markets
Outlook on Agriculture, 2016
The objective of this paper was to test whether investing activity in the futures markets of different commodities (grains, sugar, coffee, cotton, cocoa, livestock) could be identified as a source of the increasing level and volatility of agricultural commodity prices. The causal link between trading activity and market factors (returns, volatility) can be investigated using weekly data, usually derived from the Commitment of Traders Reports released by the US Commodity Futures Trading Commission (CFTC), or daily data expressed as the ratio of volume to open interest (VOIR). To increase the power of the estimation process and investigate the role of causal variables to determine the trends of all the market factors, the authors tested the estimates obtained by seemingly unrelated regression (SUR). One innovation is represented by the evaluation of the inverse relationships between market factors and causal variables. The market factors were also tested as causal variables, avoiding giving priority to only one part of the relationship according to Granger's causality. The lack of significance revealed by the Granger causality test on weekly models could be due to the inappropriate frequency of the information. The ratio of volume to open interest in futures contracts performs better than other parameters extensively adopted in the literature. The likely reason is that it depends on the daily frequency of this parameter, which provides statistical evidence of phenomena that include their effect in weekly intervals. The estimations for the daily model provide statistical evidence of a mutual relationship only between trading activity and realized volatility. No causal relationships were found for returns. The behaviour of all 12 futures markets examined is quite similar and uniform with respect to the scale of the coefficients and their temporal profile.
Price risk management instruments in agricultural and other unstable markets
ICFAI journal of risk and insurance, 2005
Summary Most economists congregate on the idea that commodity price instability should be reduced. Since at least one century a variety of instruments have been designed to that end, without much success, especially for agricultural commodities. The failure might be a ...
Modelling risk in agricultural finance: Application to the poultry industry in Taiwan
Mathematics and Computers in Simulation, 2009
The volatility in agricultural prices, such as for broiler and color broiler chickens in Taiwan, is similar in various aspects to financial volatility as it relates to the risk and returns associated with agricultural production. However, as the characteristics of agricultural markets may be different from financial markets, the results arising from empirical risk analysis need to be investigated. The broiler and color broiler industries are the second and third largest livestock industries in Taiwan. When Taiwan applied to join the World Trade Organization (WTO) in the 1990s, these two industries faced the threat of deregulation of chicken meat imports. However, developments in these two industries have not been the same under deregulation, with the level of competition in the broiler and color broiler industries being markedly different. The purpose of the paper is to model the prices, growth rates and their respective volatilities in weekly broiler and color broiler chicken prices in Taiwan from January 1995 to June 2007. The empirical results show that the time series of broiler and color broiler prices, their logarithms and their growth rates are stationary, and that the estimated symmetric and asymmetric conditional volatility models all fit the data extremely well. The empirical second moment and log-moment conditions also support the statistical adequacy of the estimated volatility models. The empirical results have significant implications for risk management and policy considerations in the agricultural production industry in Taiwan.