Revenue insurance with shallow loss protection for farmers has been introduced recently. A common attribute of most shallow loss proposals is that they would be arearevenue triggered. The impact on optimal hedge ratios of combining these shallow loss insurance proposals with deep loss farm-level insurance is examined. Since crop insurance, commodity programs and forward pricing are commonly used concurrently to manage crop revenue risk, the optimal combinations of these tools are explored. Numerical analysis in the presence of yield, basis and futures price variability is used to find the futures hedge ratio which maximizes the certainty equivalent of a risk averse producer. The results generally reveal a lower optimal hedge ratio with area-insurance than with individual insurance and show that shallow loss revenue insurance tends to slightly increase optimal hedge ratios.

The objective of the thesis is to analyze the copper futures market in London Metal Exchange (LME) and to recommend appropriate hedging strategy in copper futures market to the Erdenet Mining Corporation in Mongolia. It uses daily official settlement copper prices of LME in the spot and 3 month futures markets from 2000-2014. Initially, we use cointegration test and ECM to investigate the copper market efficiency. Then OLS, ECM, GARCH, EGARCH and ECM-GARCH models are employed to compute different optimum hedge ratios. Finally, the hedge effectiveness is measured based on minimization of the value of AIC and SBIC. Our result indicate that copper futures market is inefficient. Hedge effectiveness comparison concludes that ECM model gives the best hedging performance. However, ECM-GARCH is accounted to be the best model for hedging strategy since it captures the time-varying conditional heteroscedasticity to ECM model. Powered by TCPDF (www.tcpdf.org)

Companies dependent on commodities for their production have to deal with volatile commodity prices and should employ measures for risk reduction as unfavourable spot price development may cause significant losses. A useful tool for diminishing the risk is hedging on futures market; however, this approach faces a crucial question of optimal hedge ratio determination (ratio between spot and futures units). Our thesis examines nine different ways of optimal hedge ratio estimation (naive, Sharpe, mean extended Gini coefficient, generalized semivariance, value at risk, and minimum variance through OLS, error correction, GARCH, and bivariate GARCH models) and evaluates their efficiency using the data on eight different commodities. The results differ across the respective commodities and cannot be generalized. Two conclusions resulting from the analysis refer to performance of naive and OLS hedge ratios and constant vs time varying hedge ratios. We find that complex hedge ratios, such as bivariate GARCH or VaR hedge ratios, do not outperform naive and OLS hedge ratios and that the results of constant hedge ratios are mostly as good as results of time-varying hedge ratios.

Crop revenue insurance is an exception in the insurance industry offering a guarantee subsuming a highly systematic risk- price variability. This study examines whether crop insurance companies could use put and call options to hedge the price risk present in corn revenue insurance. The behavioral model used to examine hedging optimization behavior of a crop producer with crop insurance by Coble, Heifner, and Zuniga (2002) is modified to examine optimal hedge ratio of a company selling revenue insurance. The crop insurance summary of business from 1985-2015 for corn revenue policies was simulated. Corn futures prices were collected from the Commodity Research Bureau databases. Results show that net return from call and put options can hedge indemnities paid by corn RP and RP-HPE resulting from the price variability in some scenario. This suggests hedging the price risk of corn revenue insurance through options could be a viable practice for crop insurers.

The main objective of this study is to analyze the effects cotton quality has on hedging and basis movements within the cotton market to help market participants minimize price risk. The effectiveness of using cotton futures in hedging price risk will be determined by calculating optimal hedge ratios by tenderable quality. Hedge ratios will be calculated using simple differences and error correction models (ECM) on overlapping price data, estimated under both generalized least squares (GLS) and maximum likelihood estimation (MLE). An empirical analysis shows that as cotton quality improves, the optimal hedge ratio decreases. ECMs estimated under GLS are found to be most efficient. It is also found that cotton classing data by quality has no significant effect on cotton basis. Farmers and merchandisers can take these results as a framework to better manage price and basis risk in the hedge and speculative scenarios.

<p>Introduction: Companies that are dependent on different commodities as input or output are exposed to price risk in these commodities. The price changes can be expressed as volatility and higher volatility results in higher risk. Hedging the commodity contracts with futures can offset this risk. One of the most important questions in this field is to what extent the risk exposure should be hedged with futures contract, i.e. the optimal hedge ratio.</p><p>Purpose: The study aims to conduct an analysis of the variance in different commodities contracts and provide evidence of the optimal hedge ratio in the respective commodities.</p><p>Method: We used a quantitative study with daily spot and futures price changes of Copper, Gold, Cotton and Oil. We investigated the 6-month hedging behaviour where timeseries were created for the period January-June each year during 2001-2004. We used a simple linear regression of the futures and spot price changes and a minimum variance model in order to calculate the optimal hedge ratio.</p><p>Conclusion: Companies that are dependent on Copper, Gold, Cotton and Oil can significantly reduce the risk by engaging in futures contracts. The optimal hedge ratio for Copper is (96%), Gold (52%), Cotton (96%) and Oil (88%). By applying the optimal hedge ratio, a company may reduce their risk exposure up to 90% compared to an unhedged position.</p>

O objetivo geral deste trabalho foi avaliar a eficiência do mercado futuro como forma de mitigação do risco associado aos preços do boi gordo para as praças de Araçatuba (SP) e Goiânia (GO). Calculou-se a efetividade do hedge por meio da razão ótima de hedge para as praças estudadas no período de 2002 a 2013, utilizando três tipos de modelos econométricos. No primeiro modelo, as variâncias e covariâncias condicionais foram tratadas como constantes e os preços spot e futuro não foram considerados correlacionados no tempo; no segundo modelo, relaxou-se a hipótese de que os preços spot e futuro não são correlacionados no tempo, portanto, adicionou-se um vetor de correção de erros ao modelo; e, no terceiro modelo, assumiu-se que as variâncias e covariâncias condicionais não são constantes. Os resultados obtidos por esses métodos indicaram que o uso do contrato futuro de boi gordo diminuiu a variância dos retornos no período estudado, de modo que as estimativas dinâmicas foram inferiores na efetividade em diminuir o risco de preço diante das estimativas obtidas por modelos estáticos. Ainda com o intuito de avaliar a eficiência do mercado futuro de boi gordo, foram quantificados a variância e os retornos do confinador nas praças estudadas através de simulações de compra de boi magro e posterior venda de boi gordo, realizando, simultaneamente, o hedge no mercado futuro. Observou-se que a utilização do contrato futuro diminuiu o coeficiente de variação para os períodos analisados em comparação às estratégias que não realizaram a utilização do hedge. / The general objective of this research was to evaluate the efficiency of futures market in order to mitigate the risk of price of live cattle to the producers of Araçatuba (SP) and Goiânia (GO). To measure this effectiveness, we estimated the optimal hedge ratio from the period of 2002 to 2013, using three types of econometric models. In the first model, conditional variances and covariances were treated as constant and the spot and future prices were not considered correlated in time; in the second model, we relaxed the hypothesis that spot and future prices were not correlated in time, so, we added an error correction vector to the model; and, in the third model, we assumed that the conditional variances and covariances are not constant. The results obtained by these methods indicated that the use of live cattle contract was able to reduce the risk and also that the dynamic estimates do not overcome the static estimates. We also calculated the variance of returns for the producers of Araçatuba e Goiânia by purchasing simulations of steers and subsequent sale of live cattle, performing simultaneously the hedge on the market future. It was observed that the use of the futures contract decreased the coefficient of variation for the periods analyzed compared to the strategies that did not undergo the use of hedging.

Introduction: Companies that are dependent on different commodities as input or output are exposed to price risk in these commodities. The price changes can be expressed as volatility and higher volatility results in higher risk. Hedging the commodity contracts with futures can offset this risk. One of the most important questions in this field is to what extent the risk exposure should be hedged with futures contract, i.e. the optimal hedge ratio. Purpose: The study aims to conduct an analysis of the variance in different commodities contracts and provide evidence of the optimal hedge ratio in the respective commodities. Method: We used a quantitative study with daily spot and futures price changes of Copper, Gold, Cotton and Oil. We investigated the 6-month hedging behaviour where timeseries were created for the period January-June each year during 2001-2004. We used a simple linear regression of the futures and spot price changes and a minimum variance model in order to calculate the optimal hedge ratio. Conclusion: Companies that are dependent on Copper, Gold, Cotton and Oil can significantly reduce the risk by engaging in futures contracts. The optimal hedge ratio for Copper is (96%), Gold (52%), Cotton (96%) and Oil (88%). By applying the optimal hedge ratio, a company may reduce their risk exposure up to 90% compared to an unhedged position.

In this thesis we search for optimal hedging strategy in stock index futures markets by providing a comprehensive comparison of variety types of models in the related literature. We concentrate on the strategy that minimizes portfolio risk, i.e., minimum variance hedge ratio (MVHR) estimated from a range of time series models with different assumptions of market volatility. There are linear regression models assuming time-invariant volatility; GARCH-type models capturing time-varying volatility, Markov regime switching (MRS) regression models assuming state-varying volatility, and MRS-GARCH models capturing both time-varying and state-varying volatility. We use both Maximum Likelihood Estimation (MLE) and Bayesian Gibbs-Sampling approach to estimate the models with four commonly used index futures contracts: S&P 500, FTSE 100, Nikkei 225 and Hang Seng index futures. We apply risk reduction and utility maximization criterions to evaluate hedging performance of MVHRs estimated from these models. The in-sample results show that the optimal hedging strategy for the S&P 500 and the Hang Seng index futures contracts is the MVHR estimated using the MRS-OLS model, while the optimal hedging strategy for the Nikkei 225 and the FTSE 100 futures contracts is the MVHR estimated using the Asymmetric-Diagonal-BEKK-GARCH and the Asymmetric-DCC-GARCH model, respectively. As in the out-of sample investigation, the time-varying models such as the BEKK-GARCH models especially the Scalar-BEKK model outperform those state-varying MRS models in majority of futures contracts in both one-step- and multiple-step-ahead forecast cases. Overall the evidence suggests that there is no single model that can consistently produce the best strategy across different index futures contracts. Moreover, using more sophisticated models such as MRS-GARCH models provide some benefits compared with their corresponding single-state GARCH models in the in-sample case but not in the out-of-sample case. While comparing with other types of models MRS-GARCH models do not necessarily improve hedging efficiency. Furthermore, there is evidence that using Bayesian Gibbs-sampling approach to estimate the MRS models provides investors more efficient hedging strategy compared with the MLE method.

Made available in DSpace on 2014-12-17T13:53:38Z (GMT). No. of bitstreams: 1 CinthyaMSN_DISSERT.pdf: 1266685 bytes, checksum: 0ea848ecffd533fcf67194d0bd6fa71f (MD5) Previous issue date: 2013-10-08 / This research aims to investigate the Hedge Efficiency and Optimal Hedge Ratio for the future market of cattle, coffee, ethanol, corn and soybean. This paper uses the Optimal Hedge Ratio and Hedge Effectiveness through multivariate GARCH models with error correction, attempting to the possible phenomenon of Optimal Hedge Ratio differential during the crop and intercrop period. The Optimal Hedge Ratio must be bigger in the intercrop period due to the uncertainty related to a possible supply shock (LAZZARINI, 2010). Among the future contracts studied in this research, the coffee, ethanol and soybean contracts were not object of this phenomenon investigation, yet. Furthermore, the corn and ethanol contracts were not object of researches which deal with Dynamic Hedging Strategy. This paper distinguishes itself for including the GARCH model with error correction, which it was never considered when the possible Optimal Hedge Ratio differential during the crop and intercrop period were investigated. The commodities quotation were used as future price in the market future of BM&FBOVESPA and as spot market, the CEPEA index, in the period from May 2010 to June 2013 to cattle, coffee, ethanol and corn, and to August 2012 to soybean, with daily frequency. Similar results were achieved for all the commodities. There is a long term relationship among the spot market and future market, bicausality and the spot market and future market of cattle, coffee, ethanol and corn, and unicausality of the future price of soybean on spot price. The Optimal Hedge Ratio was estimated from three different strategies: linear regression by MQO, BEKK-GARCH diagonal model, and BEKK-GARCH diagonal with intercrop dummy. The MQO regression model, pointed out the Hedge inefficiency, taking into consideration that the Optimal Hedge presented was too low. The second model represents the strategy of dynamic hedge, which collected time variations in the Optimal Hedge. The last Hedge strategy did not detect Optimal Hedge Ratio differential between the crop and intercrop period, therefore, unlikely what they expected, the investor do not need increase his/her investment in the future market during the intercrop / Esta pesquisa objetivou investigar a efici?ncia e raz?o ?tima de hedge para os mercados futuro de boi, caf?, etanol, milho e soja. Este trabalho tratou a raz?o ?tima e efetividade de hedge atrav?s de modelos GARCH multivariados com termo de corre??o de erro, atentando para o poss?vel fen?meno de diferenciais de raz?o ?tima de hedge nos per?odos de safra e entressafra. A raz?o ?tima de hedge deve ser maior na entressafra devido ? maior incerteza com rela??o a um poss?vel choque de oferta (LAZZARINI, 2010). Dentre os contratos futuros tratados nesta pesquisa, os contratos de caf?, etanol e soja ainda n?o foram objeto de investiga??o desse fen?meno. Al?m disso, os contratos futuros de milho e etanol ainda n?o foram objeto de pesquisas que tratam de estrat?gias de hedge din?mico. Este trabalho se diferencia ainda por incluir o mecanismo de corre??o de erro na modelagem GARCH, o que nunca foi considerado ao se investigar poss?veis diferenciais de raz?o ?tima de hedge nos per?odos de safra e entressafra. Foram utilizadas como pre?o futuro das commodities as cota??es das mesmas no mercado futuro da BM&FBOVESPA e como pre?o ? vista o ?ndice CEPEA, no per?odo de maio de 2010 a junho de 2013 para boi, caf?, etanol e milho e at? agosto de 2012 para a soja, com frequ?ncia di?ria. Foram obtidos resultados semelhantes para todas as commodities. H? rela??o de longo prazo entre os mercados ? vista e futuro, bicausalidade entre os pre?os ? vista e futuro do boi, caf?, etanol e milho, e unicausalidade do pre?o futuro da soja sobre o pre?o ? vista. A raz?o ?tima de hedge foi estimada a partir de tr?s diferentes estrat?gias: regress?o linear por MQO, modelo BEKK-GARCH diagonal e modelo BEKK-GARCH diagonal com dummy de entresssafra. O modelo de regress?o por MQO apontou para a inefici?ncia de hedge, tendo em vista que as raz?es ?timas apresentadas foram muito baixas. O segundo modelo, que representa a estrat?gia de hedge din?mico, captou varia??es temporais na raz?o ?tima. A ?ltima estrat?gia de hedge n?o detectou diferencial de raz?es ?timas de hedge entre os per?odos de safra e entressafra, logo, ao contr?rio do que se esperava, o investidor n?o precisa aumentar seu investimento no mercado futuro durante a entressafra